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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FIT | FIT-main/src/structure/nbp_transe.py |
import torch
from torch import nn
from .neural_binary_predicate import NeuralBinaryPredicate
class TransE(nn.Module, NeuralBinaryPredicate):
def __init__(self, num_entities, num_relations, embedding_dim, p, margin, scale, device, **kwargs):
super(TransE, self).__init__()
self.num_entities = num_e... | 2,716 | 39.552239 | 103 | py |
FIT | FIT-main/src/structure/nbp_swtranse.py |
import torch
from torch import nn
from .neural_binary_predicate import NeuralBinaryPredicate
class SWTransE(nn.Module, NeuralBinaryPredicate):
def __init__(self, num_entities, num_relations, embedding_dim, num_particles, p, margin, scale, device, **kwargs):
super(SWTransE, self).__init__()
self.... | 3,482 | 36.858696 | 118 | py |
FIT | FIT-main/src/structure/.ipynb_checkpoints/nbp_complex-checkpoint.py | import torch
from torch import nn
from .neural_binary_predicate import NeuralBinaryPredicate
class ComplEx(NeuralBinaryPredicate, nn.Module):
def __init__(self,
num_entities: int,
num_relations: int,
embedding_dim: int,
scale: float = 1,
... | 5,701 | 40.318841 | 114 | py |
FIT | FIT-main/src/structure/.ipynb_checkpoints/neural_binary_predicate-checkpoint.py | from abc import abstractmethod
import torch
class NeuralBinaryPredicate:
num_entities: int
num_relations: int
device: torch.device
scale: float
@abstractmethod
def embedding_score(self, head_emb, rel_emb, tail_emb):
"""
This method computes the score for the triple given the ... | 3,953 | 34.303571 | 114 | py |
FIT | FIT-main/src/utils/recorder.py | import json
import os
from typing import Dict
from torch.utils.tensorboard import SummaryWriter
class JSONlRecorder:
def __init__(self, jsonl_filename):
self.jsonl_filename = jsonl_filename
def append_record(self, data: Dict):
line_content = json.dumps(data)
with open(self.jsonl_file... | 1,351 | 27.765957 | 85 | py |
FIT | FIT-main/src/utils/aggregate_query.py | import argparse
import json
import os.path as osp
import torch
parser = argparse.ArgumentParser()
parser.add_argument("--mode", type=str, default='test', choices=['valid', 'test'])
parser.add_argument("--data_folder", type=str, default='data/FB15k-237-EFO1')
parser.add_argument("--max", type=int, default=1000)
if __... | 1,076 | 34.9 | 94 | py |
FIT | FIT-main/src/utils/data.py | import json
from random import shuffle
import torch
from torch.utils.data import DataLoader
from src.language.fof import ConjunctiveFormula, DisjunctiveFormula, Disjunction
from src.language.grammar import parse_lstr_to_lformula, parse_lstr_to_lformula_v2, concate_iu_chains, \
parse_lstr_to_disjunctive_formula
... | 9,973 | 32.582492 | 104 | py |
FIT | FIT-main/src/utils/data_util.py | from itertools import chain
from typing import Union, List
import torch
from torch.nn.utils.rnn import pad_sequence
def _iter_triple_from_tsv(triple_file, to_int, check_size):
with open(triple_file, 'rt') as f:
for line in f.readlines():
triple = line.strip().split()
if check_size... | 2,836 | 35.371795 | 78 | py |
FIT | FIT-main/src/utils/config.py | import argparse
import json
import os
from abc import abstractmethod
import torch
import yaml
from datetime import datetime
class Config:
default_kv = {}
def __init__(self, config_dict) -> None:
self.params = {}
for k in self.default_kv:
v = config_dict.pop(k, self.default_kv[k... | 7,684 | 30.756198 | 86 | py |
FIT | FIT-main/src/utils/class_util.py | import collections
import hashlib
from functools import partial
from itertools import repeat
import re
import warnings
import torch
import torch.nn as nn
import torch.nn.functional as F
import os
from os.path import join, dirname, realpath, exists
from shutil import rmtree
import time
import pickle
import json
import n... | 4,454 | 31.757353 | 106 | py |
FIT | FIT-main/src/learner/sampler.py | import torch
def lcwa_negative_sampling(phead_id_ten,
ptail_id_ten,
num_entities,
num_neg_samples):
device = phead_id_ten.device
original_shape = phead_id_ten.shape
neg_sample_shape = original_shape + (num_neg_samples,)
... | 1,206 | 30.763158 | 71 | py |
FIT | FIT-main/src/learner/elementary.py | from typing import List
import random
import torch
from .abstract_learner import Learner
from ..utils.data_util import tensorize_batch_entities
from ..structure import KnowledgeGraph, NeuralBinaryPredicate
class BatchedEFG:
def __init__(self,
finite_model: KnowledgeGraph,
neural... | 8,596 | 35.739316 | 88 | py |
FIT | FIT-main/src/learner/isomorphic.py | from torch.utils.data import DataLoader
from .abstract_learner import Learner, LearnerForwardOutput
from .sampler import lcwa_negative_sampling, rel_negative_sampling
from ..structure import KnowledgeGraph, NeuralBinaryPredicate
class IsomorphicLearner(Learner):
def __init__(self,
kg: KnowledgeG... | 1,737 | 31.792453 | 81 | py |
FIT | FIT-main/src/language/fof.py | """
A base class for existential first order formulas
It supports the verification and query answering tasks given the formula
For the verification, there is no free_vars
For the query answering, there should be at least one free_vars
Several assumptions about the formula
- In DNF
- Only with existential quantifier
The... | 31,017 | 37.531677 | 120 | py |
FIT | FIT-main/src/language/tnorm.py | from abc import abstractmethod
import torch
class Tnorm:
@classmethod
def negation(self, a):
return 1-a
class ProductTNorm(Tnorm):
@classmethod
def conjunction(self, a, b):
return a * b
@classmethod
def disjunction(self, a, b):
return a + b - a * b
class GodelTNorm(... | 491 | 16.571429 | 32 | py |
FIT | FIT-main/test/test_data_class.py | import argparse
import json
import logging
import os
import os.path as osp
import random
from collections import defaultdict
import numpy as np
import torch
import torch.nn.functional as F
import tqdm
from torch import nn
from src.language.tnorm import GodelTNorm, ProductTNorm, Tnorm
from src.structure import get_nbp... | 5,249 | 37.321168 | 222 | py |
FIT | FIT-main/data_preparation/filter_matrix.py | import argparse
import json
import logging
import os
import os.path as osp
import random
from collections import defaultdict
from typing import List
import torch
import pickle
from src.structure.knowledge_graph import KnowledgeGraph
from src.structure.knowledge_graph_index import KGIndex
parser = argparse.ArgumentPa... | 2,792 | 43.333333 | 116 | py |
FIT | FIT-main/data_preparation/sample_query.py | import argparse
import json
import logging
import os
import os.path as osp
import random
from collections import defaultdict
import numpy as np
import torch
import torch.nn.functional as F
import tqdm
from torch import nn
from src.language.tnorm import GodelTNorm, ProductTNorm, Tnorm
from src.structure import get_nbp... | 9,607 | 45.868293 | 119 | py |
rxngenerator | rxngenerator-master/main.py |
import rdkit
import rdkit.Chem as Chem
from rdkit.Chem import QED
from collections import deque
import torch
import torch.nn as nn
import torch.optim as optim
import torch.optim.lr_scheduler as lr_scheduler
from nnutils import create_var
import math
import torch.nn.functional as F
from torch.utils.data import DataLoad... | 13,438 | 31.305288 | 184 | py |
rxngenerator | rxngenerator-master/sample.py | import sys
sys.path.append('./rxnft_vae')
import torch
import torch.nn as nn
import torch.optim as optim
import torch.optim.lr_scheduler as lr_scheduler
from torch.utils.data import DataLoader
from torch.autograd import Variable
import math, random, sys
from optparse import OptionParser
from collections import deque
... | 3,441 | 32.096154 | 163 | py |
rxngenerator | rxngenerator-master/trainvae.py | import sys
sys.path.append('./rxnft_vae')
import torch
import torch.nn as nn
import torch.optim as optim
import torch.optim.lr_scheduler as lr_scheduler
from torch.utils.data import DataLoader
from torch.autograd import Variable
import math, random, sys
from optparse import OptionParser
from collections import deque
... | 7,932 | 35.389908 | 184 | py |
rxngenerator | rxngenerator-master/Theano-master/theano/tensor/nnet/abstract_conv.py | """
Abstract conv interface
"""
from __future__ import absolute_import, print_function, division
import logging
from six import reraise, integer_types
import sys
import theano
from theano.tensor import as_tensor_variable, patternbroadcast
from theano.tensor import get_scalar_constant_value, NotScalarConstantError
fr... | 46,519 | 40.98556 | 95 | py |
rxngenerator | rxngenerator-master/Theano-master/theano/tensor/nnet/__init__.py | from __future__ import absolute_import, print_function, division
from .nnet import (
CrossentropyCategorical1Hot, CrossentropyCategorical1HotGrad,
CrossentropySoftmax1HotWithBiasDx, CrossentropySoftmaxArgmax1HotWithBias,
LogSoftmax, Prepend_scalar_constant_to_each_row,
Prepend_scalar_to_each_row, Softma... | 6,330 | 42.965278 | 81 | py |
rxngenerator | rxngenerator-master/bo/run_bo.py | import sys
sys.path.append('../rxnft_vae')
import rdkit
import rdkit.Chem as Chem
from rdkit.Chem import QED, Descriptors, rdmolops
import torch
import torch.nn as nn
import torch.optim as optim
import torch.optim.lr_scheduler as lr_scheduler
from torch.utils.data import DataLoader
from torch.autograd import Variable
... | 10,018 | 31.423948 | 163 | py |
rxngenerator | rxngenerator-master/rxnft_vae/ftdecoder.py | import torch
import torch.nn as nn
from nnutils import create_var, GRU
from fragment import FragmentVocab, FragmentTree, FragmentNode
from chemutils import set_atommap, enum_assemble, enum_attach
import copy
MAX_NB = 16
MAX_DECODING_LEN = 100
class FTDecoder(nn.Module):
def __init__(self, ftvocab, hidden_size, late... | 10,999 | 31.448378 | 106 | py |
rxngenerator | rxngenerator-master/rxnft_vae/evaluate.py |
import rdkit
import rdkit.Chem as Chem
from rdkit.Chem import QED
from collections import deque
import torch
import torch.nn as nn
import torch.optim as optim
import torch.optim.lr_scheduler as lr_scheduler
from nnutils import create_var
import math
import torch.nn.functional as F
from torch.utils.data import DataLoad... | 6,342 | 29.495192 | 138 | py |
rxngenerator | rxngenerator-master/rxnft_vae/rxndecoder.py | import rdkit
import rdkit.Chem as Chem
from rdkit.Chem import Descriptors
from rdkit.Chem import MolFromSmiles, MolToSmiles
from rdkit.Chem import rdmolops
import torch
import torch.nn as nn
from nnutils import create_var, attention
import math
import torch.nn.functional as F
from rdkit.Chem import rdChemReactions
from... | 34,079 | 33.918033 | 170 | py |
rxngenerator | rxngenerator-master/rxnft_vae/nnutils.py | import torch
import torch.nn as nn
from torch.autograd import Variable
import torch.nn.functional as F
def create_var(tensor, requires_grad = None):
if requires_grad is None:
return Variable(tensor)#.cuda()
else:
return Variable(tensor, requires_grad=requires_grad)#.cuda()
def index_select_ND(source, dim, index)... | 1,636 | 32.408163 | 66 | py |
rxngenerator | rxngenerator-master/rxnft_vae/mpn.py | import torch
import torch.nn as nn
import rdkit.Chem as Chem
import torch.nn.functional as F
from reaction_utils import get_mol_from_smiles
from nnutils import *
ELEM_LIST = ['C', 'N', 'O', 'S' 'F', 'Si', 'P', 'Cl', 'Br', 'Mg', 'Na', 'Ca', 'Fe', 'Al', 'I', 'B', 'K', 'Se', 'Zn', 'H', 'Cu', 'Mn', 'unknown']
BTYPE_L... | 4,576 | 27.968354 | 144 | py |
rxngenerator | rxngenerator-master/rxnft_vae/trainvae.py | import torch
import torch.nn as nn
import torch.optim as optim
import torch.optim.lr_scheduler as lr_scheduler
from torch.utils.data import DataLoader
from torch.autograd import Variable
import math, random, sys
from optparse import OptionParser
from collections import deque
from reaction_utils import read_multistep_... | 8,329 | 34.751073 | 184 | py |
rxngenerator | rxngenerator-master/rxnft_vae/vae.py |
import torch
import torch.nn as nn
from nnutils import create_var, attention
from ftencoder import FTEncoder
from ftdecoder import FTDecoder
from rxndecoder import RXNDecoder, RXNDecoder1
from rxnencoder import RXNEncoder
from mpn import MPN,PP,Discriminator
def set_batch_nodeID(ft_trees, ft_vocab):
tot = 0
for ft... | 4,889 | 37.203125 | 176 | py |
rxngenerator | rxngenerator-master/rxnft_vae/rxnencoder.py | import rdkit
import rdkit.Chem as Chem
from reaction_utils import get_mol_from_smiles, get_smiles_from_mol,read_multistep_rxns, get_template_order
from reaction import ReactionTree, extract_starting_reactants, StartingReactants, Templates
from collections import deque
import torch
import torch.nn as nn
from nnutils imp... | 4,282 | 28.136054 | 109 | py |
rxngenerator | rxngenerator-master/rxnft_vae/ftencoder.py | import torch
import torch.nn as nn
from nnutils import create_var, GRU
from fragment import FragmentVocab, FragmentTree
from collections import deque
MAX_NB = 16
device = torch.device("cuda:0")
class FTEncoder(nn.Module):
def __init__(self, ftvocab, hidden_size, embedding=None):
super(FTEncoder, self).__init__(... | 3,860 | 26.776978 | 77 | py |
rxngenerator | rxngenerator-master/reaction_trees_creator/retro_star/api.py | import torch
import logging
import time
from retro_star.common import prepare_starting_molecules, prepare_mlp, \
prepare_molstar_planner, smiles_to_fp
from retro_star.model import ValueMLP
from retro_star.utils import setup_logger
import os
dirpath = os.path.dirname(os.path.abspath(__file__))
class RSPlanner:
... | 2,839 | 32.023256 | 90 | py |
rxngenerator | rxngenerator-master/reaction_trees_creator/retro_star/run_to_create_reaction_trees.py | import numpy as np
import torch
import random
import logging
import time
import pickle
import os
from retro_star.common import args, prepare_starting_molecules, prepare_mlp, \
prepare_molstar_planner, smiles_to_fp
from retro_star.model import ValueMLP
from retro_star.api import RSPlanner
import rdkit.Chem as Chem... | 1,946 | 26.422535 | 107 | py |
rxngenerator | rxngenerator-master/reaction_trees_creator/retro_star/data_loader/value_data_loader.py | import os
import numpy as np
import torch
import pickle
import logging
from torch.utils.data import Dataset, DataLoader
def unpack_fps(packed_fps):
# packed_fps = np.array(packed_fps)
shape = (*(packed_fps.shape[:-1]), -1)
fps = np.unpackbits(packed_fps.reshape((-1, packed_fps.shape[-1])),
... | 2,887 | 34.219512 | 79 | py |
rxngenerator | rxngenerator-master/reaction_trees_creator/retro_star/common/parse_args.py | import argparse
import os
import torch
import sys
parser = argparse.ArgumentParser()
# ===================== gpu id ===================== #
parser.add_argument('--gpu', type=int, default=-1)
# =================== random seed ================== #
parser.add_argument('--seed', type=int, default=1234)
# =============... | 2,262 | 38.017241 | 78 | py |
rxngenerator | rxngenerator-master/reaction_trees_creator/retro_star/packages/mlp_retrosyn/mlp_retrosyn/mlp_inference.py | from __future__ import print_function
import numpy as np
import torch
import torch.nn.functional as F
from rdkit import Chem
import rdchiral
from rdchiral.main import rdchiralRunText, rdchiralRun
from rdchiral.initialization import rdchiralReaction, rdchiralReactants
from .mlp_policies import load_parallel_model , prep... | 4,318 | 38.990741 | 109 | py |
rxngenerator | rxngenerator-master/reaction_trees_creator/retro_star/packages/mlp_retrosyn/mlp_retrosyn/mlp_policies.py | import os
import random
import numpy as np
from time import gmtime, strftime, localtime
import torch
import torch.nn as nn
import torch.optim as optim
import torch.nn.functional as F
from torch.utils.data import Dataset, DataLoader
from rdkit import Chem, DataStructs
from rdkit.Chem import AllChem
from tqdm import tqdm... | 14,193 | 36.254593 | 119 | py |
rxngenerator | rxngenerator-master/reaction_trees_creator/retro_star/model/value_mlp.py | import torch
import torch.nn as nn
import torch.nn.functional as F
import logging
class ValueMLP(nn.Module):
def __init__(self, n_layers, fp_dim, latent_dim, dropout_rate, device):
super(ValueMLP, self).__init__()
self.n_layers = n_layers
self.fp_dim = fp_dim
self.latent_dim = late... | 1,306 | 31.675 | 81 | py |
DMF | DMF-main/train.py | from utils.logger import setup_logger
from datasets import make_dataloader, make_dataloader_cross
from model.make_model import make_model_cross
from solver import make_optimizer
from solver.scheduler_factory import create_scheduler
from loss import make_loss
from processor import do_train, do_train_cross
import random
... | 3,120 | 29.90099 | 119 | py |
DMF | DMF-main/solver/lr_scheduler.py | # encoding: utf-8
"""
@author: liaoxingyu
@contact: sherlockliao01@gmail.com
"""
from bisect import bisect_right
import torch
# FIXME ideally this would be achieved with a CombinedLRScheduler,
# separating MultiStepLR with WarmupLR
# but the current LRScheduler design doesn't allow it
class WarmupMultiStepLR(torch.... | 1,862 | 31.684211 | 80 | py |
DMF | DMF-main/solver/cosine_lr.py | """ Cosine Scheduler
Cosine LR schedule with warmup, cycle/restarts, noise.
Hacked together by / Copyright 2020 Ross Wightman
"""
import logging
import math
import torch
from .scheduler import Scheduler
_logger = logging.getLogger(__name__)
class CosineLRScheduler(Scheduler):
"""
Cosine decay with restar... | 3,958 | 33.12931 | 121 | py |
DMF | DMF-main/solver/scheduler.py | from typing import Dict, Any
import torch
class Scheduler:
""" Parameter Scheduler Base Class
A scheduler base class that can be used to schedule any optimizer parameter groups.
Unlike the builtin PyTorch schedulers, this is intended to be consistently called
* At the END of each epoch, before incre... | 4,750 | 43.820755 | 112 | py |
DMF | DMF-main/solver/make_optimizer.py | import torch
def make_optimizer(cfg, model, center_criterion):
params = []
for key, value in model.named_parameters():
if not value.requires_grad:
continue
lr = cfg.SOLVER.BASE_LR
weight_decay = cfg.SOLVER.WEIGHT_DECAY
if "bias" in key:
lr = cfg.SOLVER.B... | 1,215 | 39.533333 | 106 | py |
DMF | DMF-main/processor/processor.py | import logging
import os
import time
import torch
import torch.nn as nn
from utils.meter import AverageMeter
from utils.metrics import R1_mAP_eval
from torch.cuda import amp
import torch.distributed as dist
def do_train(cfg,
model,
center_criterion,
train_loader,
val... | 12,172 | 40.264407 | 122 | py |
DMF | DMF-main/datasets/make_dataloader.py | import torch
import torchvision.transforms as T
from torch.utils.data import DataLoader
from .bases import ImageDataset, ImageDataset_cross
from timm.data.random_erasing import RandomErasing
from .sampler import RandomIdentitySampler
from .dukemtmcreid import DukeMTMCreID
from .market1501 import Market1501
from .msmt1... | 7,810 | 40.994624 | 123 | py |
DMF | DMF-main/datasets/sampler.py | from torch.utils.data.sampler import Sampler
from collections import defaultdict
import copy
import random
import numpy as np
class RandomIdentitySampler(Sampler):
"""
Randomly sample N identities, then for each identity,
randomly sample K instances, therefore batch size is N*K.
Args:
- data_source... | 2,433 | 34.794118 | 84 | py |
DMF | DMF-main/datasets/sampler_ddp.py | from torch.utils.data.sampler import Sampler
from collections import defaultdict
import copy
import random
import numpy as np
import math
import torch.distributed as dist
_LOCAL_PROCESS_GROUP = None
import torch
import pickle
def _get_global_gloo_group():
"""
Return a process group based on gloo backend, conta... | 7,051 | 34.616162 | 167 | py |
DMF | DMF-main/datasets/bases.py | from PIL import Image, ImageFile
from torch.utils.data import Dataset
import os.path as osp
import random
import torch
ImageFile.LOAD_TRUNCATED_IMAGES = True
import random
def read_image(img_path):
"""Keep reading image until succeed.
This can avoid IOError incurred by heavy IO process."""
got_img = False... | 3,650 | 33.443396 | 112 | py |
DMF | DMF-main/fairseq/setup.py | #!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import os
import subprocess
import sys
from setuptools import Extension, find_packages, setup
from torch.utils import ... | 7,589 | 28.648438 | 92 | py |
DMF | DMF-main/fairseq/hubconf.py | # Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
"""isort:skip_file"""
import functools
import importlib
dependencies = [
"dataclasses",
"hydra",
"numpy",
"omegaconf",
"... | 2,099 | 27.378378 | 82 | py |
DMF | DMF-main/fairseq/examples/truncated_bptt/transformer_xl_model.py | # Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import logging
from dataclasses import dataclass, field
from typing import Dict, List, Optional
import torch
from fairseq.dataclass import Fa... | 4,738 | 31.909722 | 84 | py |
DMF | DMF-main/fairseq/examples/truncated_bptt/truncated_bptt_lm_task.py | # Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import logging
import os
from dataclasses import dataclass, field
from typing import List, Optional, Tuple
import torch
from fairseq import u... | 9,995 | 33.951049 | 86 | py |
DMF | DMF-main/fairseq/examples/linformer/linformer_src/modules/multihead_linear_attention.py | # Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import math
from typing import Dict, Optional, Tuple
import torch
import torch.nn.functional as F
from fairseq import utils
from fairseq.incr... | 19,151 | 38.73444 | 98 | py |
DMF | DMF-main/fairseq/examples/linformer/linformer_src/modules/linformer_sentence_encoder.py | # Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import math
import torch.nn as nn
from fairseq.models.transformer import TransformerEncoder
from .linformer_sentence_encoder_layer import Li... | 2,151 | 38.127273 | 85 | py |
DMF | DMF-main/fairseq/examples/linformer/linformer_src/modules/linformer_sentence_encoder_layer.py | # Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import torch
from fairseq import utils
from fairseq.modules import TransformerEncoderLayer
from .multihead_linear_attention import MultiheadL... | 2,743 | 40.575758 | 85 | py |
DMF | DMF-main/fairseq/examples/linformer/linformer_src/models/linformer_roberta.py | # Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
"""
Linformer: Self-Attention with Linear Complexity
"""
import logging
import torch
from fairseq import utils
from fairseq.models import reg... | 4,143 | 33.247934 | 84 | py |
DMF | DMF-main/fairseq/examples/wav2vec/vq-wav2vec_featurize.py | #!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
"""
Helper script to pre-compute embeddings for a flashlight (previously called wav2letter++) dataset
"""
import argpa... | 7,680 | 29.601594 | 99 | py |
DMF | DMF-main/fairseq/examples/wav2vec/wav2vec_featurize.py | #!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
"""
Helper script to pre-compute embeddings for a flashlight (previously called wav2letter++) dataset
"""
import argpa... | 7,020 | 27.084 | 135 | py |
DMF | DMF-main/fairseq/examples/wav2vec/xlsr/scripts/gen_audio_embedding.py | """
Usage:
This script is used to extract the embedding / logit for speech classification task.
1. Set fdir into your model checkpoint directory
2. Run the following command (preferrably on GPU machine to speed up the inference process)
CUDA_VISIBLE_DEVICES=0 python3 examples/wav2vec/gen_audio_embeddin... | 9,209 | 40.300448 | 246 | py |
DMF | DMF-main/fairseq/examples/wav2vec/unsupervised/w2vu_generate.py | #!/usr/bin/env python3 -u
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
"""
Run inference for pre-processed data with a trained model.
"""
import ast
from collections import namedtuple
fr... | 22,454 | 30.405594 | 129 | py |
DMF | DMF-main/fairseq/examples/wav2vec/unsupervised/models/wav2vec_u.py | # Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
from dataclasses import dataclass
from enum import Enum, auto
import math
import numpy as np
from typing import Tuple, List, Optional, Dict
i... | 22,945 | 32.351744 | 87 | py |
DMF | DMF-main/fairseq/examples/wav2vec/unsupervised/scripts/wav2vec_apply_cluster_faiss.py | #!/usr/bin/env python3 -u
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import argparse
import os
import os.path as osp
import numpy as np
import tqdm
import torch
import sys
import faiss... | 4,015 | 30.131783 | 129 | py |
DMF | DMF-main/fairseq/examples/wav2vec/unsupervised/scripts/merge_clusters.py | #!/usr/bin/env python3 -u
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import argparse
import os
import os.path as osp
import numpy as np
import tqdm
import torch
import random
from shuti... | 3,543 | 29.817391 | 110 | py |
DMF | DMF-main/fairseq/examples/wav2vec/unsupervised/scripts/remove_silence.py | #!/usr/bin/env python3 -u
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
"""
get intervals from .vads file, specify output data, and this script removes silences and saves the audio data in... | 1,927 | 29.125 | 128 | py |
DMF | DMF-main/fairseq/examples/wav2vec/unsupervised/scripts/apply_pca.py | #!/usr/bin/env python3 -u
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import argparse
import os
import os.path as osp
import math
import numpy as np
import tqdm
import torch
from shutil ... | 2,496 | 31.428571 | 114 | py |
DMF | DMF-main/fairseq/examples/wav2vec/unsupervised/scripts/wav2vec_cluster_faiss.py | #!/usr/bin/env python3 -u
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import argparse
import gc
import os
import os.path as osp
import random
import numpy as np
import tqdm
import torch
... | 6,315 | 28.933649 | 129 | py |
DMF | DMF-main/fairseq/examples/wav2vec/unsupervised/scripts/mean_pool.py | #!/usr/bin/env python3 -u
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import argparse
import os
import os.path as osp
import math
import numpy as np
import tqdm
import torch
import torch... | 3,187 | 30.88 | 144 | py |
DMF | DMF-main/fairseq/examples/wav2vec/unsupervised/scripts/wav2vec_extract_features.py | #!/usr/bin/env python3 -u
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import argparse
import os
import os.path as osp
import tqdm
import torch
import torch.nn.functional as F
from shutil... | 3,673 | 29.616667 | 105 | py |
DMF | DMF-main/fairseq/examples/wav2vec/unsupervised/data/extracted_features_dataset.py | # Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import logging
import os
import contextlib
import numpy as np
import torch
from fairseq.data import FairseqDataset, data_utils
logger = l... | 5,038 | 28.994048 | 87 | py |
DMF | DMF-main/fairseq/examples/wav2vec/unsupervised/tasks/unpaired_audio_text.py | # Copyright (c) 2017-present, Facebook, Inc.
# All rights reserved.
#
# This source code is licensed under the license found in the LICENSE file in
# the root directory of this source tree. An additional grant of patent rights
# can be found in the PATENTS file in the same directory.
from dataclasses import dataclass,... | 15,658 | 33.567329 | 102 | py |
DMF | DMF-main/fairseq/examples/criss/save_encoder.py | #!/usr/bin/env python3 -u
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
"""
Translate pre-processed data with a trained model.
"""
import numpy as np
import torch
from fairseq import check... | 7,473 | 33.762791 | 90 | py |
DMF | DMF-main/fairseq/examples/speech_to_speech/generate_waveform_from_code.py | # Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import argparse
import json
import logging
from pathlib import Path
import random
import soundfile as sf
import torch
from tqdm import tqdm
... | 3,285 | 27.08547 | 107 | py |
DMF | DMF-main/fairseq/examples/speech_to_speech/benchmarking/core.py | import timeit
import logging
import torch
from pypapi import events, papi_high as high
from memory_profiler import memory_usage
from torch import nn
from argparse import Namespace
from fairseq.dataclass.utils import convert_namespace_to_omegaconf
from fairseq.data import data_utils as fairseq_data_utils
from fairseq im... | 17,782 | 35.440574 | 131 | py |
DMF | DMF-main/fairseq/examples/speech_to_speech/benchmarking/data_utils.py | from fairseq import tasks
import numpy as np
import logging
import random
from fairseq import options
import torch
import os
import soundfile as sf
from fairseq.data.audio.audio_utils import (
get_waveform,
parse_path,
)
logging.basicConfig()
logging.root.setLevel(logging.INFO)
logging.basicConfig(level=loggi... | 7,893 | 28.788679 | 127 | py |
DMF | DMF-main/fairseq/examples/speech_to_speech/benchmarking/get_metrics.py | import copy
import torch
import logging
from argparse import Namespace
import yaml
from fairseq import options
from examples.speech_to_speech.benchmarking.core import (
Processing,
SpeechGeneration,
Cascaded2StageS2ST,
Cascaded3StageS2ST,
S2UT,
)
from examples.speech_to_speech.benchmarking.data_util... | 5,053 | 30.006135 | 115 | py |
DMF | DMF-main/fairseq/examples/speech_to_speech/unity/sequence_generator.py | # Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import math
import sys
from typing import Dict, List, Optional
import torch
from torch import Tensor
from fairseq.sequence_generator import ... | 25,480 | 39.639553 | 107 | py |
DMF | DMF-main/fairseq/examples/speech_to_speech/unity/sequence_generator_multi_decoder.py | # Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
from typing import Dict, List, Optional
import torch
import torch.nn as nn
from torch import Tensor
from fairseq import search
class Multi... | 9,797 | 36.54023 | 95 | py |
DMF | DMF-main/fairseq/examples/speech_to_speech/preprocessing/prep_s2spect_data.py | #!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import argparse
import logging
import os
from pathlib import Path
import shutil
import torchaudio
import soundfile as ... | 5,844 | 33.382353 | 125 | py |
DMF | DMF-main/fairseq/examples/bart/summarize.py | # Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import torch
from fairseq.models.bart import BARTModel
import argparse
XSUM_KWARGS = dict(beam=6, lenpen=1.0, max_len_b=60, min_len=10, no_re... | 3,174 | 30.435644 | 88 | py |
DMF | DMF-main/fairseq/examples/data2vec/models/data2vec_audio.py | # Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import logging
import math
from dataclasses import dataclass, field
from typing import Optional
from omegaconf import II
import torch
import... | 17,916 | 32.302974 | 104 | py |
DMF | DMF-main/fairseq/examples/data2vec/models/data2vec_text.py | # Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
from dataclasses import dataclass, field
from typing import Optional
import logging
import math
import torch
import torch.nn as nn
import tor... | 18,697 | 35.096525 | 104 | py |
DMF | DMF-main/fairseq/examples/adaptive_span/adaptive_span_attention.py | # Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import math
import torch
import torch.nn as nn
import torch.nn.functional as F
class AdaptiveMask(nn.Module):
"""Soft masking function f... | 5,881 | 35.534161 | 85 | py |
DMF | DMF-main/fairseq/examples/adaptive_span/adagrad_with_grad_clip.py | # Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
from torch.optim import Adagrad
from fairseq.optim import LegacyFairseqOptimizer, register_optimizer
@register_optimizer("adagrad_with_grad... | 4,374 | 32.914729 | 92 | py |
DMF | DMF-main/fairseq/examples/adaptive_span/adaptive_span_model.py | # Copyright (c) Facebook, Inc. and its affiliates.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
import math
import torch
import torch.nn as nn
import torch.nn.functional as F
from fairseq.modules.layer_norm import Lay... | 8,540 | 31.352273 | 87 | py |
DMF | DMF-main/fairseq/examples/adaptive_span/adaptive_span_loss.py | # Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import math
from dataclasses import dataclass
import torch.nn.functional as F
from fairseq import metrics, utils
from fairseq.criterions impo... | 4,233 | 38.570093 | 88 | py |
DMF | DMF-main/fairseq/examples/adaptive_span/adaptive_span_model_wrapper.py | # Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import logging
from dataclasses import dataclass
from typing import Dict, List, Optional
import torch
from fairseq.dataclass import FairseqDa... | 4,692 | 31.143836 | 114 | py |
DMF | DMF-main/fairseq/examples/MMPT/setup.py | import setuptools
with open("README.md", "r") as fh:
long_description = fh.read()
setuptools.setup(
name="mmpt",
version="0.0.1",
author="Hu Xu, Po-yao Huang",
author_email="huxu@fb.com",
description="A package for multimodal pretraining.",
long_description=long_description,
long_descr... | 668 | 25.76 | 59 | py |
DMF | DMF-main/fairseq/examples/MMPT/mmpt_cli/predict.py | # Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import os
import glob
import argparse
import pprint
import omegaconf
from omegaconf import OmegaConf
from torch.utils.data import DataLoader
... | 3,937 | 33.54386 | 81 | py |
DMF | DMF-main/fairseq/examples/MMPT/mmpt/modules/mm.py | # coding=utf-8
# 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 License.
# You may obtain a cop... | 5,537 | 36.931507 | 83 | py |
DMF | DMF-main/fairseq/examples/MMPT/mmpt/modules/vectorpool.py | # Copyright (c) Facebook, Inc. All Rights Reserved
import torch
import os
import numpy as np
import pickle
from . import retri
from ..utils import get_local_rank
class VectorPool(object):
"""
Base class of retrieval space.
"""
def __init__(self, config):
from transformers import AutoConfig
... | 8,278 | 32.518219 | 82 | py |
DMF | DMF-main/fairseq/examples/MMPT/mmpt/models/transformermodel.py | # coding=utf-8
# 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 License.
# You may obtain a cop... | 26,064 | 34.462585 | 87 | py |
DMF | DMF-main/fairseq/examples/MMPT/mmpt/models/mmfusionnlg.py | # coding=utf-8
# Copyright 2018 The Google AI Language Team Authors, Facebook AI Research 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 L... | 48,394 | 47.395 | 246 | py |
DMF | DMF-main/fairseq/examples/MMPT/mmpt/models/mmfusion.py | # coding=utf-8
# 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 License.
# You may obtain a cop... | 30,634 | 32.047465 | 90 | py |
DMF | DMF-main/fairseq/examples/MMPT/mmpt/datasets/fairseqmmdataset.py | # Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
"""
TODO (huxu): fairseq wrapper class for all dataset you defined: mostly MMDataset.
"""
from collections import OrderedDict
from torch.util... | 1,785 | 29.793103 | 85 | py |
DMF | DMF-main/fairseq/examples/MMPT/mmpt/datasets/mmdataset.py | # Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import torch
from collections import OrderedDict
from torch.utils.data import Dataset
from torch.utils.data.dataloader import default_collat... | 3,873 | 33.589286 | 76 | py |
DMF | DMF-main/fairseq/examples/MMPT/mmpt/evaluators/predictor.py | # Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import os
import random
import json
import numpy as np
import torch
import pickle
import math
from tqdm import tqdm
class Predictor(object):... | 23,125 | 37.802013 | 113 | py |
DMF | DMF-main/fairseq/examples/MMPT/mmpt/processors/how2processor.py | # coding=utf-8
# 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 License.
# You may obtain a cop... | 32,302 | 35.377252 | 88 | py |
DMF | DMF-main/fairseq/examples/MMPT/mmpt/processors/processor.py | # Copyright (c) Facebook, Inc. All Rights Reserved
import numpy as np
import os
import torch
class Processor(object):
"""
A generic processor for video (codec, feature etc.) and text.
"""
def __call__(self, **kwargs):
raise NotImplementedError
class MetaProcessor(Processor):
"""
A ... | 9,358 | 33.032727 | 86 | py |
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