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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GTA-RL | GTA-RL-master/test.py | import os
import numpy as np
import torch
import time
from matplotlib import pyplot as plt
import matplotlib.cm as cm
from matplotlib.collections import PatchCollection
from matplotlib.patches import Rectangle
from matplotlib.lines import Line2D
from utils import load_model
from problems.tsp.tsp_gurobi import *
from p... | 9,668 | 32.341379 | 116 | py |
GTA-RL | GTA-RL-master/run.py | #!/usr/bin/env python
import os
import json
import pprint as pp
import torch
import torch.optim as optim
from tensorboard_logger import Logger as TbLogger
from nets.critic_network import CriticNetwork
from options import get_options
from train import train_epoch, validate, get_inner_model
from reinforce_baselines im... | 6,307 | 33.850829 | 120 | py |
GTA-RL | GTA-RL-master/options.py | import os
import time
import argparse
import torch
from utils.paths import find_next_path_id, createNextFileName
def get_options(args=None):
parser = argparse.ArgumentParser(
description="Attention based model for solving the Travelling Salesman Problem with Reinforcement Learning")
# Data
parser... | 6,545 | 66.484536 | 129 | py |
GTA-RL | GTA-RL-master/eval.py | import math
import torch
import os
import argparse
import numpy as np
import itertools
from tqdm import tqdm
from utils import load_model, move_to
from utils.data_utils import save_dataset
from torch.utils.data import DataLoader
import time
from datetime import timedelta
from utils.functions import parse_softmax_temper... | 11,392 | 44.031621 | 120 | py |
GTA-RL | GTA-RL-master/train.py | import os
import time
from tqdm import tqdm
import torch
import math
from torch.utils.data import DataLoader
from torch.nn import DataParallel
from nets.attention_model import set_decode_type
from utils.log_utils import log_values
from utils import move_to
def get_inner_model(model):
return model.module if isin... | 5,119 | 30.219512 | 113 | py |
GTA-RL | GTA-RL-master/nets/pointer_network.py | import torch
import torch.nn as nn
from torch.autograd import Variable
import math
import numpy as np
class Encoder(nn.Module):
"""Maps a graph represented as an input sequence
to a hidden vector"""
def __init__(self, input_dim, hidden_dim):
super(Encoder, self).__init__()
self.hidden_dim ... | 13,515 | 37.288952 | 118 | py |
GTA-RL | GTA-RL-master/nets/st_attention_model.py | import torch
from torch import nn
from torch.utils.checkpoint import checkpoint
import math
from typing import NamedTuple
from utils.tensor_functions import compute_in_batches
from nets.graph_encoder import GraphAttentionEncoder
from torch.nn import DataParallel
from utils.beam_search import CachedLookup
from utils.fu... | 23,874 | 41.940647 | 122 | py |
GTA-RL | GTA-RL-master/nets/attention_model.py | import torch
from torch import nn
from torch.utils.checkpoint import checkpoint
import math
from typing import NamedTuple
from utils.tensor_functions import compute_in_batches
from nets.graph_encoder import GraphAttentionEncoder
from torch.nn import DataParallel
from utils.beam_search import CachedLookup
from utils.fu... | 23,184 | 41.855823 | 122 | py |
GTA-RL | GTA-RL-master/nets/graph_encoder.py | import torch
import numpy as np
from torch import nn
import math
class SkipConnection(nn.Module):
def __init__(self, module):
super(SkipConnection, self).__init__()
self.module = module
def forward(self, input):
return input + self.module(input)
class PositionalEncoding(nn.Module):
... | 16,939 | 34.738397 | 117 | py |
GTA-RL | GTA-RL-master/nets/critic_network.py | from torch import nn
from nets.graph_encoder import GraphAttentionEncoder
class CriticNetwork(nn.Module):
def __init__(
self,
input_dim,
embedding_dim,
hidden_dim,
n_layers,
encoder_normalization,
st_attention
):
super(CriticNetwork, self).__ini... | 1,026 | 22.883721 | 58 | py |
GTA-RL | GTA-RL-master/problems/pctsp/state_pctsp.py | import torch
from typing import NamedTuple
from utils.boolmask import mask_long2bool, mask_long_scatter
import torch.nn.functional as F
class StatePCTSP(NamedTuple):
# Fixed input
coords: torch.Tensor # Depot + loc
expected_prize: torch.Tensor
real_prize: torch.Tensor
penalty: torch.Tensor
#... | 7,409 | 43.107143 | 119 | py |
GTA-RL | GTA-RL-master/problems/pctsp/problem_pctsp.py | from torch.utils.data import Dataset
import torch
import os
import pickle
from problems.pctsp.state_pctsp import StatePCTSP
from utils.beam_search import beam_search
class PCTSP(object):
NAME = 'pctsp' # Prize Collecting TSP, without depot, with penalties
@staticmethod
def _get_costs(dataset, pi, stoch... | 7,214 | 38 | 120 | py |
GTA-RL | GTA-RL-master/problems/tsp/problem_tsp.py | from torch.utils.data import Dataset
import torch
import os
import pickle
from problems.tsp.state_tsp import StateTSP
from utils.beam_search import beam_search
class TSP(object):
NAME = 'tsp'
@staticmethod
def get_costs(dataset, pi):
# Check that tours are valid, i.e. contain 0 to n -1
a... | 4,123 | 32.803279 | 115 | py |
GTA-RL | GTA-RL-master/problems/tsp/tsp_baseline.py | import argparse
import numpy as np
import os
import time
from datetime import timedelta
from scipy.spatial import distance_matrix
from utils import run_all_in_pool
from utils.data_utils import check_extension, load_dataset, save_dataset
from subprocess import check_call, check_output, CalledProcessError
from problems.v... | 21,577 | 36.789842 | 120 | py |
GTA-RL | GTA-RL-master/problems/tsp/state_tsp.py | import torch
from typing import NamedTuple
from utils.boolmask import mask_long2bool, mask_long_scatter
class StateTSP(NamedTuple):
# Fixed input
loc: torch.Tensor
dist: torch.Tensor
# If this state contains multiple copies (i.e. beam search) for the same instance, then for memory efficiency
# th... | 5,751 | 37.346667 | 121 | py |
GTA-RL | GTA-RL-master/problems/vrp/problem_vrp.py | from torch.utils.data import Dataset
import torch
import os
import pickle
from problems.vrp.state_cvrp import StateCVRP
from problems.vrp.state_sdvrp import StateSDVRP
from utils.beam_search import beam_search
class CVRP(object):
NAME = 'cvrp' # Capacitated Vehicle Routing Problem
VEHICLE_CAPACITY = 1.0 ... | 9,547 | 37.345382 | 128 | py |
GTA-RL | GTA-RL-master/problems/vrp/state_sdvrp.py | import torch
from typing import NamedTuple
class StateSDVRP(NamedTuple):
# Fixed input
coords: torch.Tensor
demand: torch.Tensor
# If this state contains multiple copies (i.e. beam search) for the same instance, then for memory efficiency
# the coords and demands tensors are not kept multiple tim... | 4,821 | 39.183333 | 119 | py |
GTA-RL | GTA-RL-master/problems/vrp/state_cvrp.py | import torch
from typing import NamedTuple
from utils.boolmask import mask_long2bool, mask_long_scatter
class StateCVRP(NamedTuple):
# Fixed input
coords: torch.Tensor # Depot + loc
demand: torch.Tensor
# If this state contains multiple copies (i.e. beam search) for the same instance, then for memor... | 6,807 | 39.766467 | 118 | py |
GTA-RL | GTA-RL-master/problems/op/op_baseline.py | import argparse
import os
import numpy as np
from utils import run_all_in_pool
from utils.data_utils import check_extension, load_dataset, save_dataset
from subprocess import check_call, check_output
import tempfile
import time
from datetime import timedelta
from problems.op.opga.opevo import run_alg as run_opga_alg
fr... | 16,891 | 41.764557 | 118 | py |
GTA-RL | GTA-RL-master/problems/op/problem_op.py | from torch.utils.data import Dataset
import torch
import os
import pickle
from problems.op.state_op import StateOP
from utils.beam_search import beam_search
class OP(object):
NAME = 'op' # Orienteering problem
@staticmethod
def get_costs(dataset, pi):
if pi.size(-1) == 1: # In case all tours d... | 4,855 | 33.197183 | 106 | py |
GTA-RL | GTA-RL-master/problems/op/tsiligirides.py | import torch
from problems.op.state_op import StateOP
def op_tsiligirides(batch, sample=False, power=4.0):
state = StateOP.initialize(batch)
all_a = []
while not state.all_finished():
# Compute scores
mask = state.get_mask()
p = (
(mask[..., 1:] == 0).float() *
... | 1,672 | 37.906977 | 108 | py |
GTA-RL | GTA-RL-master/problems/op/state_op.py | import torch
from typing import NamedTuple
from utils.boolmask import mask_long2bool, mask_long_scatter
import torch.nn.functional as F
class StateOP(NamedTuple):
# Fixed input
coords: torch.Tensor # Depot + loc
prize: torch.Tensor
# Max length is not a single value, but one for each node indicating ... | 7,026 | 42.91875 | 118 | py |
GTA-RL | GTA-RL-master/utils/tensor_functions.py | import torch
def compute_in_batches(f, calc_batch_size, *args, n=None):
"""
Computes memory heavy function f(*args) in batches
:param n: the total number of elements, optional if it cannot be determined as args[0].size(0)
:param f: The function that is computed, should take only tensors as arguments a... | 1,608 | 44.971429 | 120 | py |
GTA-RL | GTA-RL-master/utils/monkey_patch.py | import torch
from itertools import chain
from collections import defaultdict, Iterable
from copy import deepcopy
def load_state_dict(self, state_dict):
"""Loads the optimizer state.
Arguments:
state_dict (dict): optimizer state. Should be an object returned
from a call to :meth:`state_dict... | 2,734 | 38.071429 | 90 | py |
GTA-RL | GTA-RL-master/utils/functions.py | import warnings
import torch
import numpy as np
import os
import json
from tqdm import tqdm
from multiprocessing.dummy import Pool as ThreadPool
from multiprocessing import Pool
import torch.nn.functional as F
def load_problem(name):
from problems import TSP, DTSP, CVRP, DCVRP, SDVRP, OP, PCTSPDet, PCTSPStoch
... | 6,679 | 30.214953 | 109 | py |
GTA-RL | GTA-RL-master/utils/boolmask.py | import torch
import torch.nn.functional as F
def _pad_mask(mask):
# By taking -size % 8, we get 0 if exactly divisible by 8
# and required padding otherwise (i.e. -1 % 8 = 7 pad)
pad = -mask.size(-1) % 8
if pad != 0:
mask = F.pad(mask, [0, pad])
return mask, mask.size(-1) // 8
def _mask_... | 2,588 | 36.521739 | 131 | py |
GTA-RL | GTA-RL-master/utils/lexsort.py | import torch
import numpy as np
def torch_lexsort(keys, dim=-1):
if keys[0].is_cuda:
return _torch_lexsort_cuda(keys, dim)
else:
# Use numpy lex sort
return torch.from_numpy(np.lexsort([k.numpy() for k in keys], axis=dim))
def _torch_lexsort_cuda(keys, dim=-1):
"""
Function c... | 2,382 | 41.553571 | 127 | py |
GTA-RL | GTA-RL-master/utils/beam_search.py | import time
import torch
from typing import NamedTuple
from utils.lexsort import torch_lexsort
def beam_search(dynamic, *args, **kwargs):
if dynamic:
beams, final_state = _dynamic_beam_search(*args, **kwargs)
else:
beams, final_state = _beam_search(*args, **kwargs)
return get_beam_search_r... | 9,624 | 35.877395 | 124 | py |
just-ask | just-ask-main/main_howtovqa.py | import torch
import torch.nn as nn
import torch.optim as optim
from torch.utils.data import DataLoader
from transformers import get_cosine_schedule_with_warmup
import numpy as np
import random
import os
import pickle
import logging
from args import get_args
from model.multimodal_transformer import MMT_VideoQA
from loss... | 5,378 | 29.050279 | 94 | py |
just-ask | just-ask-main/eval_videoqa_cm.py | import torch
import torch.nn as nn
from torch.utils.data import Dataset, DataLoader
from torch.utils.data.dataloader import default_collate
import numpy as np
import random
import os
import logging
import collections
import pandas as pd
from transformers import DistilBertTokenizer
from args import get_args
from model.m... | 9,394 | 31.735192 | 108 | py |
just-ask | just-ask-main/main_htm.py | import torch
import torch.nn as nn
from torch.utils.data import DataLoader
import numpy as np
import torch.optim as optim
from args import get_args
import random
import os
import pickle
from torch.optim.lr_scheduler import StepLR
import logging
from transformers import DistilBertTokenizer
from data.howto_loader import ... | 3,978 | 28.474074 | 82 | py |
just-ask | just-ask-main/eval_videoqa.py | import torch
import torch.nn as nn
import numpy as np
import random
import collections
from args import get_args
from model.multimodal_transformer import MMT_VideoQA
from util import (
compute_a2v,
get_mask,
compute_aggreeings,
get_types,
get_most_common,
compute_word_stats,
)
from data.videoqa_... | 4,923 | 29.395062 | 136 | py |
just-ask | just-ask-main/loss.py | import torch as torch
import torch.nn.functional as F
class Contrastive_Loss(torch.nn.Module):
def __init__(self):
super(Contrastive_Loss, self).__init__()
self.ce_loss = torch.nn.CrossEntropyLoss()
def forward(self, x, target):
return self.ce_loss(x, target)
class LogSoftmax(torch.... | 945 | 26.823529 | 80 | py |
just-ask | just-ask-main/demo_videoqa.py | import torch
import torch.nn as nn
import numpy as np
import random
from transformers import DistilBertTokenizer
from args import get_args
from model.multimodal_transformer import MMT_VideoQA
from util import compute_a2v, get_mask
import ffmpeg
from extract.s3dg import S3D
from extract.preprocessing import Preprocessin... | 4,273 | 28.888112 | 88 | py |
just-ask | just-ask-main/main_videoqa.py | import torch
import torch.nn as nn
import torch.optim as optim
import numpy as np
import random
import os
import logging
from transformers import get_cosine_schedule_with_warmup, DistilBertTokenizer
from args import get_args
from model.multimodal_transformer import MMT_VideoQA
from loss import LogSoftmax
from util impo... | 3,932 | 29.488372 | 93 | py |
just-ask | just-ask-main/util.py | import re
import torch
import torch.nn.functional as F
import json
import collections
import numpy as np
def tokenize(
seq,
tokenizer,
add_special_tokens=True,
max_length=10,
dynamic_padding=True,
truncation=True,
):
"""
:param seq: sequence of sequences of text
:param tokenizer: b... | 8,758 | 32.559387 | 161 | py |
just-ask | just-ask-main/videoqageneration/generate_questions_webvid.py | import pickle
import os
from tqdm import tqdm
from torch.utils.data import DataLoader, Dataset
import torch
import math
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
import argparse
import sys
import pandas as pd
sys.path.insert(0, os.getcwd())
from global_parameters import TRANSFORMERS_PATH, qas_dir, ... | 7,313 | 32.39726 | 108 | py |
just-ask | just-ask-main/videoqageneration/extract_answers_webvid.py | import pickle
import os
from tqdm import tqdm
from torch.utils.data import DataLoader, Dataset
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
import argparse
import torch
import sys
import pandas as pd
sys.path.insert(0, os.getcwd()) # to correct with parent folder
from global_parameters import answers... | 5,208 | 30.957055 | 96 | py |
just-ask | just-ask-main/videoqageneration/extract_answers.py | import pickle
import os
from tqdm import tqdm
from torch.utils.data import DataLoader, Dataset
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
import argparse
import math
import torch
import sys
from global_parameters import answers_dir, QG_REPO_DIR, HOWTO_PATH, TRANSFORMERS_PATH
sys.path.insert(0, os.p... | 5,134 | 31.916667 | 96 | py |
just-ask | just-ask-main/videoqageneration/generate_questions.py | import pickle
import os
from tqdm import tqdm
from torch.utils.data import DataLoader, Dataset
import torch
import math
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
import argparse
from global_parameters import TRANSFORMERS_PATH, qas_dir, HOWTO_PATH
class Question_Generation_Dataset(Dataset):
de... | 6,150 | 32.612022 | 124 | py |
just-ask | just-ask-main/preproc/preproc_how2qa.py | from tqdm import tqdm
import pandas as pd
import os
import numpy as np
import torch
from global_parameters import HOW2QA_PATH, HOWTO_FEATURES_PATH
train_csv = pd.read_csv(os.path.join(HOW2QA_PATH, "how2QA_train_release.csv"))
train_csv.columns = ["vid_id", "timesteps", "a2", "a3", "a4", "question", "a1"]
print(len(tra... | 3,490 | 31.626168 | 87 | py |
just-ask | just-ask-main/train/train_howtovqa.py | import torch
import torch.nn as nn
import logging
import collections
import numpy as np
from util import compute_aggreeings, AverageMeter, get_mask, mask_tokens
def eval_howtovqa(model, val_loader, args):
model.eval()
metrics = collections.defaultdict(int)
count = 0
with torch.no_grad():
for i... | 5,094 | 34.381944 | 143 | py |
just-ask | just-ask-main/train/train_htm.py | import torch
import logging
import math
from tqdm import tqdm
from util import (
mask_tokens,
get_mask,
AverageMeter,
compute_metrics,
print_computed_metrics,
)
def train_mlmcm(model, optimizer, dataloader, scheduler, epoch, args):
model.train()
running_mlm_loss, running_cm_loss = AverageM... | 4,622 | 34.022727 | 95 | py |
just-ask | just-ask-main/train/train_videoqa.py | import torch
import torch.nn as nn
import torch.nn.functional as F
import logging
import collections
from util import compute_aggreeings, AverageMeter, get_mask, mask_tokens
def eval(model, val_loader, a2v, args, test=False):
model.eval()
count = 0
metrics, counts = collections.defaultdict(int), collectio... | 6,057 | 36.165644 | 133 | py |
just-ask | just-ask-main/misc/server_videoqa.py | #!/usr/bin/env python
import os
import json
import torch
import torch.nn.functional as F
import pickle
import random
import urllib
import urllib.request
import cherrypy
from transformers import DistilBertTokenizer
from model.multimodal_transformer import MMT_VideoQA
from util import compute_a2v, get_mask
from args impo... | 14,705 | 42 | 382 | py |
just-ask | just-ask-main/extract/video_loader.py | import torch as th
from torch.utils.data import Dataset
import pandas as pd
import os
import numpy as np
import ffmpeg
class VideoLoader(Dataset):
"""Pytorch video loader."""
def __init__(
self,
csv,
framerate=1,
size=112,
centercrop=False,
):
self.csv = pd... | 3,410 | 33.11 | 87 | py |
just-ask | just-ask-main/extract/preprocessing.py | import torch as th
class Normalize(object):
def __init__(self, mean, std):
self.mean = th.FloatTensor(mean).view(1, 3, 1, 1)
self.std = th.FloatTensor(std).view(1, 3, 1, 1)
def __call__(self, tensor):
tensor = (tensor - self.mean) / (self.std + 1e-8)
return tensor
class Prep... | 1,075 | 29.742857 | 82 | py |
just-ask | just-ask-main/extract/s3dg.py | """Contains the definition for Gated Separable 3D network (S3D-G).
"""
import torch as th
import torch.nn.functional as F
import torch.nn as nn
import numpy as np
import re
from global_parameters import S3D_DICT_PATH
class InceptionBlock(nn.Module):
def __init__(
self,
input_dim,
num_outp... | 12,861 | 33.856369 | 86 | py |
just-ask | just-ask-main/extract/extract.py | import torch as th
import math
import numpy as np
import torch.nn.functional as F
from tqdm import tqdm
import argparse
from extract.video_loader import VideoLoader
from torch.utils.data import DataLoader
from extract.s3dg import S3D
from extract.preprocessing import Preprocessing
from extract.random_sequence_shuffler ... | 3,644 | 32.440367 | 101 | py |
just-ask | just-ask-main/extract/random_sequence_shuffler.py | from torch.utils.data.sampler import Sampler
import numpy as np
class RandomSequenceSampler(Sampler):
def __init__(self, n_sample, seq_len):
self.n_sample = n_sample
self.seq_len = seq_len
def _pad_ind(self, ind):
zeros = np.zeros(self.seq_len - self.n_sample % self.seq_len)
i... | 775 | 28.846154 | 76 | py |
just-ask | just-ask-main/extract/merge_features.py | import numpy as np
import argparse
import os
import torch
from tqdm import tqdm
import pandas as pd
from global_parameters import MSVD_PATH, HOW2QA_PATH
parser = argparse.ArgumentParser(description="Feature merger")
parser.add_argument("--folder", type=str, required=True, help="folder of features")
parser.add_argumen... | 1,710 | 26.596774 | 83 | py |
just-ask | just-ask-main/data/howto_loader.py | import torch
from torch.utils.data import Dataset
import pandas as pd
import os
import numpy as np
from util import tokenize
class HowTo_Dataset(Dataset):
def __init__(
self,
csv_path,
caption,
features_path,
min_time=10,
max_time=20,
min_words=10,
m... | 5,636 | 34.012422 | 124 | py |
just-ask | just-ask-main/data/webvidvqa_loader.py | import torch
from torch.utils.data import Dataset
import pandas as pd
import os
import numpy as np
from torch.utils.data.dataloader import default_collate
from util import tokenize
class WebVidVQA_Dataset(Dataset):
def __init__(
self,
csv_path,
caption,
features_path,
qmax_... | 5,059 | 32.959732 | 130 | py |
just-ask | just-ask-main/data/videotext_loader.py | import torch as th
from torch.utils.data import Dataset
import pandas as pd
import pickle
class VideoText_Dataset(Dataset):
def __init__(
self,
csv_path,
features_path,
max_words=30,
bert_tokenizer=None,
max_feats=20,
):
"""
Args:
"""
... | 4,213 | 28.263889 | 84 | py |
just-ask | just-ask-main/data/videoqa_loader.py | import torch
from torch.utils.data import Dataset, DataLoader
from torch.utils.data.dataloader import default_collate
import pandas as pd
import collections
from util import tokenize
class VideoQADataset(Dataset):
def __init__(
self,
csv_path,
features,
qmax_words=20,
amax_... | 7,370 | 30.909091 | 108 | py |
just-ask | just-ask-main/data/howtovqa_loader.py | import torch
from torch.utils.data import Dataset
import pandas as pd
import os
import numpy as np
from torch.utils.data.dataloader import default_collate
from util import tokenize
class HowToVQA_Dataset(Dataset):
def __init__(
self,
csv_path,
caption,
features_path,
qmax_w... | 6,044 | 32.39779 | 130 | py |
just-ask | just-ask-main/model/multimodal_transformer.py | from transformers.activations import gelu
import torch.nn as nn
import numpy as np
import torch
import math
from model.language_model import Bert, AModel
import copy
from transformers.modeling_outputs import BaseModelOutput
from transformers import DistilBertConfig
def create_sinusoidal_embeddings(n_pos, dim, out):
... | 27,052 | 36.366022 | 134 | py |
just-ask | just-ask-main/model/language_model.py | import torch
import torch.nn as nn
import torch.nn.functional as F
from transformers import DistilBertTokenizer, DistilBertModel
class Bert(nn.Module):
""" Finetuned DistilBERT module """
def __init__(self):
super(Bert, self).__init__()
self.bert_tokenizer = DistilBertTokenizer.from_pretraine... | 1,880 | 28.390625 | 78 | py |
FastFusionNet | FastFusionNet-master/prepro.py | # Origin: https://github.com/taolei87/sru/blob/master/DrQA/prepro.py
# Modified by Felix Wu
import torch
import re
import json
import spacy
# import msgpack
import unicodedata
import numpy as np
import pandas as pd
import argparse
import collections
import multiprocessing
from concurrent.futures import ProcessPoolExe... | 17,786 | 39.151242 | 159 | py |
FastFusionNet | FastFusionNet-master/eval.py | import re
import os
import sys
import time
import json
import random
import logging
import argparse
import torch
from shutil import copyfile
from datetime import datetime
from collections import Counter
from qa.model import DocReaderModel
from qa.utils import *
parser = argparse.ArgumentParser(
description='Eval... | 3,164 | 31.96875 | 144 | py |
FastFusionNet | FastFusionNet-master/train.py | import re
import os
import sys
import time
import json
import random
import logging
import argparse
import torch
from shutil import copyfile
from datetime import datetime
from collections import Counter
from tensorboardX import SummaryWriter
from qa.model import DocReaderModel
from qa.utils import *
parser = argpars... | 15,435 | 47.388715 | 291 | py |
FastFusionNet | FastFusionNet-master/qa/utils.py | # Copyright (c) 2017-present, Facebook, Inc.
# All rights reserved.
# This source code is licensed under the BSD-style license found in the
# LICENSE file in the root directory of this source tree. An additional grant
# of patent rights can be found in the PATENTS file in the same directory.
# Modified by Felix Wu
fr... | 13,206 | 35.183562 | 139 | py |
FastFusionNet | FastFusionNet-master/qa/model.py | # Copyright (c) 2017-present, Facebook, Inc.
# All rights reserved.
# This source code is licensed under the BSD-style license found in the
# LICENSE file in the root directory of this source tree. An additional grant
# of patent rights can be found in the PATENTS file in the same directory.
# Origin: https://github.c... | 7,683 | 36.300971 | 112 | py |
FastFusionNet | FastFusionNet-master/qa/encoder.py | import sys
import torch
import torch.nn as nn
import torch.nn.functional as F
from . import layers
from typing import IO, List, Iterable, Tuple
class RnnEncoder(nn.Module):
"""Network for the Document Reader module of DrQA."""
def __init__(self, opt):
super().__init__()
self.encoder_input_dim... | 1,572 | 37.365854 | 129 | py |
FastFusionNet | FastFusionNet-master/qa/layers.py | # Copyright (c) 2017-present, Facebook, Inc.
# All rights reserved.
# This source code is licensed under the BSD-style license found in the
# LICENSE file in the root directory of this source tree. An additional grant
# of patent rights can be found in the PATENTS file in the same directory.
# Origin: https://github.co... | 57,196 | 37.989093 | 182 | py |
FastFusionNet | FastFusionNet-master/qa/rnn_reader.py | # Copyright (c) 2017-present, Facebook, Inc.
# All rights reserved.
# This source code is licensed under the BSD-style license found in the
# LICENSE file in the root directory of this source tree. An additional grant
# of patent rights can be found in the PATENTS file in the same directory.
# Origin: https://github.c... | 33,559 | 43.332893 | 169 | py |
FastFusionNet | FastFusionNet-master/qa/general_utils.py | # Modified from https://github.com/momohuang/FusionNet-NLI/blob/master/general_utils.py
import re
import os
import sys
import random
import string
import logging
import argparse
import unicodedata
from shutil import copyfile
from datetime import datetime
from collections import Counter
import torch
import msgpack
impo... | 4,322 | 34.727273 | 111 | py |
WPFS | WPFS-main/src/main.py | import json
import pytorch_lightning
import pytorch_lightning as pl
from pytorch_lightning.callbacks.early_stopping import EarlyStopping
from pytorch_lightning.callbacks.model_checkpoint import ModelCheckpoint
from pytorch_lightning.callbacks import RichProgressBar, LearningRateMonitor
from pytorch_lightning.loggers im... | 15,021 | 39.6 | 192 | py |
WPFS | WPFS-main/src/_config.py | BASE_DIR = '.' # path to the project directory
DATA_DIR = f'{BASE_DIR}/data'
LOGS_DIR = f'{BASE_DIR}/logs'
RESULTS_DIR = f"{BASE_DIR}/results"
SEED_VALUE = 42
import random
import numpy as np
import torch
def seed_everything(seed):
random.seed(seed)
np.random.seed(seed)
torch.manual_seed(seed)
tor... | 367 | 15.727273 | 46 | py |
WPFS | WPFS-main/src/sparsity_network.py | import torch
import torch.nn as nn
class SparsityNetwork(nn.Module):
"""
Sparsity network
- same architecture as WPN
- input: gene embedding matrix (D x M)
- output: 1 neuron, sigmoid activation function (which will get multiplied by the weights associated with the gene)
"""
def __init__(self, args, embedding... | 1,231 | 26.377778 | 116 | py |
WPFS | WPFS-main/src/dataset.py | import os
from _config import *
import torch
from torch.utils.data import Dataset, DataLoader
import pytorch_lightning as pl
from torchnmf.nmf import NMF
import scipy.io as spio
import pandas as pd
from sklearn.model_selection import train_test_split, StratifiedKFold
from sklearn.utils.class_weight import compute_cla... | 12,510 | 28.231308 | 155 | py |
WPFS | WPFS-main/src/weight_predictor_network.py | from torch import nn
class WeightPredictorNetwork(nn.Module):
def __init__(self, args, embedding_matrix):
"""
WPN outputs a "virtual" weight matrix W
:param nn.Tensor(D, M) embedding_matrix: matrix with the embeddings (D = number of features, M = embedding size)
"""
super().__init__()
print(f"Initializin... | 1,342 | 32.575 | 123 | py |
WPFS | WPFS-main/src/models.py | import torch
from torch import nn
import torch.nn.functional as F
import pytorch_lightning as pl
import numpy as np
from sklearn.metrics import balanced_accuracy_score
from sparsity_network import SparsityNetwork
from weight_predictor_network import WeightPredictorNetwork
def get_labels_lists(outputs):
all_y_true... | 16,152 | 34.423246 | 179 | py |
outbrain-click-prediction-kaggle | outbrain-click-prediction-kaggle-master/5_best_mtv_features_xgb.py | import os
import pandas as pd
import numpy as np
import xgboost as xgb
df_all = feather.read_dataframe('tmp/clicks_train_50_50.feather')
df_test = feather.read_dataframe('tmp/clicks_test.feather')
df_train_0 = df_all[df_all.fold == 0].reset_index(drop=1)
df_train_1 = df_all[df_all.fold == 1].reset_index(drop=1)
del... | 3,479 | 27.52459 | 79 | py |
outbrain-click-prediction-kaggle | outbrain-click-prediction-kaggle-master/4_categorical_data_join.py | # coding: utf-8
import os
import pandas as pd
import numpy as np
import xgboost as xgb
import feather
from tqdm import tqdm
from sklearn.preprocessing import LabelEncoder
from itertools import combinations
df_all = feather.read_dataframe('tmp/clicks_train_50_50.feather')
df_test = feather.read_dataframe('tmp/cli... | 8,560 | 29.906137 | 112 | py |
outbrain-click-prediction-kaggle | outbrain-click-prediction-kaggle-master/7_ensemble_xgb.py | import pandas as pd
import numpy as np
import xgboost as xgb
import feather
import gc
# prapare the data matrices
df_train_0 = feather.read_dataframe('tmp/df_train_0_ensemble.feather')
ignore = {'display_id', 'ad_id', 'clicked', 'fold'}
columns = sorted(set(df_train_0.columns) - ignore)
group0_sizes = df_train_0.d... | 2,359 | 21.056075 | 76 | py |
outbrain-click-prediction-kaggle | outbrain-click-prediction-kaggle-master/5_mtv_xgb.py | import pandas as pd
import numpy as np
import xgboost as xgb
import feather
import gc
df_train_1 = feather.read_dataframe('tmp/mtv_df_train_1.feather')
features = sorted(set(df_train_1.columns) - {'display_id', 'clicked'})
y_1 = df_train_1.clicked.values
X_1 = df_train_1[features].values
del df_train_1
dfold1 = xgb... | 2,747 | 20.637795 | 72 | py |
BraVL | BraVL-master/BraVL_EEG/run_epochs_trimodal.py | import os
import numpy as np
import math
import random
import torch
from torch.autograd import Variable
import torch.distributions as dist
from tensorboardX import SummaryWriter
from torch.utils.data import DataLoader
from divergence_measures.kl_div import calc_kl_divergence
from sklearn.svm import SVC
from sklearn.met... | 42,106 | 42.231006 | 304 | py |
BraVL | BraVL-master/BraVL_EEG/main_trimodal.py | import sys
import os
os.environ['CUDA_VISIBLE_DEVICES'] = '5'
import json
import torch
from run_epochs_trimodal import run_epochs_trimodal
from utils.filehandling import create_dir_structure
from brain_image_text.flags import parser
from brain_image_text.experiment import BrainImageText
torch.set_default_tensor_type(to... | 1,562 | 33.733333 | 92 | py |
BraVL | BraVL-master/BraVL_EEG/modalities/Modality.py |
from abc import ABC, abstractmethod
import os
import torch
import torch.distributions as dist
class Modality(ABC):
def __init__(self, name, enc, dec, class_dim, style_dim, lhood_name):
self.name = name;
self.encoder = enc;
self.decoder = dec;
self.class_dim = class_dim;
se... | 1,414 | 27.877551 | 79 | py |
BraVL | BraVL-master/BraVL_EEG/brain_image_text/experiment.py | import os
import numpy as np
import itertools
import scipy.io as sio
import torch
import torch.optim as optim
from sklearn.metrics import accuracy_score
from sklearn.model_selection import train_test_split
from torch.utils.data import TensorDataset
from modalities.Modality import Modality
from brain_image_text.network... | 10,118 | 50.106061 | 158 | py |
BraVL | BraVL-master/BraVL_EEG/brain_image_text/networks/MLP_Text.py |
import torch
import torch.nn as nn
class EncoderText(nn.Module):
def __init__(self, flags):
super(EncoderText, self).__init__()
self.flags = flags;
self.hidden_dim = 256;
modules = []
modules.append(nn.Sequential(nn.Linear(flags.m3_dim, self.hidden_dim), nn.ReLU(True)))
... | 1,996 | 36.679245 | 108 | py |
BraVL | BraVL-master/BraVL_EEG/brain_image_text/networks/VAEtrimodal.py | import os
import torch
import torch.nn as nn
from utils import utils
from utils.BaseMMVae import BaseMMVae
class VAEtrimodal(BaseMMVae, nn.Module):
def __init__(self, flags, modalities, subsets):
super().__init__(flags, modalities, subsets)
class VAEbimodal(BaseMMVae, nn.Module):
def __init__(self,... | 406 | 19.35 | 52 | py |
BraVL | BraVL-master/BraVL_EEG/brain_image_text/networks/MLP_Image.py |
import torch
import torch.nn as nn
class EncoderImage(nn.Module):
def __init__(self, flags):
super(EncoderImage, self).__init__()
self.flags = flags;
self.hidden_dim = 256;
modules = []
modules.append(nn.Sequential(nn.Linear(flags.m2_dim, self.hidden_dim), nn.ReLU(True)))... | 1,980 | 37.096154 | 108 | py |
BraVL | BraVL-master/BraVL_EEG/brain_image_text/networks/QNET.py | import torch.nn as nn
import torch.nn.functional as F
import torch
class QNet(nn.Module):
def __init__(self, input_dim,latent_dim):
super(QNet, self).__init__()
self.fc1 = nn.Linear(input_dim,512)
self.fc21 = nn.Linear(512, latent_dim)
self.fc22 = nn.Linear(512, latent_dim)
def ... | 504 | 30.5625 | 46 | py |
BraVL | BraVL-master/BraVL_EEG/brain_image_text/networks/MLP_Brain.py |
import torch
import torch.nn as nn
class EncoderBrain(nn.Module):
def __init__(self, flags):
super(EncoderBrain, self).__init__()
self.flags = flags;
self.hidden_dim = 256;
modules = []
modules.append(nn.Sequential(nn.Linear(flags.m1_dim, self.hidden_dim), nn.ReLU(True)))
... | 2,001 | 36.074074 | 108 | py |
BraVL | BraVL-master/BraVL_EEG/divergence_measures/mm_div.py |
import torch
import torch.nn as nn
from divergence_measures.kl_div import calc_kl_divergence
from divergence_measures.kl_div import calc_kl_divergence_lb_gauss_mixture
from divergence_measures.kl_div import calc_kl_divergence_ub_gauss_mixture
from divergence_measures.kl_div import calc_entropy_gauss
from utils.utils... | 5,927 | 38 | 110 | py |
BraVL | BraVL-master/BraVL_EEG/divergence_measures/kl_div.py | import math
import torch
from utils.utils import reweight_weights
def calc_kl_divergence(mu0, logvar0, mu1=None, logvar1=None, norm_value=None):
if mu1 is None or logvar1 is None:
KLD = -0.5 * torch.sum(1 - logvar0.exp() - mu0.pow(2) + logvar0)
else:
KLD = -0.5 * (torch.sum(1 - logvar0.exp()/... | 4,561 | 40.099099 | 128 | py |
BraVL | BraVL-master/BraVL_EEG/utils/BaseMMVae.py | from abc import ABC, abstractmethod
import os
import torch
import torch.nn as nn
from torch.autograd import Variable
import torch.distributions as dist
from divergence_measures.mm_div import calc_alphaJSD_modalities
from divergence_measures.mm_div import calc_group_divergence_moe
from divergence_measures.mm_div impor... | 14,033 | 41.017964 | 121 | py |
BraVL | BraVL-master/BraVL_EEG/utils/utils.py | import os
import torch
# Print iterations progress
def printProgressBar (iteration, total, prefix = '', suffix = '', decimals = 1, length = 100, fill = '█'):
"""
Call in a loop to create terminal progress bar
@params:
iteration - Required : current iteration (Int)
total - Required ... | 4,100 | 32.892562 | 106 | py |
BraVL | BraVL-master/BraVL_EEG/utils/BaseFlags.py | import os
import argparse
import numpy as np
import torch
import scipy.io as sio
parser = argparse.ArgumentParser()
# TRAINING
parser.add_argument('--batch_size', type=int, default=1024, help="batch size for training")
parser.add_argument('--initial_learning_rate', type=float, default=0.0001, help="starting learning ... | 4,707 | 57.85 | 129 | py |
BraVL | BraVL-master/BraVL_fMRI/run_epochs_trimodal.py | import os
import numpy as np
import math
import random
import torch
from torch.autograd import Variable
import torch.distributions as dist
from tensorboardX import SummaryWriter
from torch.utils.data import DataLoader
from divergence_measures.kl_div import calc_kl_divergence
from sklearn.svm import SVC
from sklearn.met... | 40,510 | 43.12963 | 315 | py |
BraVL | BraVL-master/BraVL_fMRI/extract_fea_with_timm.py | import argparse
import os
from scipy import io
os.environ['CUDA_VISIBLE_DEVICES'] = '3'
import torch.nn.parallel
import torch.backends.cudnn as cudnn
import torch.optim
import torch.utils.data
import torch.utils.data.distributed
import torchvision.transforms as transforms
import torchvision.datasets as datasets
import ... | 7,451 | 35.529412 | 184 | py |
BraVL | BraVL-master/BraVL_fMRI/data_prepare_with_aug_DIR_Wiki.py | from __future__ import print_function
from itertools import product
import os
import pickle
import bdpy
from bdpy.dataform import Features
from bdpy.util import dump_info, makedir_ifnot
import numpy as np
from stability_selection import stability_selection
from sklearn.decomposition import PCA
from scipy import io
# S... | 20,190 | 40.375 | 169 | py |
BraVL | BraVL-master/BraVL_fMRI/data_prepare_with_aug_GOD_Wiki.py | from __future__ import print_function
from itertools import product
import os
import pickle
import bdpy
from bdpy.dataform import Features
from bdpy.util import dump_info, makedir_ifnot
import numpy as np
from sklearn.decomposition import PCA
from scipy import io
# Settings ############################################... | 18,971 | 41.066519 | 169 | py |
BraVL | BraVL-master/BraVL_fMRI/main_trimodal.py | import sys
import os
os.environ['CUDA_VISIBLE_DEVICES'] = '1'
import json
import torch
from run_epochs_trimodal import run_epochs_trimodal
from utils.filehandling import create_dir_structure
from brain_image_text.flags import parser
from brain_image_text.experiment import BrainImageText
torch.set_default_tensor_type(to... | 1,562 | 33.733333 | 92 | py |
BraVL | BraVL-master/BraVL_fMRI/modalities/Modality.py |
from abc import ABC, abstractmethod
import os
import torch
import torch.distributions as dist
class Modality(ABC):
def __init__(self, name, enc, dec, class_dim, style_dim, lhood_name):
self.name = name;
self.encoder = enc;
self.decoder = dec;
self.class_dim = class_dim;
se... | 1,414 | 27.877551 | 79 | py |
BraVL | BraVL-master/BraVL_fMRI/brain_image_text/experiment.py | import os
import numpy as np
import itertools
import scipy.io as sio
import torch
import torch.optim as optim
from sklearn.metrics import accuracy_score
from sklearn.model_selection import train_test_split
from torch.utils.data import TensorDataset
from modalities.Modality import Modality
from brain_image_text.network... | 10,591 | 50.417476 | 158 | py |
BraVL | BraVL-master/BraVL_fMRI/brain_image_text/networks/MLP_Text.py |
import torch
import torch.nn as nn
class EncoderText(nn.Module):
def __init__(self, flags):
super(EncoderText, self).__init__()
self.flags = flags;
self.hidden_dim = 512;
modules = []
modules.append(nn.Sequential(nn.Linear(flags.m3_dim, self.hidden_dim), nn.ReLU(True)))
... | 1,996 | 36.679245 | 108 | py |
BraVL | BraVL-master/BraVL_fMRI/brain_image_text/networks/VAEtrimodal.py | import os
import torch
import torch.nn as nn
from utils import utils
from utils.BaseMMVae import BaseMMVae
class VAEtrimodal(BaseMMVae, nn.Module):
def __init__(self, flags, modalities, subsets):
super().__init__(flags, modalities, subsets)
class VAEbimodal(BaseMMVae, nn.Module):
def __init__(self,... | 406 | 19.35 | 52 | py |
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