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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HQM | HQM-main/CDN/models/cdn.py | import copy
from typing import Optional, List
import torch
import torch.nn.functional as F
from torch import nn, Tensor
class CDN(nn.Module):
def __init__(self, d_model=512, nhead=8, num_encoder_layers=6,
num_dec_layers_hopd=3, num_dec_layers_interaction=3,
dim_feedforward=204... | 12,724 | 40.721311 | 134 | py |
HQM | HQM-main/CDN/models/DN/cdn_doq.py | import copy
from typing import Optional, List
import torch
import torch.nn.functional as F
from torch import nn, Tensor
class CDN(nn.Module):
def __init__(self, d_model=512, nhead=8, num_encoder_layers=6,
num_dec_layers_hopd=3, num_dec_layers_interaction=3,
dim_feedforward=2048... | 13,186 | 40.468553 | 142 | py |
HQM | HQM-main/CDN/models/DN/hoi_hqm.py | import torch
from torch import nn
import torch.nn.functional as F
from util.box_ops import box_cxcywh_to_xyxy, generalized_box_iou
from util.misc import (NestedTensor, nested_tensor_from_tensor_list,
accuracy, get_world_size, is_dist_avail_and_initialized)
import numpy as np
from queue import Qu... | 26,055 | 44.792619 | 120 | py |
HQM | HQM-main/CDN/models/DN/doq_components.py | import copy
import torch
from util.box_ops import box_cxcywh_to_xyxy, generalized_box_iou
from util.misc import (NestedTensor, nested_tensor_from_tensor_list,
accuracy, get_world_size, is_dist_avail_and_initialized)
import torch.nn.functional as F
from torchvision.ops.boxes import box_area
def ... | 16,447 | 41.611399 | 245 | py |
HQM | HQM-main/CDN/models/DN/hoi_doq.py | import torch
from torch import nn
import torch.nn.functional as F
from util.box_ops import box_cxcywh_to_xyxy, generalized_box_iou
from util.misc import (NestedTensor, nested_tensor_from_tensor_list,
accuracy, get_world_size, is_dist_avail_and_initialized)
import numpy as np
from queue import Qu... | 26,004 | 44.864198 | 120 | py |
HQM | HQM-main/CDN/models/DN/hoi_hqm_w.py | import torch
from torch import nn
import torch.nn.functional as F
from util.box_ops import box_cxcywh_to_xyxy, generalized_box_iou
from util.misc import (NestedTensor, nested_tensor_from_tensor_list,
accuracy, get_world_size, is_dist_avail_and_initialized)
import numpy as np
from queue import Qu... | 26,177 | 44.845884 | 120 | py |
HQM | HQM-main/CDN/util/misc.py | import os
import subprocess
import time
from collections import defaultdict, deque
import datetime
import pickle
from typing import Optional, List
import torch
import torch.distributed as dist
from torch import Tensor
import torchvision
if float(torchvision.__version__[:3]) < 0.7:
from torchvision.ops import _new... | 11,843 | 29.924282 | 95 | py |
HQM | HQM-main/CDN/util/box_ops.py | import torch
from torchvision.ops.boxes import box_area
def box_cxcywh_to_xyxy(x):
x_c, y_c, w, h = x.unbind(-1)
b = [(x_c - 0.5 * w), (y_c - 0.5 * h),
(x_c + 0.5 * w), (y_c + 0.5 * h)]
return torch.stack(b, dim=-1)
def box_xyxy_to_cxcywh(x):
x0, y0, x1, y1 = x.unbind(-1)
b = [(x0 + x1) ... | 1,858 | 26.746269 | 74 | py |
HQM | HQM-main/CDN/datasets/hico.py | from pathlib import Path
from PIL import Image
import json
from collections import defaultdict
import numpy as np
import torch
import torch.utils.data
import torchvision
import datasets.transforms as T
class HICODetection(torch.utils.data.Dataset):
def __init__(self, img_set, img_folder, anno_file, transforms,... | 8,682 | 40.151659 | 125 | py |
HQM | HQM-main/CDN/datasets/__init__.py | import torch.utils.data
import torchvision
from .hico import build as build_hico
from .vcoco import build as build_vcoco
def build_dataset(image_set, args):
if args.dataset_file == 'hico':
return build_hico(image_set, args)
if args.dataset_file == 'vcoco':
return build_vcoco(image_set, args)
... | 386 | 28.769231 | 66 | py |
HQM | HQM-main/CDN/datasets/vcoco.py | from pathlib import Path
from PIL import Image
import json
import numpy as np
import torch
import torch.utils.data
import torchvision
import datasets.transforms as T
class VCOCO(torch.utils.data.Dataset):
def __init__(self, img_set, img_folder, anno_file, transforms, num_queries):
self.img_set = img_set... | 7,829 | 40.871658 | 123 | py |
HQM | HQM-main/CDN/datasets/transforms.py | import random
import PIL
import torch
import torchvision.transforms as T
import torchvision.transforms.functional as F
from util.box_ops import box_xyxy_to_cxcywh
from util.misc import interpolate
def crop(image, target, region):
cropped_image = F.crop(image, *region)
target = target.copy()
i, j, h, w ... | 8,022 | 28.936567 | 104 | py |
NoisyNet-A3C | NoisyNet-A3C-master/main.py | # -*- coding: utf-8 -*-
import argparse
import os
import gym
import torch
from torch import multiprocessing as mp
from model import ActorCritic
from optim import SharedRMSprop
from train import train
from test import test
from utils import Counter
parser = argparse.ArgumentParser(description='NoisyNet-A3C')
parser.a... | 3,901 | 47.775 | 155 | py |
NoisyNet-A3C | NoisyNet-A3C-master/test.py | # -*- coding: utf-8 -*-
import time
from datetime import datetime
import gym
import torch
from torch.autograd import Variable
from model import ActorCritic
from utils import state_to_tensor, plot_line
def test(rank, args, T, shared_model):
torch.manual_seed(args.seed + rank)
env = gym.make(args.env)
env.seed(... | 3,831 | 36.940594 | 134 | py |
NoisyNet-A3C | NoisyNet-A3C-master/optim.py | # -*- coding: utf-8 -*-
from torch import optim
# Non-centered RMSprop update with shared statistics (without momentum)
class SharedRMSprop(optim.RMSprop):
def __init__(self, params, lr=1e-2, alpha=0.99, eps=1e-8, weight_decay=0):
super(SharedRMSprop, self).__init__(params, lr=lr, alpha=alpha, eps=eps, weight_d... | 1,607 | 30.529412 | 131 | py |
NoisyNet-A3C | NoisyNet-A3C-master/utils.py | # -*- coding: utf-8 -*-
import plotly
from plotly.graph_objs import Scatter, Line
import torch
from torch import multiprocessing as mp
# Global counter
class Counter():
def __init__(self):
self.val = mp.Value('i', 0)
self.lock = mp.Lock()
def increment(self):
with self.lock:
self.val.value += 1... | 1,886 | 34.603774 | 158 | py |
NoisyNet-A3C | NoisyNet-A3C-master/model.py | # -*- coding: utf-8 -*-
import math
import torch
from torch import nn
from torch.nn import init, Parameter
from torch.nn import functional as F
from torch.autograd import Variable
# Noisy linear layer with independent Gaussian noise
class NoisyLinear(nn.Linear):
def __init__(self, in_features, out_features, sigma_i... | 3,098 | 39.246753 | 150 | py |
NoisyNet-A3C | NoisyNet-A3C-master/train.py | # -*- coding: utf-8 -*-
import gym
import torch
from torch import nn
from torch.autograd import Variable
from model import ActorCritic
from utils import state_to_tensor
# Transfers gradients from thread-specific model to shared model
def _transfer_grads_to_shared_model(model, shared_model):
for param, shared_param... | 4,318 | 34.694215 | 120 | py |
FewSum | FewSum-master/fewsum/workflow.py | from fewsum.config.run import *
from fewsum.config.model_hp import *
from fewsum.modelling.models import *
from fewsum.data_pipelines.assemblers import assemble_unsup_pipeline, \
assemble_vocab_pipeline, assemble_eval_pipeline, assemble_tuning_pipeline
from mltoolkit.mldp.utils.tools import Vocabulary
from mltoolki... | 12,984 | 46.915129 | 103 | py |
FewSum | FewSum-master/fewsum/tests/test_beam_decoder.py | import unittest
import torch as T
from fewsum.modelling.generators import Beamer
from mltoolkit.mlmo.utils.tools import DecState
import numpy as np
class TestBeamDecoder(unittest.TestCase):
"""
Note that this test ignores the fact that word scores should be log
probabilities.
"""
def test_simple_... | 4,351 | 29.647887 | 77 | py |
FewSum | FewSum-master/fewsum/tests/test_modelling_funcs.py | import unittest
import torch as T
import numpy as np
from fewsum.utils.helpers.modelling import group_att_over_input,\
optimize_att_tens
from fewsum.utils.helpers.data import get_all_but_one_rev_indxs, \
create_optimized_selector
class TestModellingFuncs(unittest.TestCase):
def test_get_all_but_one_rev_i... | 6,002 | 45.534884 | 80 | py |
FewSum | FewSum-master/fewsum/tests/test_helpers.py | import unittest
from fewsum.utils.helpers.computation import comp_cov_cmass
import torch as T
class TestHelpers(unittest.TestCase):
def test_comp_cov_cmas(self):
""""""
probs = T.tensor([
[
[0.5, 0.2, 0.1, 0.05, 0.05],
[0.8, 0.1, 0.05, 0.025, 0.025],
... | 1,043 | 24.463415 | 59 | py |
FewSum | FewSum-master/fewsum/utils/tools/beam_search.py | import torch as T
import numpy as np
from mltoolkit.mlmo.utils.helpers.search import traverse_table
from fewsum.utils.helpers.search import find_mirror_next
EXCL_EPS = -1e20
class BeamSearch(object):
"""Wrapper over ONMT beam search that works over batches."""
def __init__(self, batch_size, beam_size, start... | 14,540 | 39.504178 | 93 | py |
FewSum | FewSum-master/fewsum/utils/helpers/modelling.py | import torch as T
from torch import Tensor
import numpy as np
def optimize_att_tens(mask, *tens):
"""
Optimizes concatenated seqs for attention by removing non-zero ones,
and re-padding.
TODO: optimize it to avoid the for-loop!
:param tens: list of [batch_size, seq_len, dim]
:param mask: [bat... | 3,167 | 34.595506 | 79 | py |
FewSum | FewSum-master/fewsum/utils/helpers/computation.py | import torch as T
def comp_cov_cmass(log_probs, words, words_mask):
"""Computes cumulative probability mass that is assigned to `words`.
Args:
log_probs: [batch_size, seq_len, vocab_size]
words: [batch_size, word_count]
words_mask: [batch_size, word_count]
Return:
prob_cm... | 1,634 | 27.684211 | 80 | py |
FewSum | FewSum-master/fewsum/utils/helpers/registries.py | from torch.nn.modules import Module
from mltoolkit.mlmo.utils.tools import BaseConfig
from functools import partial
MODEL_REGISTRY = {}
HP_CONFIG_REGISTRY = {}
RUN_CONFIG_REGISTRY = {}
def _register(cls, name, registry, basecls=None):
if name in registry:
raise ValueError("Cannot register duplicate ({})"... | 1,305 | 29.372093 | 80 | py |
FewSum | FewSum-master/fewsum/modelling/modules/transformer_embeddings.py | from torch.nn import Module, Embedding, Dropout
from .positional_encoding import PositionalEncoding
import math
import torch as T
class TransformerEmbeddings(Module):
"""Performs projection to the embedding space and selection;
Embedding of sequences (embed method):
- The actual embedding of sequen... | 3,217 | 34.755556 | 80 | py |
FewSum | FewSum-master/fewsum/modelling/modules/plugin.py | from fewsum.modelling.modules.transformer_stack import MEM_ATT_WTS, TransformerStack
from fewsum.utils.fields import ModelF
from torch.nn import Module, Sequential, Linear, Sigmoid, Softmax, ModuleDict
import torch as T
class Plugin(Module):
"""Module used to compute prop values that are passed to the decoder."""... | 2,380 | 43.924528 | 84 | py |
FewSum | FewSum-master/fewsum/modelling/modules/transformer_decoder_layer.py | import torch as T
import copy
from torch.nn import functional as F
from torch.nn import Module, Sequential
from torch.nn.modules.activation import MultiheadAttention, ReLU
from torch.nn.modules.dropout import Dropout
from torch.nn.modules.linear import Linear
from torch.nn.modules.normalization import LayerNorm
class... | 4,548 | 42.740385 | 86 | py |
FewSum | FewSum-master/fewsum/modelling/modules/positional_encoding.py | import torch
import math
class PositionalEncoding(torch.nn.Module):
"""
Returns embeddings of words which also contains the position (time)
component.
"""
def __init__(self, emb_dim, max_len=5000):
super(PositionalEncoding, self).__init__()
# initializing the lookup table
... | 1,066 | 30.382353 | 75 | py |
FewSum | FewSum-master/fewsum/modelling/modules/transformer_stack.py | import torch as T
from torch.nn.modules.transformer import _get_clones, LayerNorm
from fewsum.modelling.modules import TransformerDecoderLayer
from torch.nn import Module, Sequential, Linear
MEM_ATT_WTS = 'mem_att_wts'
class TransformerStack(Module):
"""This version of Transformer acts both like an encoder and ... | 5,008 | 40.741667 | 83 | py |
FewSum | FewSum-master/fewsum/modelling/generators/beamer.py | import torch as T
from torch import Tensor
from mltoolkit.mlmo.utils.helpers.pytorch.data import adjust_tensor_to_beam_size
from mltoolkit.mlutils.helpers.general import merge_dicts
from mltoolkit.mlmo.utils.tools import DecState
from mltoolkit.mlmo.utils.helpers.general import collect_arts
from fewsum.utils.tools impo... | 8,748 | 43.411168 | 83 | py |
FewSum | FewSum-master/fewsum/modelling/models/basesum.py | import torch as T
from mltoolkit.mlmo.utils.helpers.pytorch.computation import seq_log_prob, perpl_per_word
from torch.nn import Module, Sequential, Linear, ReLU
from torch.nn.functional import log_softmax
from collections import OrderedDict
from mltoolkit.mlmo.utils.tools import DecState
from fewsum.modelling.modules ... | 8,953 | 43.994975 | 98 | py |
FewSum | FewSum-master/fewsum/modelling/models/fewsum.py | from fewsum.modelling.models import PluginNetwork
from mltoolkit.mlmo.utils.helpers.pytorch.computation import seq_log_prob, perpl_per_word
from collections import OrderedDict
from fewsum.utils.helpers.registries import register_model
from fewsum.utils.constants import JOINT_TUNING
@register_model(JOINT_TUNING)
class... | 1,809 | 35.2 | 89 | py |
FewSum | FewSum-master/fewsum/modelling/models/nov_red.py | from .basesum import BaseSum
from collections import OrderedDict
from mltoolkit.mlmo.utils.helpers.pytorch.computation import seq_log_prob, perpl_per_word
from fewsum.utils.helpers.computation import comp_cov_cmass
from fewsum.utils.helpers.registries import register_model
from fewsum.utils.constants import NOV_RED
@... | 2,915 | 38.945205 | 93 | py |
FewSum | FewSum-master/fewsum/modelling/models/plugin_network.py | from fewsum.modelling.models.basesum import BaseSum
from fewsum.utils.fields import ModelF
from mltoolkit.mlmo.utils.helpers.pytorch.computation import kld_cat
from fewsum.modelling.modules import Plugin
from fewsum.utils.helpers.registries import register_model
from fewsum.utils.constants import PLUGIN_INIT, PLUGIN_TU... | 4,573 | 43.407767 | 93 | py |
FewSum | FewSum-master/fewsum/modelling/interfaces/i_dev_summ.py | from mltoolkit.mlutils.helpers.formatting.general import format_big_box, stats_to_str
from mltoolkit.mldp.utils.tools import DataChunk
from fewsum.utils.fields import ModelF, OutDataF
from mltoolkit.mlmo.interfaces import IBaseDevTorch
import codecs
from logging import getLogger
import os
from mltoolkit.mlutils.helpers... | 14,577 | 41.501458 | 85 | py |
FewSum | FewSum-master/fewsum/modelling/interfaces/i_summ.py | from mltoolkit.mlmo.interfaces import ITorchModel
from logging import getLogger
import torch as T
from mltoolkit.mlmo.utils.tools import DecState
from fewsum.utils.fields import ModelF
logger = getLogger(__name__)
class ISumm(ITorchModel):
def __init__(self, gen_func, **kwargs):
super(ISumm, self).__init... | 2,981 | 38.76 | 87 | py |
FewSum | FewSum-master/mltoolkit/mldp/pytorch_pipeline.py | from mltoolkit.mldp import Pipeline
from mltoolkit.mldp.steps.formatters import PyTorchFormatter
class PyTorchPipeline(Pipeline):
"""
Addresses the issue associated with processes that put PyTorch tensors to a
Queue object must be alive when the main processes gets/requests them from
the Queue. Format... | 935 | 39.695652 | 100 | py |
FewSum | FewSum-master/mltoolkit/mldp/__init__.py | from .pipeline import Pipeline
from .pytorch_pipeline import PyTorchPipeline
| 77 | 25 | 45 | py |
FewSum | FewSum-master/mltoolkit/mldp/tutorials/model/senti_lstm.py | import numpy as np
from keras.models import Sequential
from keras.layers import Activation, Dense, Embedding, Input, Masking, Lambda, LSTM
np.random.seed(41)
class SentiLSTM(object):
"""Keras implementation of an LSTM twitter sentiment classifier."""
def __init__(self, words_vocab_size, input_dim, lstm_hidde... | 1,327 | 35.888889 | 83 | py |
FewSum | FewSum-master/mltoolkit/mldp/steps/formatters/pytorch_formatter.py | from mltoolkit.mldp.steps.formatters import BaseFormatter
import numpy as np
import torch as T
class PyTorchFormatter(BaseFormatter):
"""Converts numpy arrays of integers and floats to PyTorch tensors."""
def _format(self, data_chunk):
allowed_types = [np.int32, np.int64, np.float32, np.float64]
... | 950 | 32.964286 | 79 | py |
FewSum | FewSum-master/mltoolkit/mldp/steps/formatters/__init__.py | from .base_formatter import BaseFormatter
from .pandas_formatter import PandasFormatter
from .pytorch_formatter import PyTorchFormatter
| 136 | 33.25 | 47 | py |
FewSum | FewSum-master/mltoolkit/mlmo/scripts/rename_params_by_prefix.py | import argparse
import torch as T
from mltoolkit.mlutils.helpers.paths_and_files import safe_mkfdir
from mltoolkit.mlmo.utils.constants.checkpoint import MODEL_PARAMS
from mltoolkit.mlutils.helpers.argparse import str2list
def rename_params_by_prefix(input_fp, output_fp, old_prefixes, new_prefixes):
"""Renames a ... | 1,465 | 39.722222 | 77 | py |
FewSum | FewSum-master/mltoolkit/mlmo/scripts/rename_params.py | import argparse
import torch as T
from mltoolkit.mlutils.helpers.paths_and_files import safe_mkfdir
from mltoolkit.mlmo.utils.constants.checkpoint import MODEL_PARAMS
from mltoolkit.mlutils.helpers.argparse import str2list, str2bool
def rename_params(input_fp, output_fp, old_attr_names, new_attr_names):
"""Rename... | 1,099 | 38.285714 | 71 | py |
FewSum | FewSum-master/mltoolkit/mlmo/scripts/extract_params.py | import argparse
import torch as T
from mltoolkit.mlutils.helpers.paths_and_files import safe_mkfdir
from mltoolkit.mlmo.utils.constants.checkpoint import MODEL_PARAMS
def extract_params(input_fp, output_fp, attr_names, device='cpu'):
"""Extract a subset parameters from the file. Saves to a new file."""
model_... | 876 | 31.481481 | 73 | py |
FewSum | FewSum-master/mltoolkit/mlmo/scripts/delete_attrs_from_params.py | import argparse
import torch as T
from mltoolkit.mlutils.helpers.paths_and_files import safe_mkfdir
from mltoolkit.mlutils.helpers.logging_funcs import init_logger
from mltoolkit.mlmo.utils.constants.checkpoint import MODEL_PARAMS
from mltoolkit.mlutils.helpers.argparse import str2list, str2bool
import re
logger = ini... | 1,795 | 34.215686 | 80 | py |
FewSum | FewSum-master/mltoolkit/mlmo/utils/tools/beam_search.py | import torch as T
import numpy as np
from mltoolkit.mlmo.utils.helpers.search import traverse_table
class BeamSearch(object):
"""Wrapper over ONMT beam search that works over batches."""
def __init__(self, batch_size, beam_size, start_id, end_id, device='cpu',
min_lens=None, sample=False, le... | 9,048 | 38.00431 | 82 | py |
FewSum | FewSum-master/mltoolkit/mlmo/utils/tools/module_parallel.py | import torch as T
from torch.nn import DataParallel, Module
from torch.nn.parallel.scatter_gather import scatter_kwargs, gather
from torch.nn.parallel.parallel_apply import parallel_apply
from torch.nn.parallel.data_parallel import _check_balance
from torch.nn.parallel.replicate import replicate
from torch import Tenso... | 10,840 | 40.064394 | 88 | py |
FewSum | FewSum-master/mltoolkit/mlmo/utils/helpers/pytorch/computation.py | # contains computational functions that are used for modelling
import torch as T
from torch.autograd import Variable
from torch import Tensor
from torch.nn.functional import softmax
import numpy as np
def masked_softmax(scores, mask=None, dim=-1):
"""
Normalizes scores using masked softmax operation.
:pa... | 6,037 | 31.28877 | 80 | py |
FewSum | FewSum-master/mltoolkit/mlmo/utils/helpers/pytorch/data.py | # contain data related modelling functions, mostly in PyTorch
import torch as T
from torch.autograd import Variable
import numpy as np
def create_mask_from_lens(lens):
mask = T.zeros((lens.size(0), lens.max()), dtype=T.float32,
device=lens.device)
for indx, l in enumerate(lens):
mas... | 3,217 | 35.988506 | 94 | py |
FewSum | FewSum-master/mltoolkit/mlmo/utils/helpers/pytorch/generation.py | import torch as T
def top_k_filtering(logits, top_k=0, filter_value=-float('Inf')):
"""Filters a distribution of logits using top-k filtering.
Args:
logits: predicted word logits.
[batch_size, *, vocab_size]
top_k >0: keep only top k tokens with highest probability.
(t... | 667 | 30.809524 | 84 | py |
FewSum | FewSum-master/mltoolkit/mlmo/utils/helpers/pytorch/general.py | from torch.nn.parallel import DataParallel
import numpy as np
def parallelize_submodules(parent_module):
"""Wraps a parent's sub-modules with a DataParallel object that permits multi
GPU usage.
"""
for mod_name, module in parent_module.named_children():
setattr(parent_module, mod_name, DataPar... | 3,422 | 34.65625 | 81 | py |
FewSum | FewSum-master/mltoolkit/mlmo/utils/helpers/pytorch/debug.py | import torch as T
from torch.nn import Parameter
import gc
import resource
def get_alive_tensors():
"""Returns tuples (type, size)."""
params = []
tensors = []
for obj in gc.get_objects():
if T.is_tensor(obj) or (hasattr(obj, 'data') and
T.is_tensor(obj.data... | 762 | 27.259259 | 69 | py |
FewSum | FewSum-master/mltoolkit/mlmo/interfaces/i_torch_model.py | from mltoolkit.mlmo.utils.constants.checkpoint import MODEL_PARAMS, OPTIMIZER_STATE
from mltoolkit.mlmo.interfaces import IBaseModel
from torch.nn import Module
from mltoolkit.mlmo.utils.helpers.loading_and_saving import load_embeddings
from mltoolkit.mlmo.utils.tools import ModuleParallel
from torch.optim import Adam
... | 5,783 | 41.529412 | 83 | py |
FewSum | FewSum-master/mltoolkit/mlmo/interfaces/__init__.py | from .i_base_dev import IBaseDev
from .i_base_model import IBaseModel
from .i_torch_model import ITorchModel
from .i_base_dev_torch import IBaseDevTorch
| 153 | 29.8 | 43 | py |
FewSum | FewSum-master/mltoolkit/mlmo/interfaces/i_base_dev_torch.py | from . import IBaseDev
from torch.cuda import empty_cache
class IBaseDevTorch(IBaseDev):
"""PyTorch specific base interface for development."""
def train(self, *args, **kwargs):
empty_cache()
return super(IBaseDevTorch, self).train(*args, **kwargs)
def eval(self, *args, **kwargs):
... | 404 | 26 | 64 | py |
FewSum | FewSum-master/mltoolkit/mlutils/helpers/formatting/seq.py | import numpy as np
import torch as T
def conv_seqs_to_sents(seqs, excl_tokens=None, end_token='.'):
"""
Converts sequences of tokens into a list of sentences (strings). Does not use
a proper de-tokenizer, but instead just joins the tokens.
:param seqs: list of lists (tokens).
:param excl_tokens: ... | 2,569 | 31.531646 | 81 | py |
nuts-flow | nuts-flow-master/nutsflow/function.py | """
.. module:: function
:synopsis: Nuts that perform functions on single stream elements.
"""
from __future__ import print_function
from __future__ import absolute_import
import time
import threading
from nutsflow.common import (shapestr, as_tuple, is_iterable, istensor,
print_type, c... | 14,149 | 27.643725 | 80 | py |
FARM | FARM-master/tutorials/sagemaker/source/doc_classification.py | import argparse
import logging
from pathlib import Path
from farm.data_handler.data_silo import DataSilo
from farm.data_handler.processor import TextClassificationProcessor
from farm.modeling.adaptive_model import AdaptiveModel
from farm.modeling.language_model import LanguageModel
from farm.modeling.optimization impo... | 4,063 | 34.964602 | 133 | py |
FARM | FARM-master/examples/doc_classification_cola.py | # fmt: off
import logging
from pathlib import Path
from farm.data_handler.data_silo import DataSilo
from farm.data_handler.processor import TextClassificationProcessor
from farm.modeling.optimization import initialize_optimizer
from farm.infer import Inferencer
from farm.modeling.adaptive_model import AdaptiveModel
fr... | 4,458 | 36.158333 | 162 | py |
FARM | FARM-master/examples/train_from_scratch.py | import argparse
import logging
from pathlib import Path
from farm.modeling.tokenization import Tokenizer
from farm.data_handler.data_silo import StreamingDataSilo, DataSilo
from farm.data_handler.processor import BertStyleLMProcessor
from farm.data_handler.utils import split_file
from farm.modeling.adaptive_model impo... | 6,173 | 38.075949 | 114 | py |
FARM | FARM-master/examples/doc_classification_holdout.py | # fmt: off
import logging
import numbers
import json
import statistics
import mlflow
from pathlib import Path
from collections import defaultdict
import torch
from farm.data_handler.data_silo import DataSilo, DataSiloForHoldout
from farm.data_handler.processor import TextClassificationProcessor
from farm.modeling.opti... | 12,645 | 44.489209 | 162 | py |
FARM | FARM-master/examples/doc_classification_custom_optimizer.py | # fmt: off
import logging
from pathlib import Path
from farm.data_handler.data_silo import DataSilo
from farm.data_handler.processor import TextClassificationProcessor
from farm.modeling.optimization import initialize_optimizer
from farm.infer import Inferencer
from farm.modeling.adaptive_model import AdaptiveModel
fr... | 5,813 | 37.76 | 133 | py |
FARM | FARM-master/examples/ner.py | # fmt: off
import logging
from pathlib import Path
from farm.data_handler.data_silo import DataSilo
from farm.data_handler.processor import NERProcessor
from farm.modeling.optimization import initialize_optimizer
from farm.infer import Inferencer
from farm.modeling.adaptive_model import AdaptiveModel
from farm.modelin... | 3,875 | 33.918919 | 162 | py |
FARM | FARM-master/examples/question_answering_confidence.py | import logging
import torch
from pathlib import Path
from farm.utils import set_all_seeds, MLFlowLogger, initialize_device_settings
from farm.modeling.tokenization import Tokenizer
from farm.data_handler.processor import SquadProcessor
from farm.data_handler.data_silo import DataSilo
from farm.modeling.adaptive_model ... | 6,899 | 50.492537 | 523 | py |
FARM | FARM-master/examples/doc_classification_with_earlystopping.py | # fmt: off
import logging
from pathlib import Path
from farm.data_handler.data_silo import DataSilo
from farm.data_handler.processor import TextClassificationProcessor
from farm.modeling.optimization import initialize_optimizer
from farm.infer import Inferencer
from farm.modeling.adaptive_model import AdaptiveModel
fr... | 7,096 | 41.497006 | 162 | py |
FARM | FARM-master/examples/dpr_encoder.py | # fmt: off
import logging
import os
import pprint
from pathlib import Path
import argparse
from farm.data_handler.data_silo import DataSilo
from farm.data_handler.processor import TextSimilarityProcessor
from farm.modeling.biadaptive_model import BiAdaptiveModel
from farm.modeling.language_model import LanguageModel
... | 6,895 | 42.371069 | 162 | py |
FARM | FARM-master/examples/natural_questions.py | # fmt: off
import logging
import pprint
from pathlib import Path
from farm.data_handler.data_silo import DataSilo
from farm.data_handler.processor import NaturalQuestionsProcessor
from farm.file_utils import fetch_archive_from_http
from farm.infer import QAInferencer
from farm.modeling.adaptive_model import AdaptiveMo... | 6,567 | 43.680272 | 529 | py |
FARM | FARM-master/examples/train_from_scratch_with_sagemaker.py | import json
import logging
from pathlib import Path
from transformers.tokenization_bert import BertTokenizer
from farm.data_handler.data_silo import StreamingDataSilo, DataSilo
from farm.data_handler.processor import BertStyleLMProcessor
from farm.modeling.adaptive_model import AdaptiveModel
from farm.modeling.langua... | 6,877 | 38.528736 | 192 | py |
FARM | FARM-master/examples/question_answering.py | # fmt: off
import logging
import os
import pprint
from pathlib import Path
from farm.data_handler.data_silo import DataSilo
from farm.data_handler.processor import SquadProcessor
from farm.data_handler.utils import write_squad_predictions
from farm.infer import QAInferencer
from farm.modeling.adaptive_model import Ada... | 5,907 | 41.2 | 528 | py |
FARM | FARM-master/examples/doc_classification_fasttext_LM.py | # fmt: off
import logging
from pathlib import Path
from farm.data_handler.data_silo import DataSilo, StreamingDataSilo
from farm.data_handler.processor import TextClassificationProcessor
from farm.modeling.optimization import initialize_optimizer
from farm.infer import Inferencer
from farm.modeling.adaptive_model impo... | 4,989 | 38.291339 | 143 | py |
FARM | FARM-master/examples/doc_classification_multilabel_roberta.py | # fmt: off
import logging
from pathlib import Path
from farm.data_handler.data_silo import DataSilo
from farm.data_handler.processor import TextClassificationProcessor
from farm.modeling.optimization import initialize_optimizer
from farm.infer import Inferencer
from farm.modeling.adaptive_model import AdaptiveModel
fr... | 4,636 | 36.395161 | 162 | py |
FARM | FARM-master/examples/passage_ranking.py | # fmt: off
import logging
from pathlib import Path
from farm.data_handler.data_silo import DataSilo
from farm.data_handler.processor import RegressionProcessor, TextPairClassificationProcessor
from farm.experiment import initialize_optimizer
from farm.infer import Inferencer
from farm.modeling.adaptive_model import Ad... | 6,123 | 40.659864 | 162 | py |
FARM | FARM-master/examples/mtl01_tclass_tclass.py | #!/usr/bin/env python
# coding: utf-8
import logging
import torch
import farm
from farm.modeling.tokenization import Tokenizer
from farm.data_handler.processor import TextClassificationProcessor
from farm.data_handler.data_silo import DataSilo
from farm.modeling.language_model import LanguageModel
from farm.modeling.... | 5,180 | 27.311475 | 84 | py |
FARM | FARM-master/examples/doc_regression.py | # fmt: off
import logging
from pathlib import Path
from farm.data_handler.data_silo import DataSilo
from farm.data_handler.processor import RegressionProcessor
from farm.experiment import initialize_optimizer
from farm.infer import Inferencer
from farm.modeling.adaptive_model import AdaptiveModel
from farm.modeling.la... | 3,833 | 33.232143 | 162 | py |
FARM | FARM-master/examples/doc_classification_crossvalidation.py | # fmt: off
import logging
import numbers
import json
import mlflow
import statistics
from pathlib import Path
from collections import defaultdict
import torch
from farm.data_handler.data_silo import DataSilo, DataSiloForCrossVal
from farm.data_handler.processor import TextClassificationProcessor
from farm.modeling.opt... | 12,744 | 44.517857 | 162 | py |
FARM | FARM-master/examples/doc_classification_word_embedding_LM.py | # fmt: off
import logging
from pathlib import Path
import time
from farm.data_handler.data_silo import DataSilo, StreamingDataSilo
from farm.data_handler.processor import TextClassificationProcessor
from farm.modeling.optimization import initialize_optimizer
from farm.infer import Inferencer
from farm.modeling.adaptiv... | 3,903 | 33.857143 | 133 | py |
FARM | FARM-master/examples/question_answering_crossvalidation.py | import logging
import json
import torch
from pathlib import Path
from farm.utils import set_all_seeds, MLFlowLogger, initialize_device_settings
from farm.modeling.tokenization import Tokenizer
from farm.data_handler.processor import SquadProcessor
from farm.data_handler.data_silo import DataSilo, DataSiloForCrossVal
f... | 8,231 | 38.38756 | 162 | py |
FARM | FARM-master/examples/text_pair_classification.py | # fmt: off
import logging
from pathlib import Path
from farm.data_handler.data_silo import DataSilo
from farm.data_handler.processor import TextPairClassificationProcessor
from farm.experiment import initialize_optimizer
from farm.infer import Inferencer
from farm.modeling.adaptive_model import AdaptiveModel
from farm... | 4,894 | 39.454545 | 217 | py |
FARM | FARM-master/examples/evaluation.py | from farm.utils import initialize_device_settings
from farm.modeling.tokenization import Tokenizer
from farm.data_handler.processor import TextClassificationProcessor, SquadProcessor
from farm.data_handler.data_silo import DataSilo
from farm.eval import Evaluator
from farm.modeling.adaptive_model import AdaptiveModel
f... | 4,656 | 33.753731 | 136 | py |
FARM | FARM-master/examples/doc_classification.py | # fmt: off
import logging
from pathlib import Path
from farm.data_handler.data_silo import DataSilo
from farm.data_handler.processor import TextClassificationProcessor
from farm.modeling.optimization import initialize_optimizer
from farm.infer import Inferencer
from farm.modeling.adaptive_model import AdaptiveModel
fr... | 4,732 | 35.976563 | 133 | py |
FARM | FARM-master/examples/doc_classification_multilabel.py | # fmt: off
import logging
from pathlib import Path
from farm.data_handler.data_silo import DataSilo
from farm.data_handler.processor import TextClassificationProcessor
from farm.modeling.optimization import initialize_optimizer
from farm.infer import Inferencer
from farm.modeling.adaptive_model import AdaptiveModel
fr... | 4,555 | 36.344262 | 162 | py |
FARM | FARM-master/examples/lm_finetuning.py | import logging
from pathlib import Path
from farm.data_handler.data_silo import DataSilo
from farm.data_handler.processor import BertStyleLMProcessor
from farm.modeling.adaptive_model import AdaptiveModel
from farm.modeling.language_model import LanguageModel
from farm.modeling.prediction_head import BertLMHead, NextS... | 3,780 | 35.355769 | 162 | py |
FARM | FARM-master/test/test_dpr.py | import pytest
import torch
import logging
import numpy as np
from pathlib import Path
from torch.utils.data import SequentialSampler
from tqdm import tqdm
from farm.data_handler.dataloader import NamedDataLoader
from farm.data_handler.processor import TextSimilarityProcessor
from farm.data_handler.data_silo import Da... | 53,065 | 64.757125 | 1,453 | py |
FARM | FARM-master/test/test_onnx_conversion.py | import numpy as np
import pytest
from farm.infer import Inferencer
from farm.modeling.adaptive_model import AdaptiveModel
@pytest.mark.parametrize("model_name", ["deepset/bert-base-cased-squad2", "deepset/roberta-base-squad2"])
def test_onnx_conversion_and_inference(tmp_path, model_name):
AdaptiveModel.convert_t... | 1,706 | 43.921053 | 115 | py |
FARM | FARM-master/test/test_lm_finetuning.py | import logging
from pathlib import Path
import numpy as np
import torch
from farm.data_handler.data_silo import DataSilo
from farm.data_handler.processor import BertStyleLMProcessor
from farm.experiment import initialize_optimizer
from farm.modeling.adaptive_model import AdaptiveModel
from farm.modeling.language_mode... | 8,572 | 30.869888 | 121 | py |
FARM | FARM-master/test/test_processor_saving_loading.py | import logging
from pathlib import Path
from farm.data_handler.processor import TextClassificationProcessor
from farm.modeling.tokenization import Tokenizer
from farm.utils import set_all_seeds
import torch
def test_processor_saving_loading(caplog):
if caplog is not None:
caplog.set_level(logging.CRITICAL... | 1,897 | 39.382979 | 84 | py |
FARM | FARM-master/test/test_model_versioning.py | import pytest
import torch
from farm.infer import Inferencer
def test_wrong_revision(caplog=None):
# We want this load attempt to fail because we specify an invalid revision
failed_load = None
try:
failed_load = Inferencer.load("deepset/roberta-base-squad2", revision="xxx", task_type="question_answ... | 1,402 | 41.515152 | 116 | py |
FARM | FARM-master/test/benchmarks/question_answering.py | import logging
import pytest
import torch
logger = logging.getLogger(__name__)
@pytest.mark.parametrize("max_seq_len", [128, 256, 384])
@pytest.mark.parametrize("batch_size", [1, 4, 16, 64])
@pytest.mark.parametrize("document_size", [10_000, 100_000])
@pytest.mark.parametrize("num_processes", [0], scope="session")
... | 2,119 | 40.568627 | 117 | py |
FARM | FARM-master/farm/utils.py | import hashlib
import json
import logging
import random
import os
import signal
import numpy as np
import torch
import torch.distributed as dist
from requests.exceptions import ConnectionError
from torch import multiprocessing as mp
import mlflow
from copy import deepcopy
import pandas as pd
from tqdm import tqdm
impor... | 19,827 | 33.785965 | 120 | py |
FARM | FARM-master/farm/eval.py | from tqdm import tqdm
import torch
import numbers
import logging
import numpy as np
from torch.utils.data import DataLoader
from farm.evaluation.metrics import compute_metrics, compute_report_metrics
from farm.utils import to_numpy
from farm.utils import MLFlowLogger as MlLogger
from farm.modeling.adaptive_model impor... | 9,068 | 49.949438 | 142 | py |
FARM | FARM-master/farm/file_utils.py | """
Utilities for working with the local dataset cache.
This file is adapted from the AllenNLP library at https://github.com/allenai/allennlp
Copyright by the AllenNLP authors.
"""
from __future__ import absolute_import, division, print_function, unicode_literals
import json
import logging
import os
import tempfile
im... | 13,244 | 33.048843 | 124 | py |
FARM | FARM-master/farm/__init__.py | import logging
import torch.multiprocessing as mp
from farm._version import __version__
logging.basicConfig(
format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
datefmt="%m/%d/%Y %H:%M:%S",
level=logging.INFO,
)
# reduce verbosity from transformers library
logging.getLogger('transformers.con... | 780 | 31.541667 | 82 | py |
FARM | FARM-master/farm/infer.py | import logging
import multiprocessing as mp
import os
from functools import partial
import warnings
import torch
from torch.utils.data.sampler import SequentialSampler
from tqdm import tqdm
from typing import Generator, List, Union
from farm.data_handler.dataloader import NamedDataLoader
from farm.data_handler.proces... | 39,232 | 51.241012 | 189 | py |
FARM | FARM-master/farm/train.py | import logging
import sys
import torch
from pathlib import Path
from tqdm import tqdm
import numpy
import shutil
import dill
from farm.utils import MLFlowLogger as MlLogger
from farm.utils import GracefulKiller, set_all_seeds
from farm.eval import Evaluator
from farm.data_handler.data_silo import DataSilo
from farm.vi... | 30,596 | 47.336493 | 153 | py |
FARM | FARM-master/farm/evaluation/metrics.py | import logging
import numpy as np
import torch
from functools import reduce
from scipy.stats import pearsonr, spearmanr
from seqeval.metrics import classification_report as token_classification_report
from seqeval.metrics import f1_score as ner_f1_score
from sklearn.metrics import (
matthews_corrcoef,
f1_score... | 15,109 | 41.804533 | 188 | py |
FARM | FARM-master/farm/conversion/convert_tf_checkpoint_to_pytorch.py | # coding=utf-8
# Copyright 2018 The HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable... | 2,305 | 29.342105 | 83 | py |
FARM | FARM-master/farm/modeling/optimization.py | from importlib import import_module
import logging
import sys
import inspect
import torch
from torch.nn.parallel import DistributedDataParallel
from torch.nn import DataParallel
# Used indirectly in _get_optim() to avoid name collision with torch's AdamW
from transformers.optimization import AdamW as TransformersAdam... | 15,061 | 45.631579 | 173 | py |
FARM | FARM-master/farm/modeling/prediction_head.py | import json
import logging
import os
import numpy as np
from pathlib import Path
from transformers.models.bert.modeling_bert import BertForPreTraining, ACT2FN
from transformers import AutoModelForQuestionAnswering, AutoModelForTokenClassification, AutoModelForSequenceClassification
from typing import List, Tuple
impo... | 88,126 | 46.456651 | 194 | py |
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