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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Diagnose_VLN | Diagnose_VLN-master/r2r/model/PREVALENT_R2R/r2r_src/r2rpretrain_class.py |
from pytorch_transformers import BertPreTrainedModel,BertConfig
from pytorch_transformers.modeling_bert import BertOnlyMLMHead
from vilmodel import DicModel
import torch
import torch.nn as nn
import pdb
class HugAddActionPreTrain(BertPreTrainedModel):
#def __init__(self,vision_size, hidden_size, dec_hidden_size,d... | 22,134 | 37.495652 | 191 | py |
Diagnose_VLN | Diagnose_VLN-master/r2r/model/PREVALENT_R2R/r2r_src/utils.py | ''' Utils for io, language, connectivity graphs etc '''
import os
import sys
import re
sys.path.append('../R2R-EnvDrop/build/')
import MatterSim
import string
import json
import time
import math
from collections import Counter, defaultdict
import numpy as np
import networkx as nx
from param import args
# padding, un... | 20,315 | 33.728205 | 126 | py |
Diagnose_VLN | Diagnose_VLN-master/r2r/model/PREVALENT_R2R/r2r_src/model.py |
import torch
import torch.nn as nn
from torch.autograd import Variable
import torch.nn.functional as F
from torch.nn.utils.rnn import pack_padded_sequence, pad_packed_sequence
from param import args
import pdb
class EncoderLSTM(nn.Module):
''' Encodes navigation instructions, returning hidden state context (for
... | 22,199 | 40.651032 | 173 | py |
Diagnose_VLN | Diagnose_VLN-master/r2r/model/PREVALENT_R2R/r2r_src/speaker.py | import torch
import numpy as np
from param import args
import os
import utils
import model
import torch.nn.functional as F
class Speaker():
env_actions = {
'left': (0,-1, 0), # left
'right': (0, 1, 0), # right
'up': (0, 0, 1), # up
'down': (0, 0,-1), # down
'forward': (1, 0... | 16,945 | 42.562982 | 144 | py |
Diagnose_VLN | Diagnose_VLN-master/r2r/model/PREVALENT_R2R/r2r_src/ndhtrain.py | import argparse
import torch
import torch.nn as nn
from torch.autograd import Variable
from torch import optim
import torch.nn.functional as F
import os
import time
import numpy as np
import pandas as pd
from collections import defaultdict
from utils import read_vocab,write_vocab,build_vocab,Tokenizer,BTokenizer,pad... | 23,276 | 49.274298 | 261 | py |
Diagnose_VLN | Diagnose_VLN-master/r2r/model/PREVALENT_R2R/r2r_src/agent.py |
import json
import os
import sys
import numpy as np
import random
import math
import time
import torch
import torch.nn as nn
from torch.autograd import Variable
from torch import optim
import torch.nn.functional as F
from env import R2RBatch
from utils import padding_idx, add_idx, Tokenizer
import utils
import model... | 57,107 | 45.618776 | 333 | py |
Diagnose_VLN | Diagnose_VLN-master/r2r/model/PREVALENT_R2R/r2r_src/train.py |
import torch
import os
import time
import json
import numpy as np
from collections import defaultdict
from speaker import Speaker
from utils import read_vocab,write_vocab,build_vocab,Tokenizer,BTokenizer,padding_idx,timeSince, read_img_features
import utils
from env import R2RBatch
from agent import Seq2SeqAgent
fro... | 26,263 | 39.406154 | 169 | py |
Diagnose_VLN | Diagnose_VLN-master/r2r/model/PREVALENT_R2R/r2r_src/param.py | import argparse
import os
import torch
def create_folders(path):
""" recursively create folders """
if not os.path.isdir(path):
while True:
try:
os.makedirs(path)
except:
pass
time.sleep(1)
else:
break
... | 9,398 | 56.310976 | 146 | py |
Diagnose_VLN | Diagnose_VLN-master/r2r/model/PREVALENT_R2R/tasks/NDH/vilmodel.py |
from __future__ import absolute_import, division, print_function, unicode_literals
import json
import logging
import math
import os
import sys
from io import open
import torch
from torch import nn
from torch.nn import CrossEntropyLoss, MSELoss
from modeling_utils import (WEIGHTS_NAME, CONFIG_NAME, PretrainedConfig,... | 66,108 | 50.446693 | 187 | py |
Diagnose_VLN | Diagnose_VLN-master/r2r/model/PREVALENT_R2R/tasks/NDH/r2rmodel.py |
import torch
import torch.nn as nn
from torch.autograd import Variable
import torch.nn.functional as F
from torch.nn.utils.rnn import pack_padded_sequence, pad_packed_sequence
#from pytorch_pretrained_bert import BertModel, OpenAIGPTModel
from vilmodel import BertModel,BertImgModel,BertLayer,BertLayerNorm,BertPooler,B... | 156,744 | 44.912419 | 223 | py |
Diagnose_VLN | Diagnose_VLN-master/r2r/model/PREVALENT_R2R/tasks/NDH/modeling_utils.py | from __future__ import (absolute_import, division, print_function,
unicode_literals)
import copy
import json
import logging
import os
from io import open
import six
import torch
from torch import nn
from torch.nn import CrossEntropyLoss
from torch.nn import functional as F
#from .file_utils i... | 48,476 | 53.714447 | 480 | py |
Diagnose_VLN | Diagnose_VLN-master/r2r/model/PREVALENT_R2R/tasks/NDH/r2rpretrain_class.py |
from pytorch_transformers import BertPreTrainedModel,BertConfig
from pytorch_transformers.modeling_bert import BertOnlyMLMHead
from vilmodel import DicModel
import torch
import torch.nn as nn
import pdb
class HugAddActionPreTrain(BertPreTrainedModel):
#def __init__(self,vision_size, hidden_size, dec_hidden_size,d... | 22,134 | 37.495652 | 191 | py |
Diagnose_VLN | Diagnose_VLN-master/r2r/model/PREVALENT_R2R/tasks/NDH/utils.py | ''' Utils for io, language, connectivity graphs etc '''
import os
import sys
import re
import string
import json
import time
import math
from collections import Counter
import numpy as np
import networkx as nx
# padding, unknown word, end of sentence
base_vocab = ['<PAD>', '<UNK>', '<EOS>', '<NAV>', '<ORA>', '<TAR>'... | 11,553 | 38.433447 | 121 | py |
Diagnose_VLN | Diagnose_VLN-master/r2r/model/PREVALENT_R2R/tasks/NDH/model.py |
import torch
import torch.nn as nn
from torch.autograd import Variable
import torch.nn.functional as F
from torch.nn.utils.rnn import pack_padded_sequence, pad_packed_sequence
from r2rmodel import BertImgEncoder
class EncoderLSTM(nn.Module):
''' Encodes navigation instructions, returning hidden state context (fo... | 13,344 | 39.075075 | 130 | py |
Diagnose_VLN | Diagnose_VLN-master/r2r/model/PREVALENT_R2R/tasks/NDH/ndhtrain.py | import argparse
import torch
import torch.nn as nn
from torch.autograd import Variable
from torch import optim
import torch.nn.functional as F
import os
import time
import numpy as np
import pandas as pd
from collections import defaultdict
from utils import read_vocab,write_vocab,build_vocab,Tokenizer,BTokenizer,pad... | 29,187 | 51.972777 | 261 | py |
Diagnose_VLN | Diagnose_VLN-master/r2r/model/PREVALENT_R2R/tasks/NDH/agent.py | ''' Agents: stop/random/shortest/seq2seq '''
import json
import os
import sys
import numpy as np
import random
import time
import torch
import torch.nn as nn
import torch.distributions as D
from torch.autograd import Variable
from torch import optim
import torch.nn.functional as F
from env import R2RBatch
from util... | 35,887 | 39.735528 | 148 | py |
Diagnose_VLN | Diagnose_VLN-master/r2r/model/PREVALENT_R2R/tasks/NDH/r2ragent.py | ''' Agents: stop/random/shortest/seq2seq '''
import json
import os
import sys
import numpy as np
import random
import time
import pickle
import torch
import torch.nn as nn
import torch.distributions as D
from torch.autograd import Variable
from torch import optim
import torch.nn.functional as F
import copy
from env ... | 151,322 | 45.247861 | 225 | py |
Diagnose_VLN | Diagnose_VLN-master/r2r/model/PREVALENT_R2R/tasks/NDH/r2rutils.py | ''' Utils for io, language, connectivity graphs etc '''
import os
import sys
import re
import string
import json
import time
import math
from collections import Counter
import numpy as np
import networkx as nx
# padding, unknown word, end of sentence
base_vocab = ['<PAD>', '<UNK>', '<EOS>']
padding_idx = base_vocab.... | 15,972 | 37.489157 | 121 | py |
Diagnose_VLN | Diagnose_VLN-master/r2r/model/PREVALENT_R2R/tasks/R2R/zipdata.py | import os.path as op
from zipfile import ZipFile
import torch.utils.data as data
from PIL import Image
try:
from StringIO import StringIO
except ImportError:
from io import StringIO, BytesIO
_VALID_IMAGE_TYPES = ['.jpg', '.jpeg', '.tiff', '.bmp', '.png']
class ZipData(data.Dataset):
_IGNORE_ATTRS = {'_z... | 3,297 | 35.644444 | 110 | py |
Diagnose_VLN | Diagnose_VLN-master/r2r/model/PREVALENT_R2R/tasks/R2R/pretrain.py |
import os, argparse, json
import time, copy, random, pickle
import numpy as np
import pandas as pd
from collections import defaultdict
import torch
import torch.nn as nn
import torch.utils.data as data
from torch.autograd import Variable
from torch import optim
import torch.nn.functional as F
from utils import read_... | 27,320 | 50.744318 | 262 | py |
Diagnose_VLN | Diagnose_VLN-master/r2r/model/PREVALENT_R2R/tasks/R2R/vilmodel.py |
from __future__ import absolute_import, division, print_function, unicode_literals
import json
import logging
import math
import os
import sys
from io import open
import torch
from torch import nn
from torch.nn import CrossEntropyLoss, MSELoss
from modeling_utils import (WEIGHTS_NAME, CONFIG_NAME, PretrainedConfig,... | 73,492 | 50.755634 | 187 | py |
Diagnose_VLN | Diagnose_VLN-master/r2r/model/PREVALENT_R2R/tasks/R2R/test.py |
from vilmodel import BertImgModel,BertLayerNorm
from pytorch_transformers import BertConfig
import inspect
import pdb
"""
b = BertConfig.from_pretrained('bert-base-uncased')
b.img_feature_dim = 2176
b.img_feature_type = ""
#model1 = BertModel(b)
model = BertImgModel(b)
pdb.set_trace()
"""
print("you got it")
| 316 | 14.85 | 51 | py |
Diagnose_VLN | Diagnose_VLN-master/r2r/model/PREVALENT_R2R/tasks/R2R/agent_utils.py | import torch
import numpy as np
from utils import padding_idx
from collections import namedtuple
def basic_actions():
# For now, the agent can't pick which forward move to make - just the one in the middle
model_actions = ['left', 'right', 'up', 'down', 'forward', '<end>', '<start>', '<ignore>']
env_actio... | 6,350 | 37.259036 | 186 | py |
Diagnose_VLN | Diagnose_VLN-master/r2r/model/PREVALENT_R2R/tasks/R2R/modeling_utils.py | from __future__ import (absolute_import, division, print_function,
unicode_literals)
import copy
import json
import logging
import os
from io import open
import six
import torch
from torch import nn
from torch.nn import CrossEntropyLoss
from torch.nn import functional as F
#from .file_utils i... | 48,476 | 53.714447 | 480 | py |
Diagnose_VLN | Diagnose_VLN-master/r2r/model/PREVALENT_R2R/tasks/R2R/nav_dic_pretrain.py | from __future__ import absolute_import, division, print_function
import argparse
import glob
import logging
import os
import pickle
import random
import pdb
import numpy as np
import torch
import torch.utils.data as data
from torch.utils.data import DataLoader, Dataset, SequentialSampler, RandomSampler
from ... | 27,117 | 28.637158 | 165 | py |
Diagnose_VLN | Diagnose_VLN-master/r2r/model/PREVALENT_R2R/tasks/R2R/utils.py | ''' Utils for io, language, connectivity graphs etc '''
import os
import sys
import re
import string
import json
import time
import math
from collections import Counter
import numpy as np
import networkx as nx
# padding, unknown word, end of sentence
base_vocab = ['<PAD>', '<UNK>', '<EOS>']
padding_idx = base_vocab.... | 16,262 | 37.906699 | 121 | py |
Diagnose_VLN | Diagnose_VLN-master/r2r/model/PREVALENT_R2R/tasks/R2R/batch_loader.py | import glob
import os, argparse, json
import time, copy, random, pickle
import numpy as np
import pandas as pd
from collections import defaultdict
import torch
import torch.nn as nn
import torch.utils.data as data
from torch.autograd import Variable
from torch import optim
import torch.nn.functional as F
from utils i... | 26,312 | 37.30131 | 171 | py |
Diagnose_VLN | Diagnose_VLN-master/r2r/model/PREVALENT_R2R/tasks/R2R/model.py |
import torch
import torch.nn as nn
from torch.autograd import Variable
import torch.nn.functional as F
from torch.nn.utils.rnn import pack_padded_sequence, pad_packed_sequence
from pytorch_pretrained_bert import BertModel, OpenAIGPTModel
from vilmodel import BertImgModel,BertLayer,BertLayerNorm,BertPooler,BertAddModel... | 171,546 | 44.868182 | 223 | py |
Diagnose_VLN | Diagnose_VLN-master/r2r/model/PREVALENT_R2R/tasks/R2R/finetune.py |
import os, argparse, json
import time, copy, random, pickle
import numpy as np
import pandas as pd
from collections import defaultdict
import torch
import torch.nn as nn
from torch.autograd import Variable
from torch import optim
import torch.nn.functional as F
from utils import read_vocab, write_vocab, build_vocab,... | 64,248 | 53.68 | 286 | py |
Diagnose_VLN | Diagnose_VLN-master/r2r/model/PREVALENT_R2R/tasks/R2R/thmodel.py | # coding=utf-8
# Copyright 2019 project LXRT.
# Copyright 2018 The Google AI Language Team Authors and The HuggingFace Inc. team.
# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the ... | 46,324 | 42.253968 | 136 | py |
Diagnose_VLN | Diagnose_VLN-master/r2r/model/PREVALENT_R2R/tasks/R2R/pretrain_class.py |
from pytorch_transformers import BertPreTrainedModel,BertConfig
from pytorch_transformers.modeling_bert import BertOnlyMLMHead
from model import BertAddEncoder, BertImgEncoder,BertLangEncoder,BertAddSepEncoder
from vilmodel import BertAddModel,VicModel,DicModel
import torch
import torch.nn as nn
import pdb
class Hug... | 19,628 | 35.896617 | 190 | py |
Diagnose_VLN | Diagnose_VLN-master/r2r/model/PREVALENT_R2R/tasks/R2R/agent.py | ''' Agents: stop/random/shortest/seq2seq '''
import json
import os
import sys
import numpy as np
import random
import time
import pickle
import torch
import torch.nn as nn
import torch.distributions as D
from torch.autograd import Variable
from torch import optim
import torch.nn.functional as F
import copy
from env ... | 152,177 | 45.311016 | 251 | py |
Diagnose_VLN | Diagnose_VLN-master/r2r/model/PREVALENT_R2R/tasks/R2R/nav_hugging_pretrain.py | from __future__ import absolute_import, division, print_function
import argparse
import glob
import logging
import os
import pickle
import random
import pdb
import numpy as np
import torch
import time
import torch.utils.data as data
from torch.utils.data import DataLoader, Dataset, SequentialSampler, RandomSa... | 25,840 | 27.872626 | 165 | py |
Diagnose_VLN | Diagnose_VLN-master/r2r/model/PREVALENT_R2R/tasks/R2R/train.py |
import os, argparse, json
import time, copy, random, pickle
import numpy as np
import pandas as pd
from collections import defaultdict
import torch
import torch.nn as nn
from torch.autograd import Variable
from torch import optim
import torch.nn.functional as F
from utils import read_vocab, write_vocab, build_vocab,... | 69,720 | 55.362975 | 286 | py |
Diagnose_VLN | Diagnose_VLN-master/r2r/model/PREVALENT_R2R/tasks/R2R/testpretrain.py |
import os, argparse, json
import time, copy, random, pickle
import numpy as np
import pandas as pd
from collections import defaultdict
import torch
import torch.nn as nn
import torch.utils.data as data
from torch.autograd import Variable
from torch import optim
import torch.nn.functional as F
from utils import read_... | 28,188 | 51.10536 | 262 | py |
Diagnose_VLN | Diagnose_VLN-master/r2r/model/PREVALENT_R2R/tasks/R2R/parallel.py | ##+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
## Created by: Hang Zhang, Rutgers University, Email: zhang.hang@rutgers.edu
## Modified by Thomas Wolf, HuggingFace Inc., Email: thomas@huggingface.co
## Copyright (c) 2017-2018
##
## This source code is licensed under the MIT-style license fo... | 11,394 | 41.360595 | 95 | py |
Diagnose_VLN | Diagnose_VLN-master/r2r/model/PREVALENT_R2R/tasks/R2R/speaker/train_speaker.py | import torch
from torch import optim
import os
import os.path
import time
import numpy as np
import pandas as pd
from collections import defaultdict
import argparse
from . import utils
from .utils import read_vocab, Tokenizer, timeSince, try_cuda
from .env import R2RBatch, ImageFeatures
from .model import SpeakerEnco... | 9,841 | 35.451852 | 129 | py |
Diagnose_VLN | Diagnose_VLN-master/r2r/model/PREVALENT_R2R/tasks/R2R/speaker/follower.py | ''' Agents: stop/random/shortest/seq2seq '''
import json
import sys
import numpy as np
import random
from collections import namedtuple
import torch
import torch.nn as nn
from torch.autograd import Variable
import torch.nn.functional as F
import torch.distributions as D
from .utils import vocab_pad_idx, vocab_eos_i... | 47,882 | 45.219112 | 234 | py |
Diagnose_VLN | Diagnose_VLN-master/r2r/model/PREVALENT_R2R/tasks/R2R/speaker/utils.py | ''' Utils for io, language, connectivity graphs etc '''
import os
import sys
import re
import string
import json
import time
import math
from collections import Counter
import numpy as np
import networkx as nx
import subprocess
import itertools
import base64
# padding, unknown word, end of sentence
base_vocab = ['<P... | 8,685 | 31.654135 | 126 | py |
Diagnose_VLN | Diagnose_VLN-master/r2r/model/PREVALENT_R2R/tasks/R2R/speaker/model.py | import torch
import torch.nn as nn
from torch.autograd import Variable
import torch.nn.functional as F
from torch.nn.utils.rnn import pack_padded_sequence, pad_packed_sequence
from .utils import try_cuda
from .env import ConvolutionalImageFeatures, BottomUpImageFeatures
def make_image_attention_layers(args, image_f... | 22,228 | 41.748077 | 82 | py |
Diagnose_VLN | Diagnose_VLN-master/r2r/model/PREVALENT_R2R/tasks/R2R/speaker/speaker.py | import json
import sys
import numpy as np
import random
from collections import namedtuple
import torch
import torch.nn as nn
from torch.autograd import Variable
import torch.nn.functional as F
import torch.distributions as D
from .utils import vocab_pad_idx, vocab_bos_idx, vocab_eos_idx, flatten, try_cuda
from .foll... | 18,160 | 43.187348 | 180 | py |
Diagnose_VLN | Diagnose_VLN-master/r2r/model/PREVALENT_R2R/tasks/R2R/speaker/env.py | ''' Batched Room-to-Room navigation environment '''
import sys
sys.path.append('build')
import MatterSim
import csv
import numpy as np
import math
import json
import random
import networkx as nx
import functools
import os.path
import time
import pickle
import os
import os.path
import sys
import itertools
from . impor... | 37,302 | 42.62924 | 231 | py |
Diagnose_VLN | Diagnose_VLN-master/r2r/model/R2R-EnvDrop/r2r_src/utils.py | ''' Utils for io, language, connectivity graphs etc '''
import os
import sys
import re
sys.path.append('build')
import MatterSim
import string
import json
import time
import math
from collections import Counter, defaultdict
import numpy as np
import networkx as nx
from param import args
# padding, unknown word, end ... | 18,192 | 32.628466 | 126 | py |
Diagnose_VLN | Diagnose_VLN-master/r2r/model/R2R-EnvDrop/r2r_src/model.py |
import torch
import torch.nn as nn
from torch.autograd import Variable
import torch.nn.functional as F
from torch.nn.utils.rnn import pack_padded_sequence, pad_packed_sequence
from param import args
class EncoderLSTM(nn.Module):
''' Encodes navigation instructions, returning hidden state context (for
att... | 12,387 | 39.75 | 173 | py |
Diagnose_VLN | Diagnose_VLN-master/r2r/model/R2R-EnvDrop/r2r_src/speaker.py | import torch
import numpy as np
from param import args
import os
import utils
import model
import torch.nn.functional as F
class Speaker():
env_actions = {
'left': (0,-1, 0), # left
'right': (0, 1, 0), # right
'up': (0, 0, 1), # up
'down': (0, 0,-1), # down
'forward': (1, 0... | 16,945 | 42.562982 | 144 | py |
Diagnose_VLN | Diagnose_VLN-master/r2r/model/R2R-EnvDrop/r2r_src/agent.py |
import json
import os
import sys
import numpy as np
import random
import math
import time
import torch
import torch.nn as nn
from torch.autograd import Variable
from torch import optim
import torch.nn.functional as F
from env import R2RBatch
from utils import padding_idx, add_idx, Tokenizer
import utils
import model... | 38,389 | 43.74359 | 145 | py |
Diagnose_VLN | Diagnose_VLN-master/r2r/model/R2R-EnvDrop/r2r_src/train.py |
import torch
import os
import time
import json
import numpy as np
from collections import defaultdict
from speaker import Speaker
from utils import read_vocab,write_vocab,build_vocab,Tokenizer,padding_idx,timeSince, read_img_features
import utils
from env import R2RBatch
from agent import Seq2SeqAgent
from eval impo... | 20,008 | 39.259557 | 169 | py |
Diagnose_VLN | Diagnose_VLN-master/r2r/model/R2R-EnvDrop/r2r_src/param.py | import argparse
import os
import torch
class Param:
def __init__(self):
self.parser = argparse.ArgumentParser(description="")
# General
self.parser.add_argument('--iters', type=int, default=100000)
self.parser.add_argument('--name', type=str, default='default')
self.parser... | 6,230 | 52.715517 | 125 | py |
Diagnose_VLN | Diagnose_VLN-master/r2r/model/Recurrent-VLN-BERT/r2r_src/utils.py | ''' Utils for io, language, connectivity graphs etc '''
import os
import sys
import re
sys.path.append('../R2R-EnvDrop/build/')
import MatterSim
import string
import json
import time
import math
from collections import Counter, defaultdict
import numpy as np
import networkx as nx
from param import args
from numpy.lina... | 23,767 | 33.396527 | 126 | py |
Diagnose_VLN | Diagnose_VLN-master/r2r/model/Recurrent-VLN-BERT/r2r_src/model_PREVALENT.py | # Recurrent VLN-BERT, 2020, by Yicong.Hong@anu.edu.au
import torch
import torch.nn as nn
from torch.autograd import Variable
import torch.nn.functional as F
from torch.nn.utils.rnn import pack_padded_sequence, pad_packed_sequence
from param import args
from vlnbert.vlnbert_init import get_vlnbert_models
class VLNBER... | 3,976 | 40 | 125 | py |
Diagnose_VLN | Diagnose_VLN-master/r2r/model/Recurrent-VLN-BERT/r2r_src/agent.py | # R2R-EnvDrop, 2019, haotan@cs.unc.edu
# Modified in Recurrent VLN-BERT, 2020, by Yicong.Hong@anu.edu.au
import json
import os
import sys
import numpy as np
import random
import math
import time
import torch
import torch.nn as nn
from torch.autograd import Variable
from torch import optim
import torch.nn.functional a... | 25,258 | 42.177778 | 126 | py |
Diagnose_VLN | Diagnose_VLN-master/r2r/model/Recurrent-VLN-BERT/r2r_src/model_OSCAR.py | # Recurrent VLN-BERT, 2020, by Yicong.Hong@anu.edu.au
import torch
import torch.nn as nn
from param import args
from vlnbert.vlnbert_init import get_vlnbert_models
class VLNBERT(nn.Module):
def __init__(self, feature_size=2048+128):
super(VLNBERT, self).__init__()
print('\nInitalizing the VLN-BE... | 3,363 | 37.227273 | 108 | py |
Diagnose_VLN | Diagnose_VLN-master/r2r/model/Recurrent-VLN-BERT/r2r_src/train.py | import torch
import os
import time
import json
import random
import numpy as np
from collections import defaultdict
from utils import read_vocab, write_vocab, build_vocab, padding_idx, timeSince, read_img_features, print_progress
import utils
from env import R2RBatch
from agent import Seq2SeqAgent
from eval import Ev... | 11,010 | 37.5 | 169 | py |
Diagnose_VLN | Diagnose_VLN-master/r2r/model/Recurrent-VLN-BERT/r2r_src/param.py | import argparse
import os
import torch
class Param:
def __init__(self):
self.parser = argparse.ArgumentParser(description="")
# General
self.parser.add_argument('--test_only', type=int, default=0, help='fast mode for testing')
self.parser.add_argument('--iters', type=int, default=... | 4,900 | 48.01 | 120 | py |
Diagnose_VLN | Diagnose_VLN-master/r2r/model/Recurrent-VLN-BERT/r2r_src/vlnbert/vlnbert_PREVALENT.py | # PREVALENT, 2020, weituo.hao@duke.edu
# Modified in Recurrent VLN-BERT, 2020, Yicong.Hong@anu.edu.au
from __future__ import absolute_import, division, print_function, unicode_literals
import json
import logging
import math
import os
import sys
from io import open
import torch
from torch import nn
from torch.nn impo... | 19,392 | 42.095556 | 159 | py |
Diagnose_VLN | Diagnose_VLN-master/r2r/model/Recurrent-VLN-BERT/r2r_src/vlnbert/vlnbert_OSCAR.py | # Copyright (c) 2020 Microsoft Corporation. Licensed under the MIT license.
# Modified in Recurrent VLN-BERT, 2020, Yicong.Hong@anu.edu.au
from __future__ import absolute_import, division, print_function, unicode_literals
import logging
import math
import torch
from torch import nn
import torch.nn.functional as F
from... | 12,034 | 41.526502 | 176 | py |
Diagnose_VLN | Diagnose_VLN-master/r2r/model/Recurrent-VLN-BERT/r2r_src/vlnbert/vlnbert_init.py | # Recurrent VLN-BERT, 2020, by Yicong.Hong@anu.edu.au
from pytorch_transformers import (BertConfig, BertTokenizer)
def get_tokenizer(args):
if args.vlnbert in ['oscar', 'uninit_oscar']:
tokenizer_class = BertTokenizer
model_name_or_path = 'Oscar/pretrained_models/base-no-labels/ep_67_588997'
... | 2,743 | 41.215385 | 110 | py |
Diagnose_VLN | Diagnose_VLN-master/r2r/model/FAST/train_reranker.py | from env import R2RBatch
from utils import Tokenizer, read_vocab, check_dir
from vocab import TRAINVAL_VOCAB, TRAIN_VOCAB
import os
import torch
import torch.nn as nn
import json
import torch.nn.functional as F
import torch.optim as optim
import numpy as np
class Net(nn.Module):
def __init__(self, input_dim):
... | 7,135 | 33.980392 | 148 | py |
Diagnose_VLN | Diagnose_VLN-master/r2r/model/FAST/run_search.py | import torch
from torch import optim
import os
import os.path
import json
import time
import numpy as np
import pandas as pd
from collections import defaultdict
import argparse
import pprint; pp = pprint.PrettyPrinter(indent=4)
import utils
from utils import read_vocab, Tokenizer, vocab_pad_idx, timeSince, try_cuda
f... | 15,095 | 38.007752 | 148 | py |
Diagnose_VLN | Diagnose_VLN-master/r2r/model/FAST/train_speaker.py | import torch
from torch import optim
import os
import os.path
import time
import numpy as np
import pandas as pd
from collections import defaultdict
import argparse
import utils
from utils import read_vocab, Tokenizer, timeSince, try_cuda
from env import R2RBatch, ImageFeatures
from model import SpeakerEncoderLSTM, S... | 10,029 | 34.821429 | 81 | py |
Diagnose_VLN | Diagnose_VLN-master/r2r/model/FAST/follower.py | ''' Agents: stop/random/shortest/seq2seq '''
import json
import sys
import numpy as np
import networkx as nx
import random
from collections import namedtuple, defaultdict
import os
import torch
import torch.nn as nn
from torch.autograd import Variable
import torch.nn.functional as F
import torch.distributions as D
... | 81,221 | 43.602965 | 234 | py |
Diagnose_VLN | Diagnose_VLN-master/r2r/model/FAST/scorer.py | import torch
import torch.nn as nn
from utils import try_cuda
from speaker import batch_observations_and_actions
import numpy as np
class Scorer:
def __init__(self):
self.scorer = scorer
self.text_encoder = encoder
self.traj_encoder = None
self.sm = try_cuda(nn.Softmax(dim=1))
... | 3,170 | 37.670732 | 95 | py |
Diagnose_VLN | Diagnose_VLN-master/r2r/model/FAST/utils.py | ''' Utils for io, language, connectivity graphs etc '''
import os
import sys
import re
import string
import json
import time
import math
from collections import Counter
import numpy as np
import networkx as nx
import subprocess
import itertools
import base64
import heapq
import random
from nltk.corpus import wordnet a... | 14,762 | 31.375 | 126 | py |
Diagnose_VLN | Diagnose_VLN-master/r2r/model/FAST/model.py | import math
import torch
import torch.nn as nn
from torch.autograd import Variable
import torch.nn.functional as F
from torch.nn.utils.rnn import pack_padded_sequence, pad_packed_sequence
import numpy as np
from utils import try_cuda
from attn import MultiHeadAttention
from env import ConvolutionalImageFeatures, Bott... | 37,332 | 41.327664 | 106 | py |
Diagnose_VLN | Diagnose_VLN-master/r2r/model/FAST/speaker.py | import json
import sys
import numpy as np
import random
from collections import namedtuple
import torch
import torch.nn as nn
from torch.autograd import Variable
import torch.nn.functional as F
import torch.distributions as D
from utils import vocab_pad_idx, vocab_bos_idx, vocab_eos_idx, flatten, try_cuda
from follow... | 17,948 | 42.671533 | 176 | py |
Diagnose_VLN | Diagnose_VLN-master/r2r/model/FAST/make_speaker.py | import torch
from torch import optim
import os
import os.path
import time
import numpy as np
import pandas as pd
from collections import defaultdict
import argparse
import utils
from utils import read_vocab, Tokenizer, timeSince, try_cuda
from env import R2RBatch, ImageFeatures
from model import SpeakerEncoderLSTM, S... | 1,431 | 26.018868 | 76 | py |
Diagnose_VLN | Diagnose_VLN-master/r2r/model/FAST/refine_search.py | import pprint; pp = pprint.PrettyPrinter(indent=2)
from env import R2RBatch, ImageFeatures
from utils import Tokenizer, read_vocab, DotDict
from vocab import TRAINVAL_VOCAB, TRAIN_VOCAB
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
import json
import numpy as np
from tra... | 11,225 | 33.121581 | 177 | py |
Diagnose_VLN | Diagnose_VLN-master/r2r/model/FAST/test_with_reranker.py | import torch
from torch import optim
import os
import os.path
import json
import time
import numpy as np
import pandas as pd
from collections import defaultdict
import pprint; pp = pprint.PrettyPrinter(indent=4)
import utils
from utils import timeSince, try_cuda
from utils import filter_param, colorize
from utils imp... | 17,240 | 40.644928 | 169 | py |
Diagnose_VLN | Diagnose_VLN-master/r2r/model/FAST/attn.py | import torch
import torch.nn as nn
import numpy as np
class ScaledDotProductAttention(nn.Module):
''' Scaled Dot-Product Attention '''
def __init__(self, temperature, attn_dropout=0.1):
super().__init__()
self.temperature = temperature
self.dropout = nn.Dropout(attn_dropout)
se... | 2,719 | 31.771084 | 96 | py |
Diagnose_VLN | Diagnose_VLN-master/r2r/model/FAST/train.py | import torch
from torch import optim
import os
import os.path
import time
import numpy as np
import pandas as pd
from collections import defaultdict
import argparse
import utils
from utils import read_vocab, Tokenizer, vocab_pad_idx, timeSince, try_cuda
from utils import module_grad, colorize, filter_param
from env i... | 14,986 | 38.543536 | 130 | py |
Diagnose_VLN | Diagnose_VLN-master/r2r/model/FAST/env.py | ''' Batched Room-to-Room navigation environment '''
import os
import sys
file_path = os.path.dirname(__file__)
module_path = os.path.abspath(os.path.join(file_path))
sys.path.append(module_path)
module_path = os.path.abspath(os.path.join(file_path,'..','..','build'))
sys.path.append(module_path)
sys.path.append('../R2... | 39,556 | 42.564978 | 231 | py |
Diagnose_VLN | Diagnose_VLN-master/data_processing/Matterport3DSimulator/scripts/generate_clip_img_features.py | #!/usr/bin/env python3
''' Script to precompute image features using a Caffe ResNet CNN, using 36 discretized views
at each viewpoint in 30 degree increments, and the provided camera WIDTH, HEIGHT
and VFOV parameters. '''
import os
from tkinter import E
import numpy as np
import cv2
import json
import math
i... | 10,231 | 33.921502 | 127 | py |
Diagnose_VLN | Diagnose_VLN-master/data_processing/Matterport3DSimulator/scripts/generate_img_features.py | #!/usr/bin/env python3
''' Script to precompute image features using a Caffe ResNet CNN, using 36 discretized views
at each viewpoint in 30 degree increments, and the provided camera WIDTH, HEIGHT
and VFOV parameters. '''
import os
import numpy as np
import cv2
import json
import math
import base64
import cs... | 11,376 | 35.581994 | 127 | py |
Diagnose_VLN | Diagnose_VLN-master/data_processing/Matterport3DSimulator/scripts/precompute_img_features.py | #!/usr/bin/env python3
''' Script to precompute image features using a Caffe ResNet CNN, using 36 discretized views
at each viewpoint in 30 degree increments, and the provided camera WIDTH, HEIGHT
and VFOV parameters. '''
import numpy as np
import cv2
import json
import math
import base64
import csv
import s... | 5,570 | 32.969512 | 120 | py |
Diagnose_VLN | Diagnose_VLN-master/data_processing/Matterport3DSimulator/tasks/R2R/model.py |
import torch
import torch.nn as nn
from torch.autograd import Variable
import torch.nn.functional as F
from torch.nn.utils.rnn import pack_padded_sequence, pad_packed_sequence
class EncoderLSTM(nn.Module):
''' Encodes navigation instructions, returning hidden state context (for
attention methods) and a d... | 5,708 | 38.923077 | 100 | py |
Diagnose_VLN | Diagnose_VLN-master/data_processing/Matterport3DSimulator/tasks/R2R/agent.py | ''' Agents: stop/random/shortest/seq2seq '''
import json
import os
import sys
import numpy as np
import random
import time
import torch
import torch.nn as nn
import torch.distributions as D
from torch.autograd import Variable
from torch import optim
import torch.nn.functional as F
from env import R2RBatch
from util... | 11,955 | 36.246106 | 114 | py |
Diagnose_VLN | Diagnose_VLN-master/data_processing/Matterport3DSimulator/tasks/R2R/train.py |
import torch
import torch.nn as nn
from torch.autograd import Variable
from torch import optim
import torch.nn.functional as F
import os
import time
import numpy as np
import pandas as pd
from collections import defaultdict
from utils import read_vocab,write_vocab,build_vocab,Tokenizer,padding_idx,timeSince
from env... | 6,637 | 39.230303 | 116 | py |
Diagnose_VLN | Diagnose_VLN-master/rxr/model/CLIP-ViL-VLN/precomute_imagenet_views.py | #!/usr/bin/env python
''' Script to precompute image features using a Caffe ResNet CNN, using 36 discretized views
at each viewpoint in 30 degree increments, and the provided camera WIDTH, HEIGHT
and VFOV parameters. '''
import argparse
import numpy as np
import cv2
import json
import math
import base64
impo... | 6,117 | 30.214286 | 120 | py |
Diagnose_VLN | Diagnose_VLN-master/rxr/model/CLIP-ViL-VLN/rxr_src/utils.py | ''' Utils for io, language, connectivity graphs etc '''
import os
import sys
import re
sys.path.append('build')
sys.path.append('../../../data_processing/Matterport3DSimulator/build-clipvil')
import MatterSim
import string
import json
import time
import math
from collections import Counter, defaultdict
import numpy as... | 20,010 | 32.631933 | 126 | py |
Diagnose_VLN | Diagnose_VLN-master/rxr/model/CLIP-ViL-VLN/rxr_src/model.py |
import torch
import torch.nn as nn
from torch.autograd import Variable
import torch.nn.functional as F
from torch.nn.utils.rnn import pack_padded_sequence, pad_packed_sequence
from param import args
class EncoderLSTM(nn.Module):
''' Encodes navigation instructions, returning hidden state context (for
att... | 12,387 | 39.75 | 173 | py |
Diagnose_VLN | Diagnose_VLN-master/rxr/model/CLIP-ViL-VLN/rxr_src/speaker.py | import torch
import numpy as np
from param import args
import os
import utils
import model
import torch.nn.functional as F
class Speaker():
env_actions = {
'left': (0,-1, 0), # left
'right': (0, 1, 0), # right
'up': (0, 0, 1), # up
'down': (0, 0,-1), # down
'forward': (1, 0... | 16,945 | 42.562982 | 144 | py |
Diagnose_VLN | Diagnose_VLN-master/rxr/model/CLIP-ViL-VLN/rxr_src/agent.py |
import json
import os
import sys
import numpy as np
import random
import math
import time
import torch
import torch.nn as nn
from torch.autograd import Variable
from torch import optim
import torch.nn.functional as F
from env import R2RBatch
from utils import padding_idx, add_idx, Tokenizer
import utils
import model... | 38,535 | 43.861467 | 145 | py |
Diagnose_VLN | Diagnose_VLN-master/rxr/model/CLIP-ViL-VLN/rxr_src/train.py |
import torch
import os
import time
import json
import numpy as np
from collections import defaultdict
from speaker import Speaker
from utils import read_vocab,write_vocab,build_vocab,Tokenizer,padding_idx,timeSince, read_img_features, build_vocab_hi, build_vocab_te
import utils
from env import R2RBatch
from agent im... | 22,540 | 40.435662 | 169 | py |
Diagnose_VLN | Diagnose_VLN-master/rxr/model/CLIP-ViL-VLN/rxr_src/param.py | import argparse
import os
import torch
class Param:
def __init__(self):
self.parser = argparse.ArgumentParser(description="")
# General
self.parser.add_argument('--iters', type=int, default=100000)
self.parser.add_argument('--name', type=str, default='default')
self.parser... | 6,344 | 52.319328 | 125 | py |
Diagnose_VLN | Diagnose_VLN-master/rxr/model/CLIP-ViL-VLN/r2r_src/utils.py | ''' Utils for io, language, connectivity graphs etc '''
import os
import sys
import re
sys.path.append('build')
import MatterSim
import string
import json
import time
import math
from collections import Counter, defaultdict
import numpy as np
import networkx as nx
from param import args
# padding, unknown word, end ... | 17,752 | 32.559546 | 139 | py |
Diagnose_VLN | Diagnose_VLN-master/rxr/model/CLIP-ViL-VLN/r2r_src/model.py |
import torch
import torch.nn as nn
from torch.autograd import Variable
import torch.nn.functional as F
from torch.nn.utils.rnn import pack_padded_sequence, pad_packed_sequence
from param import args
class EncoderLSTM(nn.Module):
''' Encodes navigation instructions, returning hidden state context (for
att... | 12,387 | 39.75 | 173 | py |
Diagnose_VLN | Diagnose_VLN-master/rxr/model/CLIP-ViL-VLN/r2r_src/speaker.py | import torch
import numpy as np
from param import args
import os
import utils
import model
import torch.nn.functional as F
class Speaker():
env_actions = {
'left': (0,-1, 0), # left
'right': (0, 1, 0), # right
'up': (0, 0, 1), # up
'down': (0, 0,-1), # down
'forward': (1, 0... | 16,945 | 42.562982 | 144 | py |
Diagnose_VLN | Diagnose_VLN-master/rxr/model/CLIP-ViL-VLN/r2r_src/agent.py |
import json
import os
import sys
import numpy as np
import random
import math
import time
import torch
import torch.nn as nn
from torch.autograd import Variable
from torch import optim
import torch.nn.functional as F
from env import R2RBatch
from utils import padding_idx, add_idx, Tokenizer
import utils
import model... | 38,382 | 43.735431 | 145 | py |
Diagnose_VLN | Diagnose_VLN-master/rxr/model/CLIP-ViL-VLN/r2r_src/train.py |
import torch
import os
import time
import json
import numpy as np
from collections import defaultdict
from speaker import Speaker
from utils import read_vocab,write_vocab,build_vocab,Tokenizer,padding_idx,timeSince, read_img_features
import utils
from env import R2RBatch
from agent import Seq2SeqAgent
from eval impo... | 18,406 | 38.75594 | 138 | py |
Diagnose_VLN | Diagnose_VLN-master/rxr/model/CLIP-ViL-VLN/r2r_src/param.py | import argparse
import os
import torch
class Param:
def __init__(self):
self.parser = argparse.ArgumentParser(description="")
# General
self.parser.add_argument('--iters', type=int, default=100000)
self.parser.add_argument('--name', type=str, default='default')
self.parser... | 5,743 | 50.285714 | 125 | py |
Diagnose_VLN | Diagnose_VLN-master/rxr/model/VLN-HAMT/finetune_src/cvdn/main.py | import os
import json
import time
import numpy as np
from collections import defaultdict
import torch
from tensorboardX import SummaryWriter
from utils.misc import set_random_seed
from utils.logger import write_to_record_file, print_progress, timeSince
from utils.distributed import init_distributed, is_default_gpu
fr... | 10,960 | 38.428058 | 133 | py |
Diagnose_VLN | Diagnose_VLN-master/rxr/model/VLN-HAMT/finetune_src/cvdn/parser.py | import argparse
import os
import torch
def parse_args():
parser = argparse.ArgumentParser(description="")
parser.add_argument('--root_dir', type=str, default='/sequoia/data1/shichen/datasets')
parser.add_argument(
'--dataset', type=str, default='cvdn',
choices=['cvdn']
)
parser.a... | 5,668 | 40.683824 | 113 | py |
Diagnose_VLN | Diagnose_VLN-master/rxr/model/VLN-HAMT/finetune_src/cvdn/agent.py | import json
import os
import sys
import numpy as np
import random
import math
import time
from collections import defaultdict
import torch
import torch.nn as nn
from torch import optim
import torch.nn.functional as F
from utils.misc import length2mask
from r2r.agent_cmt import Seq2SeqCMTAgent
class NavCMTAgent(Seq2... | 12,394 | 42.038194 | 126 | py |
Diagnose_VLN | Diagnose_VLN-master/rxr/model/VLN-HAMT/finetune_src/models/vilmodel_cmt.py | import json
import logging
import math
import os
import sys
from io import open
from typing import Callable, List, Tuple
import numpy as np
import copy
import torch
from torch import nn
from torch import Tensor, device, dtype
from transformers import BertPreTrainedModel
logger = logging.getLogger(__name__)
BertLaye... | 31,912 | 42.716438 | 124 | py |
Diagnose_VLN | Diagnose_VLN-master/rxr/model/VLN-HAMT/finetune_src/models/model_HAMT.py | import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from utils.misc import length2mask
from models.vlnbert_init import get_vlnbert_models
class VLNBertCMT(nn.Module):
def __init__(self, args):
super().__init__()
print('\nInitalizing the VLN-BERT model ...')
... | 10,616 | 38.322222 | 91 | py |
Diagnose_VLN | Diagnose_VLN-master/rxr/model/VLN-HAMT/finetune_src/models/vlnbert_init.py | import torch
def get_tokenizer(args):
from transformers import AutoTokenizer
if args.dataset == 'rxr' or args.tokenizer == 'xlm':
cfg_name = 'xlm-roberta-base'
else:
cfg_name = 'bert-base-uncased'
tokenizer = AutoTokenizer.from_pretrained(cfg_name)
return tokenizer
def get_vlnbert... | 2,384 | 32.591549 | 61 | py |
Diagnose_VLN | Diagnose_VLN-master/rxr/model/VLN-HAMT/finetune_src/r2r/agent_cmt.py | import json
import os
import sys
import numpy as np
import random
import math
import time
from collections import defaultdict
import torch
import torch.nn as nn
from torch import optim
import torch.nn.functional as F
from torch.nn.parallel import DistributedDataParallel as DDP
from utils.distributed import is_default... | 28,400 | 42.964396 | 126 | py |
Diagnose_VLN | Diagnose_VLN-master/rxr/model/VLN-HAMT/finetune_src/r2r/main.py | import os
import json
import time
import numpy as np
from collections import defaultdict
import torch
from tensorboardX import SummaryWriter
from utils.misc import set_random_seed
from utils.logger import write_to_record_file, print_progress, timeSince
from utils.distributed import init_distributed, is_default_gpu
fr... | 13,565 | 39.738739 | 169 | py |
Diagnose_VLN | Diagnose_VLN-master/rxr/model/VLN-HAMT/finetune_src/r2r/agent_r2rback.py | import json
import os
import sys
import numpy as np
import random
import math
import time
from collections import defaultdict
import torch
import torch.nn as nn
from torch import optim
import torch.nn.functional as F
from torch.nn.parallel import DistributedDataParallel as DDP
from utils.misc import length2mask
from... | 15,573 | 42.141274 | 126 | py |
Diagnose_VLN | Diagnose_VLN-master/rxr/model/VLN-HAMT/finetune_src/reverie/parser.py | import argparse
import os
import torch
def parse_args():
parser = argparse.ArgumentParser(description="")
parser.add_argument('--root_dir', type=str, default='../datasets')
parser.add_argument(
'--dataset', type=str, default='reverie',
choices=['reverie']
)
parser.add_argument('-... | 6,082 | 41.838028 | 113 | py |
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