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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FILM-public | FILM-public/envs/utils/depth_utils.py | # Copyright 2016 The TensorFlow Authors All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicab... | 9,589 | 33.37276 | 97 | py |
FILM-public | FILM-public/alfred_utils/models/nn/resnet.py | import torch
import torch.nn as nn
from torchvision import models, transforms
class Resnet18(object):
'''
pretrained Resnet18 from torchvision
'''
def __init__(self, args, eval=True, share_memory=False, use_conv_feat=True):
self.model = models.resnet18(pretrained=True)
if args.gpu:
... | 2,622 | 27.51087 | 95 | py |
FILM-public | FILM-public/alfred_utils/models/nn/vnn.py | import torch
from torch import nn
from torch.nn import functional as F
class SelfAttn(nn.Module):
'''
self-attention with learnable parameters
'''
def __init__(self, dhid):
super().__init__()
self.scorer = nn.Linear(dhid, 1)
def forward(self, inp):
scores = F.softmax(self... | 9,623 | 32.533101 | 119 | py |
FILM-public | FILM-public/alfred_utils/models/eval/leaderboard.py | import os
import sys
sys.path.append(os.path.join(os.environ['ALFRED_ROOT']))
sys.path.append(os.path.join(os.environ['ALFRED_ROOT'], 'gen'))
sys.path.append(os.path.join(os.environ['ALFRED_ROOT'], 'models'))
import json
import argparse
import numpy as np
from PIL import Image
from datetime import datetime
from eval_t... | 7,322 | 31.118421 | 122 | py |
FILM-public | FILM-public/alfred_utils/models/eval/eval_seq2seq.py | import os
import sys
sys.path.append(os.path.join(os.environ['ALFRED_ROOT']))
sys.path.append(os.path.join(os.environ['ALFRED_ROOT'], 'gen'))
sys.path.append(os.path.join(os.environ['ALFRED_ROOT'], 'models'))
import argparse
import torch.multiprocessing as mp
from eval_task import EvalTask
from eval_subgoals import Ev... | 2,515 | 43.140351 | 149 | py |
FILM-public | FILM-public/alfred_utils/models/eval/eval.py | import json
import pprint
import random
import time
import torch
import torch.multiprocessing as mp
from models.nn.resnet import Resnet
from data.preprocess import Dataset
from importlib import import_module
class Eval(object):
# tokens
STOP_TOKEN = "<<stop>>"
SEQ_TOKEN = "<<seg>>"
TERMINAL_TOKENS = [... | 4,308 | 30.918519 | 154 | py |
FILM-public | FILM-public/alfred_utils/models/train/train_seq2seq.py | import os
import sys
sys.path.append(os.path.join(os.environ['ALFRED_ROOT']))
sys.path.append(os.path.join(os.environ['ALFRED_ROOT'], 'models'))
import os
import torch
import pprint
import json
from data.preprocess import Dataset
from importlib import import_module
from argparse import ArgumentDefaultsHelpFormatter, A... | 5,659 | 50.926606 | 142 | py |
FILM-public | FILM-public/alfred_utils/models/utils/extract_resnet.py | import os
import sys
sys.path.append(os.path.join(os.environ['ALFRED_ROOT']))
sys.path.append(os.path.join(os.environ['ALFRED_ROOT'], 'models'))
import torch
import os
from PIL import Image
from nn.resnet import Resnet
from argparse import ArgumentDefaultsHelpFormatter, ArgumentParser
if __name__ == '__main__':
... | 2,273 | 41.111111 | 136 | py |
FILM-public | FILM-public/alfred_utils/models/utils/helper_utils.py | import torch
def delete_keys_from_dict(dict_del, lst_keys):
"""
Delete the keys present in lst_keys from the dictionary.
Loops recursively over nested dictionaries.
"""
dict_foo = dict_del.copy() #Used as iterator to avoid the 'DictionaryHasChanged' error
for field in dict_foo.keys():
... | 1,403 | 38 | 91 | py |
FILM-public | FILM-public/alfred_utils/models/model/seq2seq_im_mask.py | import os
import torch
import numpy as np
from alfred_utils.models.nn import vnn as vnn
import collections
from torch import nn
from torch.nn import functional as F
from torch.nn.utils.rnn import pad_sequence, pack_padded_sequence, pad_packed_sequence
from alfred_utils.models.model.seq2seq import Module as Base
from al... | 14,984 | 38.642857 | 164 | py |
FILM-public | FILM-public/alfred_utils/models/model/seq2seq.py | import os
import random
import json
import torch
import pprint
import collections
import numpy as np
from torch import nn
from tensorboardX import SummaryWriter
from tqdm import trange
class Module(nn.Module):
def __init__(self, args, vocab):
'''
Base Seq2Seq agent with common train and val loops
... | 13,140 | 37.994065 | 172 | py |
FILM-public | FILM-public/alfred_utils/scripts/docker_run.py | #!/usr/bin/env python
from __future__ import print_function
#########
# Credit: https://github.com/RobotLocomotion/pytorch-dense-correspondence/blob/master/docker/docker_run.py
#########
import argparse
import os
import socket
import getpass
import yaml
if __name__=="__main__":
user_name = getpass.getuser()
... | 3,799 | 35.893204 | 260 | py |
FILM-public | FILM-public/alfred_utils/scripts/docker_build.py | #!/usr/bin/env python
#########
# Credit: https://github.com/RobotLocomotion/pytorch-dense-correspondence/blob/master/docker/docker_build.py
#########
from __future__ import print_function
import argparse
import os
import getpass
if __name__=="__main__":
print("building docker container . . . ")
user_name ... | 1,986 | 35.796296 | 172 | py |
FILM-public | FILM-public/alfred_utils/env/thor_env_code.py | import os
import sys
import cv2
import copy
import alfred_utils.gen.constants as constants
import numpy as np
from collections import Counter, OrderedDict
from alfred_utils.env.tasks import get_task
from ai2thor.controller import Controller
import alfred_utils.gen.utils.image_util as image_util
from alfred_utils.gen.u... | 48,982 | 39.348435 | 160 | py |
FILM-public | FILM-public/models/sem_mapping.py | import torch
import torch.nn as nn
from torch.nn import functional as F
import torchvision.models as models
import numpy as np
from utils.distributions import Categorical, DiagGaussian
from utils.model import get_grid, ChannelPool, Flatten, NNBase
import envs.utils.depth_utils as du
import cv2
import time
class Se... | 7,245 | 39.255556 | 123 | py |
FILM-public | FILM-public/models/depth/segmentation_definitions.py | from typing import Tuple
import sys
import torch
import random
import hashlib
_INTERACTIVE_OBJECTS = [
'AlarmClock',
'Apple',
'ArmChair',
'BaseballBat',
'BasketBall',
'Bathtub',
'BathtubBasin',
'Bed',
'Blinds',
'Book',
'Boots',
'Bowl',
'Box',
'Bread',
'Butte... | 11,897 | 22.283757 | 104 | py |
FILM-public | FILM-public/models/depth/alfred_perception_models.py | from typing import Dict, Tuple
import math
import torch
import torch.nn as nn
import torch.nn.functional as F
from torchvision import models
from . import segmentation_definitions as segdef
DEPTH_BINS = 50
DEPTH_MAX = 5.0
DISTR_DEPTH = True
VEC_HEAD = True
TRAIN_DEPTH_ONLY = False
TRAIN_SEG_ONLY = True
TRAIN_INV_... | 15,327 | 36.385366 | 169 | py |
FILM-public | FILM-public/models/segmentation/alfworld_mrcnn.py | import torch
import torchvision
from torchvision.models.detection.rpn import AnchorGenerator, RPNHead
from torchvision.models.detection.faster_rcnn import FastRCNNPredictor
from torchvision.models.detection.mask_rcnn import MaskRCNNPredictor
import pickle
import os
import sys
#import alfworld.gen.constants as constant... | 3,713 | 39.813187 | 898 | py |
FILM-public | FILM-public/models/segmentation/segmentation_helper.py | #Class for segmentation support
import cv2
import torch
import torchvision.transforms as T
import copy
import numpy as np
import skimage.morphology
from .alfworld_mrcnn import load_pretrained_model
from . import alfworld_constants
class SemgnetationHelper:
def __init__(self, agent):
self.agent = agent
arg... | 19,336 | 36.186538 | 216 | py |
FILM-public | FILM-public/models/instructions_processed_LP/BERT/end_to_end_outputs.py | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Wed Feb 3 23:52:39 2021
@author: soyeonmin
"""
#Run base model (into templates) and then extract arguments
import random
import time
import torch
from torch import nn
import pickle
import glob
import argparse
import os
parser = argparse.ArgumentParser()
... | 7,748 | 38.535714 | 136 | py |
FILM-public | FILM-public/models/instructions_processed_LP/BERT/train_bert_base.py | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Wed Jan 20 11:41:44 2021
@author: soyeonmin
"""
#Train BERT on ALFRED data
import random
import time
import torch
from torch import nn
import os
import argparse
parser = argparse.ArgumentParser()
parser.add_argument('-lr','--learning_rate', type=float, h... | 6,245 | 37.319018 | 146 | py |
FILM-public | FILM-public/models/instructions_processed_LP/BERT/train_bert_args.py | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Wed Feb 3 21:49:31 2021
@author: soyeonmin
"""
import random
import time
import torch
from torch import nn
import os
import glob
from collections import OrderedDict
import argparse
parser = argparse.ArgumentParser()
parser.add_argument('-lr','--learning_r... | 10,391 | 38.363636 | 184 | py |
FILM-public | FILM-public/models/semantic_policy/sem_map_model.py | import torch
import torch.nn as nn
from torch.nn import functional as F
import torchvision.models as models
import numpy as np
from utils.model import get_grid, ChannelPool, Flatten, NNBase
import cv2
import time
class UNet(nn.Module):
def __init__(self, input_shape, recurrent=False, hidden_size=512,
... | 6,632 | 31.199029 | 96 | py |
FILM-public | FILM-public/models/semantic_policy/train_map_multi.py | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Mon Aug 30 16:35:46 2021
@author: soyeonmin
"""
import skimage.morphology
import pickle
import numpy as np
from torch.utils.data import Dataset, DataLoader
import torch
from sem_map_model import UNetMulti
from utils.model import Flatten
import math
import a... | 15,273 | 50.255034 | 897 | py |
FILM-public | FILM-public/agents/sem_exp_thor.py | import math
import os, sys
import matplotlib
if sys.platform == 'darwin':
matplotlib.use("tkagg")
else:
matplotlib.use('Agg')
import pickle, json
import copy
import string
import torch
from torch.nn import functional as F
from torchvision import transforms
import numpy as np
import skimage.morphology
import cv2
fr... | 54,293 | 34.119017 | 261 | py |
FILM-public | FILM-public/utils/optimization.py | import inspect
import re
from torch import optim
def get_optimizer(parameters, s):
"""
Parse optimizer parameters.
Input should be of the form:
- "sgd,lr=0.01"
- "adagrad,lr=0.1,lr_decay=0.05"
"""
if "," in s:
method = s[:s.find(',')]
optim_params = {}
for ... | 1,816 | 30.877193 | 98 | py |
FILM-public | FILM-public/utils/distributions.py | # The following code is largely borrowed from:
# https://github.com/ikostrikov/pytorch-a2c-ppo-acktr-gail/blob/master/a2c_ppo_acktr/distributions.py
import torch
import torch.nn as nn
from utils.model import AddBias
from arguments import get_args
"""
Modify standard PyTorch distributions so they are compatible with ... | 1,903 | 28.75 | 101 | py |
FILM-public | FILM-public/utils/storage.py | # The following code is largely borrowed from:
# https://github.com/ikostrikov/pytorch-a2c-ppo-acktr-gail/blob/master/a2c_ppo_acktr/storage.py
from collections import namedtuple
import numpy as np
import torch
from torch.utils.data.sampler import BatchSampler, SubsetRandomSampler
def _flatten_helper(T, N, _tensor):... | 8,686 | 42.435 | 95 | py |
FILM-public | FILM-public/utils/model.py | import numpy as np
import torch
from torch import nn
from torch.nn import functional as F
def get_grid(pose, grid_size, device):
"""
Input:
`pose` FloatTensor(bs, 3)
`grid_size` 4-tuple (bs, _, grid_h, grid_w)
`device` torch.device (cpu or gpu)
Output:
`rot_grid` FloatTenso... | 3,887 | 28.233083 | 97 | py |
DCFM | DCFM-master/test.py | from PIL import Image
from dataset import get_loader
import torch
from torchvision import transforms
from util import save_tensor_img, Logger
from tqdm import tqdm
from torch import nn
import os
from models.main import *
import argparse
import numpy as np
import cv2
from skimage import img_as_ubyte
def main(args):
... | 3,022 | 33.747126 | 132 | py |
DCFM | DCFM-master/loss.py | from torch import nn
import torch
import torch.nn.functional as F
import math
import numpy as np
from torch.autograd import Variable
class IoU_loss(torch.nn.Module):
def __init__(self):
super(IoU_loss, self).__init__()
def forward(self, pred, target):
b = pred.shape[0]
IoU = 0.0
... | 1,552 | 26.245614 | 101 | py |
DCFM | DCFM-master/utils.py | import torch
import torch.nn.functional as F
import torch.nn as nn
import utils.utils as gen_utils
import numpy as np
def adjust_rate_poly(cur_iter, max_iter, power=0.9):
return (1.0 - 1.0 * cur_iter / max_iter) ** power
def adjust_learning_rate_exp(lr, optimizer, iters, decay_rate=0.1, decay_step=25):
lr = l... | 2,758 | 31.845238 | 82 | py |
DCFM | DCFM-master/dataset.py | import os
from PIL import PILLOW_VERSION, Image, ImageOps, ImageFilter
import torch
import random
import numpy as np
from torch.utils import data
from torchvision import transforms
from torchvision.transforms import functional as F
import numbers
import random
import pandas as pd
class CoData(data.Dataset):
def _... | 8,319 | 31 | 139 | py |
DCFM | DCFM-master/util.py | import logging
import os
import torch
import shutil
from torchvision import transforms
import numpy as np
import random
import cv2
class Logger():
def __init__(self, path="log.txt"):
self.logger = logging.getLogger('DCFM')
self.file_handler = logging.FileHandler(path, "w")
self.stdout_hand... | 2,826 | 29.397849 | 100 | py |
DCFM | DCFM-master/train.py | import torch
import torch.nn as nn
import torch.optim as optim
from util import Logger, AverageMeter, save_checkpoint, save_tensor_img, set_seed
import os
import numpy as np
from matplotlib import pyplot as plt
import time
import argparse
from tqdm import tqdm
from dataset import get_loader
from loss import *
from conf... | 10,547 | 35.372414 | 124 | py |
DCFM | DCFM-master/evaluation/main.py | import os
import argparse
import numpy as np
import matplotlib.pyplot as plt
from scipy.io import loadmat
import torch
import torch.nn as nn
from evaluator import Eval_thread
from dataloader import EvalDataset
import sys
sys.path.append('..')
from config import Config
styles = ['.-r', '.--b', '.--g', '.--c', '.-m'... | 7,468 | 34.065728 | 114 | py |
DCFM | DCFM-master/evaluation/dataloader.py | from torch.utils import data
import os
from PIL import Image, ImageFile
ImageFile.LOAD_TRUNCATED_IMAGES = True
class EvalDataset(data.Dataset):
def __init__(self, pred_root, label_root, return_predpath=False, return_gtpath=False):
self.return_predpath = return_predpath
self.return_gtpath = retur... | 1,668 | 31.72549 | 90 | py |
DCFM | DCFM-master/evaluation/evaluator.py | import os
import time
import json
import numpy as np
from scipy.io import savemat
import torch
from torchvision import transforms
from PIL import ImageFile
ImageFile.LOAD_TRUNCATED_IMAGES = True
class Eval_thread():
def __init__(self, loader, method='', dataset='', output_dir='', epoch='', cuda=True):
s... | 18,449 | 36.576375 | 147 | py |
DCFM | DCFM-master/models/main.py | import torch
from torch import nn
import torch.nn.functional as F
from models.vgg import VGG_Backbone
from util import *
def weights_init(module):
if isinstance(module, nn.Conv2d):
nn.init.kaiming_normal_(module.weight, mode='fan_in', nonlinearity='relu')
if module.bias is not None:
nn... | 11,962 | 36.857595 | 114 | py |
DCFM | DCFM-master/models/vgg.py | import torch
import torch.nn as nn
import os
class VGG_Backbone(nn.Module):
# VGG16 with two branches
# pooling layer at the front of block
def __init__(self):
super(VGG_Backbone, self).__init__()
conv1 = nn.Sequential()
conv1.add_module('conv1_1', nn.Conv2d(3, 64, 3, 1, 1))
... | 5,233 | 42.616667 | 81 | py |
Visual-InCompatibility-Transformer | Visual-InCompatibility-Transformer-main/data_loaders.py | import clip
import torch
import numpy as np
from PIL import Image
class DatasetIterator_VICTOR(torch.utils.data.Dataset):
def __init__(
self,
input_data,
data_path,
vf_df,
tf_df,
vf_shape,
tf_shape,
limit_items=10,
use_misfits=False,
... | 4,118 | 29.511111 | 84 | py |
Visual-InCompatibility-Transformer | Visual-InCompatibility-Transformer-main/prepare_polyvore.py | import re
import nltk
import string
from nltk.stem.porter import PorterStemmer
import os
import json
import numpy as np
import pandas as pd
from tqdm import tqdm
import random
import copy
from torch.utils.data import DataLoader
random.seed(0)
np.random.seed(0)
def fetch_polyvore(
data_path,
use_misfits,
... | 12,890 | 30.288835 | 87 | py |
Visual-InCompatibility-Transformer | Visual-InCompatibility-Transformer-main/VICTOR.py | import timm
import time
import os
import json
import numpy as np
from sklearn import metrics
import pandas as pd
from tqdm import tqdm
import random
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torch.utils.data import DataLoader
import copy
import time
from PIL imp... | 34,596 | 38.630011 | 342 | py |
Visual-InCompatibility-Transformer | Visual-InCompatibility-Transformer-main/models.py | import timm
import torch
import torch.nn as nn
from transformers import AutoTokenizer, AutoModel
import numpy as np
import clip
class VICTOR(nn.Module):
def __init__(
self,
device,
emb_dim=64,
tf_layers=1,
tf_head=2,
tf_dim=128,
activation="relu",
dro... | 6,963 | 28.138075 | 87 | py |
Visual-InCompatibility-Transformer | Visual-InCompatibility-Transformer-main/FLIP.py | import os
import re
import copy
import time
import clip
import nltk
import string
import json
import numpy as np
import pandas as pd
from tqdm import tqdm
import random
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torch.utils.data import DataLoader
from transformer... | 18,833 | 27.026786 | 183 | py |
nasbench-1shot1 | nasbench-1shot1-master/nasbench_analysis/evaluate_one_shot_models_enas.py | import argparse
import json
import os
import pickle
import numpy as np
import torch
from nasbench import api
from nasbench_analysis.search_spaces.search_space_1 import SearchSpace1
from nasbench_analysis.search_spaces.search_space_2 import SearchSpace2
from nasbench_analysis.search_spaces.search_space_3 import Search... | 4,922 | 41.439655 | 115 | py |
nasbench-1shot1 | nasbench-1shot1-master/nasbench_analysis/eval_darts_one_shot_model_in_nasbench.py | import glob
import json
import os
import pickle
import numpy as np
import torch
import torch.nn.functional as F
from nasbench import api
from nasbench_analysis.search_spaces.search_space_1 import SearchSpace1
from nasbench_analysis.search_spaces.search_space_2 import SearchSpace2
from nasbench_analysis.search_spaces.... | 5,763 | 35.948718 | 136 | py |
nasbench-1shot1 | nasbench-1shot1-master/nasbench_analysis/architecture_inductive_bias/model_search.py | import torch.nn.functional as F
from optimizers.darts.model_search import Network, MixedOp, ChoiceBlock, Cell
from optimizers.darts.operations import *
class MixedOpIndependentTraining(MixedOp):
def __init__(self, *args, **kwargs):
super(MixedOpIndependentTraining, self).__init__(*args, **kwargs)
de... | 4,618 | 41.768519 | 121 | py |
nasbench-1shot1 | nasbench-1shot1-master/nasbench_analysis/architecture_inductive_bias/train.py | import argparse
import glob
import json
import logging
import os
import pickle
import sys
import time
import numpy as np
import torch
import torch.backends.cudnn as cudnn
import torch.nn as nn
import torch.utils
import torchvision.datasets as dset
from torch.autograd import Variable
from nasbench_analysis import eval... | 13,001 | 41.910891 | 134 | py |
nasbench-1shot1 | nasbench-1shot1-master/nasbench_analysis/single_architecture_training/train_sgdr.py | import argparse
import glob
import json
import logging
import os
import pickle
import sys
import time
import numpy as np
import torch
import torch.backends.cudnn as cudnn
import torch.nn as nn
import torch.utils
import torchvision.datasets as dset
from torch.autograd import Variable
from nasbench_analysis import eval... | 12,564 | 41.449324 | 117 | py |
nasbench-1shot1 | nasbench-1shot1-master/nasbench_analysis/single_architecture_training/train_nasbench_like.py | import argparse
import glob
import json
import logging
import os
import pickle
import sys
import time
import numpy as np
import torch
import torch.backends.cudnn as cudnn
import torch.nn as nn
import torch.utils
import torchvision.datasets as dset
from torch.autograd import Variable
from nasbench_analysis import eval... | 12,207 | 40.104377 | 117 | py |
nasbench-1shot1 | nasbench-1shot1-master/nasbench_analysis/single_architecture_training/train.py | import argparse
import glob
import json
import logging
import os
import pickle
import sys
import time
import numpy as np
import torch
import torch.backends.cudnn as cudnn
import torch.nn as nn
import torch.utils
import torchvision.datasets as dset
from torch.autograd import Variable
from nasbench_analysis import eval... | 12,588 | 41.530405 | 117 | py |
nasbench-1shot1 | nasbench-1shot1-master/optimizers/gdas/train_search_bohb.py | import argparse
import glob
import json
import logging
import os
import pickle
import sys
import time
import numpy as np
import torch
import torch.backends.cudnn as cudnn
import torch.nn as nn
import torch.utils
from nasbench_analysis.search_spaces.search_space_1 import SearchSpace1
from nasbench_analysis.search_spac... | 10,580 | 38.928302 | 115 | py |
nasbench-1shot1 | nasbench-1shot1-master/optimizers/gdas/architect.py | import torch
from torch.autograd import Variable
from torch import autograd
from optimizers.darts.architect import Architect
def _concat(xs):
return torch.cat([x.view(-1) for x in xs])
class ArchitectGDAS(Architect):
def __init__(self, model, args):
self.grad_clip = args.grad_clip
super(Ar... | 2,861 | 43.71875 | 120 | py |
nasbench-1shot1 | nasbench-1shot1-master/optimizers/gdas/model_search.py | import torch.nn.functional as F
from optimizers.darts.model_search import Network, MixedOp, ChoiceBlock, Cell
from optimizers.darts.operations import *
class MixedOpGDAS(MixedOp):
"""
Adapted from GDAS:
https://github.com/D-X-Y/GDAS/blob/ea4d245a0eb1d1863418ded661c5867d4669d9bf/lib/nas/model_search_acc2.... | 5,264 | 44 | 121 | py |
nasbench-1shot1 | nasbench-1shot1-master/optimizers/gdas/train_search.py | import argparse
import glob
import json
import logging
import os
import pickle
import sys
import time
import numpy as np
import torch
import torch.backends.cudnn as cudnn
import torch.nn as nn
import torch.utils
import torchvision.datasets as dset
from nasbench_analysis.search_spaces.search_space_1 import SearchSpace... | 10,335 | 40.51004 | 118 | py |
nasbench-1shot1 | nasbench-1shot1-master/optimizers/darts/train_search_bohb.py | import argparse
import glob
import json
import logging
import os
import pickle
import sys
import time
import numpy as np
import torch
import torch.backends.cudnn as cudnn
import torch.nn as nn
import torch.utils
from nasbench_analysis.search_spaces.search_space_1 import SearchSpace1
from nasbench_analysis.search_spac... | 9,966 | 38.551587 | 115 | py |
nasbench-1shot1 | nasbench-1shot1-master/optimizers/darts/architect.py | import numpy as np
import torch
from torch.autograd import Variable
def _concat(xs):
return torch.cat([x.view(-1) for x in xs])
class Architect(object):
def __init__(self, model, args):
self.network_momentum = args.momentum
self.network_weight_decay = args.weight_decay
self.model = ... | 4,255 | 40.320388 | 118 | py |
nasbench-1shot1 | nasbench-1shot1-master/optimizers/darts/train_imagenet.py | import argparse
import glob
import logging
import sys
import time
import numpy as np
import os
import torch
import torch.backends.cudnn as cudnn
import torch.nn as nn
import torch.utils
import torchvision.datasets as dset
import torchvision.transforms as transforms
from torch.autograd import Variable
from optimizers.... | 8,579 | 36.304348 | 107 | py |
nasbench-1shot1 | nasbench-1shot1-master/optimizers/darts/utils.py | from __future__ import print_function
import numpy as np
import os
import os.path
import sys
import shutil
import torch
import torchvision.transforms as transforms
from PIL import Image
from torch.autograd import Variable
from torchvision.datasets import VisionDataset
from torchvision.datasets import utils
if sys.ve... | 8,950 | 31.314079 | 109 | py |
nasbench-1shot1 | nasbench-1shot1-master/optimizers/darts/model.py | from optimizers.darts.operations import *
from optimizers.darts.utils import drop_path
class Cell(nn.Module):
def __init__(self, genotype, C_prev_prev, C_prev, C, reduction, reduction_prev):
super(Cell, self).__init__()
print(C_prev_prev, C_prev, C)
if reduction_prev:
self.pr... | 7,490 | 34.50237 | 96 | py |
nasbench-1shot1 | nasbench-1shot1-master/optimizers/darts/model_search.py | import torch.nn.functional as F
from torch.autograd import Variable
from nasbench_analysis.search_spaces.search_space_1 import SearchSpace1
from optimizers.darts.genotypes import PRIMITIVES
from optimizers.darts.operations import *
class MixedOp(nn.Module):
def __init__(self, C, stride):
super(MixedOp, ... | 10,027 | 42.411255 | 121 | py |
nasbench-1shot1 | nasbench-1shot1-master/optimizers/darts/train_search.py | import argparse
import glob
import json
import logging
import os
import pickle
import sys
import time
import numpy as np
import torch
import torch.backends.cudnn as cudnn
import torch.nn as nn
import torch.utils
import torchvision.datasets as dset
from nasbench_analysis import eval_darts_one_shot_model_in_nasbench as... | 10,712 | 40.847656 | 116 | py |
nasbench-1shot1 | nasbench-1shot1-master/optimizers/darts/train.py | import argparse
import glob
import logging
import sys
import time
import numpy as np
import os
import torch
import torch.backends.cudnn as cudnn
import torch.nn as nn
import torch.utils
import torchvision.datasets as dset
from torch.autograd import Variable
from optimizers.darts import utils
from optimizers.darts.mod... | 6,543 | 37.721893 | 100 | py |
nasbench-1shot1 | nasbench-1shot1-master/optimizers/darts/operations.py | import torch
import torch.nn as nn
OPS = {
# For nasbench
'maxpool3x3': lambda C, stride, affine: nn.MaxPool2d(3, stride=stride, padding=1),
'conv3x3-bn-relu': lambda C, stride, affine: Conv3x3BnRelu(C, stride),
'conv1x1-bn-relu': lambda C, stride, affine: Conv1x1BnRelu(C, stride),
# Normal DARTS
... | 5,847 | 34.017964 | 117 | py |
nasbench-1shot1 | nasbench-1shot1-master/optimizers/random_search_with_weight_sharing/darts_wrapper_discrete.py | import json
import logging
import os
import random
import time
import numpy as np
import torch
import torch.backends.cudnn as cudnn
import torch.nn as nn
import torchvision.datasets as dset
from torch.autograd import Variable
from nasbench_analysis.search_spaces.search_space_1 import SearchSpace1
from optimizers.dart... | 12,342 | 36.516717 | 116 | py |
nasbench-1shot1 | nasbench-1shot1-master/optimizers/enas/enas_child.py | import logging
import torch
from torch import nn
from optimizers.darts import utils
from optimizers.enas.data import get_loaders
from optimizers.random_search_with_weight_sharing.darts_wrapper_discrete import DartsWrapper
class ENASChild(DartsWrapper):
def __init__(self, controller, *args, **kwargs):
su... | 5,980 | 34.182353 | 118 | py |
nasbench-1shot1 | nasbench-1shot1-master/optimizers/enas/utils.py | import collections
from collections import defaultdict
import numpy as np
import torch
Node = collections.namedtuple('Node', ['id', 'name'])
class keydefaultdict(defaultdict):
def __missing__(self, key):
if self.default_factory is None:
raise KeyError(key)
else:
ret = sel... | 644 | 22.888889 | 55 | py |
nasbench-1shot1 | nasbench-1shot1-master/optimizers/enas/data.py | from torch.utils.data import DataLoader, SubsetRandomSampler
from torchvision import transforms
from torchvision.datasets import CIFAR10
MEAN = [0.4914, 0.4822, 0.4465]
STD = [0.2023, 0.1994, 0.2010]
def get_loaders(args):
train_transform = transforms.Compose([
transforms.RandomCrop(32, padding=4),
... | 2,248 | 24.556818 | 66 | py |
nasbench-1shot1 | nasbench-1shot1-master/optimizers/enas/micro_controller.py | import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from optimizers.darts.genotypes import PRIMITIVES
"""
Implementation done by MengTianjian reimplementation by https://github.com/MengTianjian/enas-pytorch/blob/master/micro_controller.py
Modified by Julien Siems
"""
class Controll... | 6,410 | 39.834395 | 132 | py |
nasbench-1shot1 | nasbench-1shot1-master/optimizers/enas/enas.py | import argparse
import inspect
import json
import logging
import os
import pickle
import sys
import time
import numpy as np
import torch
from nasbench_analysis.search_spaces.search_space_1 import SearchSpace1
from nasbench_analysis.search_spaces.search_space_2 import SearchSpace2
from nasbench_analysis.search_spaces.... | 7,765 | 37.445545 | 111 | py |
nasbench-1shot1 | nasbench-1shot1-master/optimizers/pc_darts/train_search_bohb.py | import argparse
import glob
import json
import logging
import os
import pickle
import sys
import time
import numpy as np
import torch
import torch.backends.cudnn as cudnn
import torch.nn as nn
import torch.utils
from nasbench_analysis.search_spaces.search_space_1 import SearchSpace1
from nasbench_analysis.search_spac... | 9,987 | 38.634921 | 115 | py |
nasbench-1shot1 | nasbench-1shot1-master/optimizers/pc_darts/model_search.py | from optimizers.darts.genotypes import PRIMITIVES
from optimizers.darts.model_search import Network, Cell, ChoiceBlock, MixedOp
from optimizers.darts.operations import *
def channel_shuffle(x, groups):
"""
https://github.com/yuhuixu1993/PC-DARTS/blob/86446d1b6bbbd5f752cc60396be13d2d5737a081/model_search.py#L9... | 3,601 | 35.383838 | 119 | py |
nasbench-1shot1 | nasbench-1shot1-master/optimizers/pc_darts/train_search.py | import argparse
import glob
import json
import logging
import os
import pickle
import sys
import time
import numpy as np
import torch
import torch.backends.cudnn as cudnn
import torch.nn as nn
import torch.utils
import torchvision.datasets as dset
from nasbench_analysis.search_spaces.search_space_1 import SearchSpace... | 9,789 | 40.308017 | 117 | py |
OpenGlue | OpenGlue-main/pretrain_homography.py | import shutup
shutup.please()
import os
import argparse
import pathlib
from datetime import datetime
from omegaconf import OmegaConf
import pytorch_lightning as pl
from pytorch_lightning.plugins import DDPPlugin
from data.oxford_paris_datamodule import OxfordParis1MDataModule
from models.matching_module import Matchi... | 3,016 | 35.792683 | 119 | py |
OpenGlue | OpenGlue-main/inference.py | import cv2
import os
from omegaconf import OmegaConf
from typing import Dict, Optional
import argparse
import matplotlib.pyplot as plt
import kornia as K
import kornia.feature as KF
from kornia_moons.feature import *
import torch
import torch.nn as nn
from models.features import get_feature_extractor
from models.fea... | 12,288 | 44.346863 | 116 | py |
OpenGlue | OpenGlue-main/train_cached.py | import shutup
shutup.please()
import os
import argparse
from datetime import datetime
from omegaconf import OmegaConf
import pytorch_lightning as pl
from pytorch_lightning.plugins import DDPPlugin
from data.megadepth_datamodule import MegaDepthPairsDataModuleFeatures
from models.matching_module import MatchingTrainin... | 3,718 | 38.56383 | 110 | py |
OpenGlue | OpenGlue-main/extract_features.py | import argparse
import glob
import logging
import math
import pathlib
from typing import Tuple, List, Union, Optional
import cv2
import deepdish as dd
import numpy as np
import os
import torch
import yaml
from torch import multiprocessing
from models.features import get_feature_extractor
logging.basicConfig(format='... | 10,398 | 36.677536 | 115 | py |
OpenGlue | OpenGlue-main/train.py | import shutup
shutup.please()
import os
import argparse
from datetime import datetime
from omegaconf import OmegaConf
import pytorch_lightning as pl
from pytorch_lightning.plugins import DDPPlugin
from data.megadepth_datamodule import MegaDepthPairsDataModule
from models.matching_module import MatchingTrainingModule
... | 3,592 | 38.922222 | 140 | py |
OpenGlue | OpenGlue-main/models/gt_matches_generation.py | """
Module for generating ground truth matches between two images given keypoints on both images
and ground truth transformation
"""
from typing import Dict, Any, Optional, Tuple
import torch
from utils.misc import get_inverse_transformation, reproject_keypoints
# define module constants
UNMATCHED_INDEX = -1 # ind... | 4,237 | 45.065217 | 115 | py |
OpenGlue | OpenGlue-main/models/matching_module.py | import gc
import pytorch_lightning as pl
import torch
from models.features import get_feature_extractor
from models.features.utils import prepare_features_output
from models.gt_matches_generation import generate_gt_matches
from models.laf_converter import get_laf_to_sideinfo_converter
from models.superglue.superglue ... | 8,971 | 46.723404 | 119 | py |
OpenGlue | OpenGlue-main/models/utils.py | import numpy as np
import torch
import torch.nn as nn
def first_layer_sine_init(m):
with torch.no_grad():
if hasattr(m, 'weight'):
num_input = m.weight.size(-1)
# See paper sec. 3.2, final paragraph, and supplement Sec. 1.5 for discussion of factor 30
m.weight.uniform_(... | 1,884 | 30.949153 | 102 | py |
OpenGlue | OpenGlue-main/models/laf_converter.py | """Module that implements different strategies for converting Local Affine Frame (LAF)
to side information used in positional encoding by SuperGlue
"""
from typing import Iterable, Optional
import kornia.feature as KF
import torch
from abc import ABC, abstractmethod
class BaseLAFConversionFunction(ABC):
@abstrac... | 3,968 | 29.767442 | 109 | py |
OpenGlue | OpenGlue-main/models/features/base.py | from typing import Tuple
import numpy as np
import torch
import torch.nn as nn
from kornia.geometry.subpix import nms2d
class Features(nn.Module):
def __init__(self, detector, descriptor, perform_nms: bool = True, nms_diameter: int = 9, max_keypoints: int = 1024):
super(Features, self).__init__()
... | 3,240 | 38.048193 | 121 | py |
OpenGlue | OpenGlue-main/models/features/hardnet.py | from kornia.feature import LAFDescriptor
from kornia.feature.affine_shape import LAFAffNetShapeEstimator
from kornia.feature.orientation import LAFOrienter, PassLAF
from kornia.feature.responses import CornerGFTT
from kornia.feature.scale_space_detector import ScaleSpaceDetector
from kornia.geometry.subpix import ConvQ... | 1,695 | 42.487179 | 111 | py |
OpenGlue | OpenGlue-main/models/features/sift.py | from kornia.feature.scale_space_detector import ScaleSpaceDetector
from kornia.feature import LAFDescriptor
from kornia.feature.orientation import LAFOrienter, PassLAF
from kornia.geometry.subpix import ConvQuadInterp3d
from kornia.geometry.transform import ScalePyramid
from kornia.feature.responses import BlobDoG, Cor... | 2,172 | 42.46 | 126 | py |
OpenGlue | OpenGlue-main/models/features/iterative_features_extractor.py | from typing import Tuple, Optional
import torch
import torch.nn as nn
from .utils import min_stack
class IterativeLocalFeature(nn.Module):
"""Iteratively extract features for each pair in the batch.
Convenience module for local features extractors that don't support batching
Applies min-stack for batchi... | 1,334 | 38.264706 | 104 | py |
OpenGlue | OpenGlue-main/models/features/utils.py | import inspect
import kornia.feature as KF
import numpy as np
import torch
def filter_dict(dict_to_filter, thing_with_kwargs):
sig = inspect.signature(thing_with_kwargs)
filter_keys = [param.name for param in sig.parameters.values() if param.kind == param.POSITIONAL_OR_KEYWORD]
filtered_dict = {filter_ke... | 2,641 | 39.646154 | 112 | py |
OpenGlue | OpenGlue-main/models/features/opencv/torch_wrapper.py | from typing import Tuple
import kornia as K
import numpy as np
import torch
import torch.nn as nn
from .base import OpenCVFeatures
class OpenCVFeaturesTorchWrapper(nn.Module):
"""
This class wraps opencv based feature extract of type OpenCVFeatures.
Provides unified interface for all feature extractors ... | 1,790 | 34.82 | 118 | py |
OpenGlue | OpenGlue-main/models/features/opencv/_features_torch.py | """
Convenience functions for creating several types of OpenCV based descriptors wrapped with OpenCVFeaturesTorchWrapper.
"""
from ._features import sift_create
from .torch_wrapper import OpenCVFeaturesTorchWrapper
def sift_create_torch(max_keypoints: int = -1, nms_diameter: float = 9.,
rootsif... | 471 | 32.714286 | 117 | py |
OpenGlue | OpenGlue-main/models/features/opencv/__init__.py | from ._features_torch import sift_create_torch
from .dog_affnet_harnet import DoGOpenCVAffNetHardNet
methods = {
'OPENCV_SIFT': sift_create_torch,
'OPENCVDoGAffNetHardNet': DoGOpenCVAffNetHardNet
# register new methods here
}
__all__ = ['methods']
| 262 | 22.909091 | 53 | py |
OpenGlue | OpenGlue-main/models/features/opencv/dog_affnet_harnet.py | """
This module contains mixed feature extractors, where part of the pipeline is done using OpenCV methods on CPU
and other part is done using torch-based modules either on CPU or GPU.
"""
from typing import Union, Tuple
import cv2
import kornia as K
import kornia.feature as KF
import numpy as np
import torch
import ... | 3,317 | 39.463415 | 110 | py |
OpenGlue | OpenGlue-main/models/features/superpoint/utils.py | import torch
def remove_borders(keypoints, scores, border_size: int, H: int, W: int):
"""Removing keypoints near the border"""
toremoveW = torch.logical_or(keypoints[:, 1] < border_size, keypoints[:, 1] >= (W - border_size))
toremoveH = torch.logical_or(keypoints[:, 0] < border_size, keypoints[:, 0] >= (H... | 1,338 | 32.475 | 101 | py |
OpenGlue | OpenGlue-main/models/features/superpoint/model.py | import pathlib
from typing import Optional, Union
import torch
import torch.nn as nn
import torch.nn.functional as F
from kornia.geometry.subpix import nms2d
from models.features.superpoint.utils import remove_borders, sample_desc_from_points, top_k_keypoints
from models.features.utils import min_stack
conv2d = lamb... | 8,047 | 39.24 | 106 | py |
OpenGlue | OpenGlue-main/models/superglue/superglue.py | import math
import torch
import torch.nn as nn
from .attention_gnn import GraphAttentionNet
from .optimal_transport import log_otp_solver
from .positional_encoding import MLPPositionalEncoding
class SuperGlue(nn.Module):
def __init__(self, config):
super(SuperGlue, self).__init__()
self.config: ... | 4,593 | 40.017857 | 115 | py |
OpenGlue | OpenGlue-main/models/superglue/attention_gnn.py | import torch
import torch.nn as nn
from models.superglue import get_attention_mechanism
from ..utils import FeedForwardNet
class MultiheadAttention(nn.Module):
def __init__(self, embed_dim, num_heads, attention='softmax'):
super(MultiheadAttention, self).__init__()
self.embed_dim = embed_dim // n... | 3,779 | 39.212766 | 102 | py |
OpenGlue | OpenGlue-main/models/superglue/positional_encoding.py | import torch
import torch.nn as nn
from models.superglue import get_positional_encoder
class MLPPositionalEncoding(nn.Module):
def __init__(self, output_size, side_info_size=1, encoder_name='FeedForwardNet', hidden_layers_sizes=None):
super(MLPPositionalEncoding, self).__init__()
if hidden_layers... | 748 | 36.45 | 111 | py |
OpenGlue | OpenGlue-main/models/superglue/__init__.py | import torch.nn as nn
from models.utils import FeedForwardNet, FeedForwardNetSiren
from .attention import softmax_attention, linear_attention_elu, GeneralizedFavorAttention, SoftmaxFavorAttention
methods = {
"FeedForwardNet": FeedForwardNet,
"FeedForwardNetSiren": FeedForwardNetSiren
}
def get_attention_me... | 1,496 | 30.1875 | 112 | py |
OpenGlue | OpenGlue-main/models/superglue/optimal_transport.py | import torch
def log_otp_solver(log_a, log_b, M, num_iters: int = 20, reg: float = 1.0) -> torch.Tensor:
r"""Sinkhorn matrix scaling algorithm for Differentiable Optimal Transport problem.
This function solves the optimization problem and returns the OT matrix for the given parameters.
Args:
log_... | 946 | 31.655172 | 101 | py |
OpenGlue | OpenGlue-main/models/superglue/attention.py | import math
import torch
import torch.nn as nn
import torch.nn.functional as F
def softmax_attention(query, key, value):
embed_dim = query.size(2)
# get attention scores
query = query.transpose(2, 3).contiguous() # B,H,D,N -> B,H,N,D
attention = torch.matmul(query, key) * embed_dim ** -0.5 # B,H,N,... | 5,226 | 36.335714 | 116 | py |
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