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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LWSIS | LWSIS-main/adet/modeling/fcos/fcos.py | import math
from typing import List, Dict
import torch
from torch import nn
from torch.nn import functional as F
from detectron2.layers import ShapeSpec, NaiveSyncBatchNorm
from detectron2.modeling.proposal_generator.build import PROPOSAL_GENERATOR_REGISTRY
from adet.layers import DFConv2d, NaiveGroupNorm
from adet.u... | 8,954 | 36.944915 | 97 | py |
LWSIS | LWSIS-main/adet/modeling/fcos/fcos_outputs.py | import logging
import torch
from torch import nn
import torch.nn.functional as F
from detectron2.layers import cat
from detectron2.structures import Instances, Boxes
from detectron2.utils.comm import get_world_size
from fvcore.nn import sigmoid_focal_loss_jit
from adet.utils.comm import reduce_sum, reduce_mean, compu... | 22,136 | 39.176044 | 150 | py |
LWSIS | LWSIS-main/adet/modeling/condinst/dynamic_mask_head.py | import torch
from torch.nn import functional as F
from torch import nn
from adet.utils.comm import compute_locations, aligned_bilinear
from detectron2.layers import cat
def compute_project_term(mask_scores, gt_bitmasks):
mask_losses_y = dice_coefficient(
mask_scores.max(dim=2, keepdim=True)[0],
g... | 20,968 | 42.868201 | 114 | py |
LWSIS | LWSIS-main/adet/modeling/condinst/condinst copy.py | # -*- coding: utf-8 -*-
import logging
from skimage import color
import torch
from torch import nn
import torch.nn.functional as F
from detectron2.structures import ImageList
from detectron2.modeling.proposal_generator import build_proposal_generator
from detectron2.modeling.backbone import build_backbone
from detect... | 19,472 | 41.610503 | 174 | py |
LWSIS | LWSIS-main/adet/modeling/condinst/mask_branch.py | from typing import Dict
import math
import torch
from torch import nn
from fvcore.nn import sigmoid_focal_loss_jit
from detectron2.layers import ShapeSpec
from adet.layers import conv_with_kaiming_uniform
from adet.utils.comm import aligned_bilinear
INF = 100000000
def build_mask_branch(cfg, input_shape):
re... | 5,136 | 35.956835 | 83 | py |
LWSIS | LWSIS-main/adet/modeling/condinst/condinst.py | # -*- coding: utf-8 -*-
import logging
from skimage import color
import torch
from torch import nn
import torch.nn.functional as F
from detectron2.structures import ImageList
from detectron2.modeling.proposal_generator import build_proposal_generator
from detectron2.modeling.backbone import build_backbone
from detect... | 25,694 | 40.510501 | 116 | py |
LWSIS | LWSIS-main/adet/modeling/batext/batext_outputs.py | import logging
import torch
import torch.nn.functional as F
from detectron2.layers import cat
from detectron2.structures import Instances, Boxes
from adet.utils.comm import get_world_size
from fvcore.nn import sigmoid_focal_loss_jit
from adet.utils.comm import reduce_sum, compute_ious
from adet.layers import ml_nms
... | 19,881 | 36.302064 | 111 | py |
LWSIS | LWSIS-main/adet/modeling/batext/batext.py | import math
from typing import List, Dict
import torch
from torch import nn
from torch.nn import functional as F
from detectron2.layers import ShapeSpec
from detectron2.modeling.proposal_generator.build import PROPOSAL_GENERATOR_REGISTRY
from adet.layers import DFConv2d, IOULoss
from .batext_outputs import BATextOutp... | 10,102 | 36.697761 | 97 | py |
LWSIS | LWSIS-main/adet/modeling/roi_heads/attn_predictor.py | import random
import torch
from torch import nn
from torch.nn import functional as F
from torch.autograd import Variable
from adet.layers import conv_with_kaiming_uniform
class BidirectionalLSTM(nn.Module):
def __init__(self, nIn, nHidden, nOut):
super(BidirectionalLSTM, self).__init__()
se... | 6,014 | 37.557692 | 116 | py |
LWSIS | LWSIS-main/adet/modeling/roi_heads/text_head.py | import math
from typing import Dict, List
import torch
from torch import nn
from torch.nn import functional as F
from detectron2.layers import ShapeSpec, cat
from detectron2.modeling import ROI_HEADS_REGISTRY
from adet.layers import conv_with_kaiming_uniform
from ..poolers import TopPooler
from .attn_predictor impor... | 6,210 | 33.126374 | 88 | py |
LWSIS | LWSIS-main/adet/modeling/blendmask/blendmask.py | # -*- coding: utf-8 -*-
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
import torch
from torch import nn
from detectron2.structures import ImageList
from detectron2.modeling.postprocessing import detector_postprocess, sem_seg_postprocess
from detectron2.modeling.proposal_generator import build... | 7,136 | 45.045161 | 97 | py |
LWSIS | LWSIS-main/adet/modeling/blendmask/basis_module.py | from typing import Dict
from torch import nn
from torch.nn import functional as F
from detectron2.utils.registry import Registry
from detectron2.layers import ShapeSpec
from adet.layers import conv_with_kaiming_uniform
BASIS_MODULE_REGISTRY = Registry("BASIS_MODULE")
BASIS_MODULE_REGISTRY.__doc__ = """
Registry for... | 4,397 | 40.885714 | 92 | py |
LWSIS | LWSIS-main/adet/modeling/blendmask/blender.py | import torch
from torch.nn import functional as F
from detectron2.layers import cat
from detectron2.modeling.poolers import ROIPooler
def build_blender(cfg):
return Blender(cfg)
class Blender(object):
def __init__(self, cfg):
# fmt: off
self.pooler_resolution = cfg.MODEL.BLENDMASK.BOTTOM_R... | 4,183 | 39.230769 | 98 | py |
LWSIS | LWSIS-main/adet/modeling/solov2/loss.py | import torch
from torch import nn
import torch.nn.functional as F
from fvcore.nn import sigmoid_focal_loss_jit
def dice_loss(input, target):
input = input.contiguous().view(input.size()[0], -1)
target = target.contiguous().view(target.size()[0], -1).float()
a = torch.sum(input * target, 1)
b = torch.... | 4,437 | 33.671875 | 79 | py |
LWSIS | LWSIS-main/adet/modeling/solov2/utils.py | import cv2
import torch
import torch.nn.functional as F
def _scale_size(size, scale):
"""Rescale a size by a ratio.
Args:
size (tuple[int]): (w, h).
scale (float): Scaling factor.
Returns:
tuple[int]: scaled size.
"""
w, h = size
return int(w * float(scale) + 0.5), int(... | 7,245 | 33.018779 | 109 | py |
LWSIS | LWSIS-main/adet/modeling/solov2/solov2.py | # -*- coding: utf-8 -*-
import logging
import math
from typing import List
import torch
import torch.nn.functional as F
from torch import nn
from detectron2.layers import ShapeSpec, batched_nms, cat, paste_masks_in_image
from detectron2.modeling.anchor_generator import DefaultAnchorGenerator
from detectron2.modeling.... | 32,145 | 42.148993 | 174 | py |
LWSIS | LWSIS-main/adet/modeling/MEInst/MEInst.py | import math
from typing import List, Dict
import numpy as np
import torch
from torch import nn
from torch.nn import functional as F
from detectron2.layers import ShapeSpec
from detectron2.modeling.proposal_generator.build import PROPOSAL_GENERATOR_REGISTRY
from adet.layers import DFConv2d, IOULoss, NaiveGroupNorm, GC... | 13,324 | 39.50152 | 123 | py |
LWSIS | LWSIS-main/adet/modeling/MEInst/MEInst_outputs.py | import logging
from typing import List
import torch
import torch.nn as nn
import torch.nn.functional as F
from detectron2.layers import cat
from detectron2.structures import Instances, Boxes, pairwise_iou
from detectron2.utils.comm import get_world_size
from detectron2.modeling.matcher import Matcher
from fvcore.nn ... | 25,186 | 37.989164 | 111 | py |
LWSIS | LWSIS-main/adet/modeling/MEInst/MaskEncoding.py | # coding:utf-8
import torch
import torch.nn as nn
VALUE_MAX = 0.05
VALUE_MIN = 0.01
@torch.no_grad()
class PCAMaskEncoding(nn.Module):
"""
To do the mask encoding of PCA.
components_: (tensor), shape (n_components, n_features) if agnostic=True
else (n_samples, n_compon... | 5,145 | 39.84127 | 110 | py |
LWSIS | LWSIS-main/adet/modeling/MEInst/LME/mask_generation.py | # coding:utf-8
import os
import argparse
import time
import numpy as np
import torch
from torch.utils.data import DataLoader
from sklearn.decomposition import IncrementalPCA
from MaskLoader import MaskLoader
from utils import inverse_sigmoid
VALUE_MAX = 0.05
VALUE_MIN = 0.01
def mask_encoding(masks, n_components=... | 4,633 | 38.948276 | 110 | py |
LWSIS | LWSIS-main/adet/modeling/MEInst/LME/MaskLoader.py | # coding:utf-8
import os
import json
import numpy as np
import torch.utils.data as data
from detectron2.structures import (
Boxes,
PolygonMasks,
BoxMode
)
DATASETS = {
"coco_2017_train": {
"img_dir": "coco/train2017",
"ann_file": "coco/annotations/instances_train2017.jso... | 2,261 | 26.585366 | 93 | py |
LWSIS | LWSIS-main/adet/modeling/MEInst/LME/mask_evaluation.py | # coding:utf-8
import os
import argparse
import numpy as np
from torch.utils.data import DataLoader
from MaskLoader import MaskLoader
from utils import (
IOUMetric,
transform,
inverse_transform,
direct_sigmoid,
inverse_sigmoid
)
VALUE_MAX = 0.05
VALUE_MIN = 0.01
def parse_args():
parser = ... | 3,982 | 36.933333 | 112 | py |
LWSIS | LWSIS-main/adet/structures/beziers.py | from typing import Union
import torch
class Beziers:
"""
This structure stores a list of bezier curves as a Nx16 torch.Tensor.
It will support some common methods about bezier shapes
(`area`, `clip`, `nonempty`, etc),
and also behaves like a Tensor
(support indexing, `to(device)`, `.device`, a... | 1,685 | 37.318182 | 96 | py |
LWSIS | LWSIS-main/demo/predictor.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
import numpy as np
import atexit
import bisect
import multiprocessing as mp
from collections import deque
import cv2
import torch
import matplotlib.pyplot as plt
from detectron2.data import MetadataCatalog
from detectron2.engine.defaults import Def... | 9,022 | 35.383065 | 96 | py |
LWSIS | LWSIS-main/docs/conf.py | # Configuration file for the Sphinx documentation builder.
#
# This file only contains a selection of the most common options. For a full
# list see the documentation:
# https://www.sphinx-doc.org/en/master/usage/configuration.html
# -- Path setup --------------------------------------------------------------
# If ex... | 8,887 | 27.857143 | 91 | py |
LWSIS | LWSIS-main/onnx/test_onnxruntime.py |
"""
A working example to export the R-50 based FCOS model:
python onnx/test_onnxruntime.py \
--config-file configs/FCOS-Detection/R_50_1x.yaml \
--output /data/pretrained/onnx/fcos/FCOS_R_50_1x_bn_head.onnx
--opts MODEL.WEIGHTS /data/pretrained/pytorch/fcos/FCOS_R_50_1x_bn_head.pth MODEL.FCOS.NORM "BN"
""... | 11,336 | 35.808442 | 127 | py |
LWSIS | LWSIS-main/onnx/export_model_to_onnx.py | """
A working example to export the R-50 based FCOS model:
python onnx/export_model_to_onnx.py \
--config-file configs/FCOS-Detection/R_50_1x.yaml \
--output /data/pretrained/onnx/fcos/FCOS_R_50_1x_bn_head.onnx
--opts MODEL.WEIGHTS /data/pretrained/pytorch/fcos/FCOS_R_50_1x_bn_head.pth MODEL.FCOS.NORM "BN"
... | 10,438 | 33.796667 | 127 | py |
entroformer | entroformer-main/dataset.py | import torch.utils.data as data
from os import listdir
from os.path import join
from PIL import Image
import pickle
def is_image_file(filename):
return any(filename.endswith(extension) for extension in [".png", ".jpg", ".jpeg", ".JPEG"])
def load_img(filepath):
img = Image.open(filepath).convert('RGB')
r... | 2,474 | 33.375 | 99 | py |
entroformer | entroformer-main/util.py | # coding=utf-8
import torch
import math, argparse
import torch.nn as nn
import numpy as np
import torch.nn.functional as F
import logging
from logging import handlers
def get_parser():
parser = argparse.ArgumentParser(description='PyTorch Compression')
# Base configure
parser.add_argument("--na", type=str... | 12,938 | 45.711191 | 146 | py |
entroformer | entroformer-main/main_trans_hyper_ar.py | # coding=utf-8
"""
Entroformer with hyperprior and context model.
"""
import argparse, os
import numpy as np
from PIL import Image
import torch
import torch.nn as nn
from torch.utils.data import DataLoader
from torchvision.transforms import RandomCrop, RandomHorizontalFlip, RandomVerticalFlip, Compose, ToTensor
from to... | 26,695 | 41.850722 | 128 | py |
entroformer | entroformer-main/module/quant.py | import torch
from torch import nn
from torch.distributions import uniform
import numpy as np
class SteQuant(nn.Module):
def __init__(self, table_range=128, func=torch.round):
super(SteQuant, self).__init__()
assert(func is not None)
self.func = func
self.table_range = table_range
... | 1,222 | 28.829268 | 79 | py |
entroformer | entroformer-main/module/ballenet.py | # coding=utf-8
import torch
from torch import nn
from .ops import GDN, GSDN, Upsample
class Balle2Encoder(nn.Module):
"""4 layer NA"""
def __init__(self, num_filter=128, last_channel_num=128, norm='GDN'):
super(Balle2Encoder, self).__init__()
self.channel = num_filter
self.last_channel... | 2,080 | 37.537037 | 100 | py |
entroformer | entroformer-main/module/ssim.py | import torch
import torch.nn.functional as F
def _fspecial_gauss_1d(size, sigma):
r"""Create 1-D gauss kernel
Args:
size (int): the size of gauss kernel
sigma (float): sigma of normal distribution
Returns:
torch.Tensor: 1D kernel
"""
coords = torch.arange(size).to(dtype=to... | 10,506 | 39.256705 | 174 | py |
entroformer | entroformer-main/module/prob_model.py | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import torch
from torch import nn
class Entropy(torch.nn.Module):
"""Parameterized Entropy Model (Channel-wise)"""
def __init__(self, channel, bin_size=1):
super(Entropy, self).__init__()
... | 1,363 | 33.974359 | 100 | py |
entroformer | entroformer-main/module/entroformer.py | #coding:utf-8
"""
"""
import numpy as np
import torch
from torch import nn
import torch.nn.functional as F
from einops import rearrange, repeat
from .entroformer_helper import Config, Block, clones
from .ops import UpPixelShuffle
class TransDecoder(nn.Module):
debug = False
train_scan_mode = 'default' # def... | 17,372 | 39.877647 | 139 | py |
entroformer | entroformer-main/module/ops.py | # coding=utf-8
import torch
from torch import nn
from torch.autograd import Function
import torch.nn.functional as F
class MaskedConv2d(nn.Conv2d):
def __init__(self, mask_type, *args, **kwargs):
super(MaskedConv2d, self).__init__(*args, **kwargs)
assert mask_type in {'A', 'B'}
... | 8,185 | 33.251046 | 114 | py |
entroformer | entroformer-main/module/entroformer_helper.py | #coding:utf-8
"""
"""
import numpy as np
import torch
import copy
from torch import nn
import torch.nn.functional as F
from einops import rearrange, repeat
class Config():
def __init__(
self,
debug=False,
dim=384,
num_layers=6,
num_decoder_layers=None,
num_heads=6,
... | 12,256 | 37.544025 | 156 | py |
entroformer | entroformer-main/criterion/distribution_mixture.py | """
# ------
# Naming
# ------
Note that we use the following names through the code, following the code PixelCNN++:
- x: targets, e.g., the RGB image for scale 0
- l: for the output of the network;
In Fig. 2 in our paper, l is the final output, denoted with p(z^(s-1) | f^(s)), i.e., it contains the para... | 11,765 | 42.098901 | 130 | py |
entroformer | entroformer-main/ac/util.py | import sys, os
import torch
import torchac
import numpy as np
from torch.utils.cpp_extension import load
python3 = sys.version_info.major >= 3
numpyAc_backend = load(
name="numpyAc_backend",
sources=["ac/backend/numpyAc_backend.cpp"],
verbose=False)
PRECISION = 16 # DO NOT EDIT!
def convert_to_int_and_normali... | 7,070 | 35.637306 | 116 | py |
contextual-repr-analysis | contextual-repr-analysis-master/scripts/calypso_elmo_parameter_shift.py | """
Given a pretrained contexteval model, extract the Calypso BiLM contextualizer
and compare each layer with a pre-trained Calypso BiLM contextualizer.
"""
import argparse
import logging
import os
import sys
from allennlp.models import load_archive
import torch
sys.path.insert(0, os.path.dirname(os.path.abspath(os.p... | 3,212 | 43.013699 | 115 | py |
contextual-repr-analysis | contextual-repr-analysis-master/scripts/get_elmo_scalar_weights.py | """
Given a model archive, find an ElmoEmbedder and get the scalar
weighting learned by ELMo.
"""
import argparse
import json
import logging
from allennlp.models import load_archive
from allennlp.modules.text_field_embedders import TextFieldEmbedder
from allennlp.modules.token_embedders import ElmoTokenEmbedder
from a... | 4,144 | 43.569892 | 87 | py |
contextual-repr-analysis | contextual-repr-analysis-master/scripts/generate_openai_transformer_embeddings.py | """
Given a pre-processed input text file, this command outputs the internal
layers used to compute OpenAI transformer representations to a single (potentially large) file.
The input file is previously tokenized, whitespace separated text, one sentence per line.
The output is a hdf5 file (<http://docs.h5py.org/en/late... | 10,816 | 42.971545 | 139 | py |
contextual-repr-analysis | contextual-repr-analysis-master/scripts/allennlp_elmo_parameter_shift.py | """
Given a pretrained contexteval model, extract the Elmo contextualizer
and compare each layer with a pre-trained AllenNLP Elmo contextualizer.
"""
import argparse
import logging
import os
import sys
from allennlp.models import load_archive
from allennlp.modules.elmo import Elmo
import torch
sys.path.insert(0, os.p... | 4,332 | 46.615385 | 168 | py |
contextual-repr-analysis | contextual-repr-analysis-master/tests/contextualizers/elmo_contextualizer_test.py | from allennlp.common import Params
from allennlp.common.checks import ConfigurationError
import numpy as np
from numpy.testing import assert_allclose
import pytest
import torch
from contexteval.contextualizers import Contextualizer
from contexteval.common.custom_test_case import CustomTestCase
from contexteval.common.... | 10,707 | 41.15748 | 94 | py |
contextual-repr-analysis | contextual-repr-analysis-master/tests/common/util_test.py | import numpy as np
from numpy.testing import assert_allclose
import torch
from contexteval.common.custom_test_case import CustomTestCase
from contexteval.common.util import (
get_text_mask_from_representations,
pad_contextualizer_output)
class TestUtil(CustomTestCase):
def test_get_text_mask_from_represe... | 2,062 | 49.317073 | 83 | py |
contextual-repr-analysis | contextual-repr-analysis-master/contexteval/contextualizers/contextualizer.py | from typing import List
import logging
from allennlp.common.registrable import Registrable
import torch
logger = logging.getLogger(__name__) # pylint: disable=invalid-name
class Contextualizer(torch.nn.Module, Registrable):
"""
A ``Contextualizer`` knows how to turn a batch of sentences (the latter of whic... | 1,328 | 34.918919 | 95 | py |
contextual-repr-analysis | contextual-repr-analysis-master/contexteval/contextualizers/glove_contextualizer.py | import logging
from typing import List
from allennlp.common import Tqdm
from allennlp.common.checks import ConfigurationError
from allennlp.data.vocabulary import DEFAULT_OOV_TOKEN
from allennlp.modules.token_embedders.embedding import EmbeddingsTextFile
import numpy
import torch
from contexteval.contextualizers impo... | 5,310 | 47.281818 | 111 | py |
contextual-repr-analysis | contextual-repr-analysis-master/contexteval/contextualizers/elmo_contextualizer.py | import logging
from typing import List
from allennlp.common.checks import ConfigurationError
from allennlp.common.util import lazy_groups_of
from allennlp.modules.elmo import Elmo, batch_to_ids
import torch
from torch.nn import ParameterList, Parameter
from contexteval.contextualizers import Contextualizer
logger =... | 8,679 | 48.885057 | 95 | py |
contextual-repr-analysis | contextual-repr-analysis-master/contexteval/contextualizers/scalar_mixed_precomputed_contextualizer.py | import json
import logging
from typing import List
from allennlp.common.checks import ConfigurationError
from allennlp.common.file_utils import cached_path
from allennlp.modules.scalar_mix import ScalarMix
import h5py
import torch
from contexteval.contextualizers import Contextualizer
logger = logging.getLogger(__na... | 4,624 | 43.471154 | 95 | py |
contextual-repr-analysis | contextual-repr-analysis-master/contexteval/contextualizers/precomputed_contextualizer.py | import json
import logging
from typing import List, Optional
from allennlp.common.checks import ConfigurationError
from allennlp.common.file_utils import cached_path
import h5py
import torch
from contexteval.contextualizers import Contextualizer
logger = logging.getLogger(__name__) # pylint: disable=invalid-name
... | 7,326 | 49.881944 | 100 | py |
contextual-repr-analysis | contextual-repr-analysis-master/contexteval/common/model_test_case.py | """
This is mostly copied over from allennlp.common.testing.ModelTestCase
"""
import copy
from typing import Any, Dict, Set, Union
from allennlp.commands.train import train_model_from_file
from allennlp.common import Params
from allennlp.data import DataIterator, DatasetReader, Vocabulary
from allennlp.data.dataset im... | 11,809 | 49.042373 | 103 | py |
contextual-repr-analysis | contextual-repr-analysis-master/contexteval/common/custom_test_case.py | import logging
import os
import pathlib
import shutil
from unittest import TestCase
from allennlp.common.checks import log_pytorch_version_info
class CustomTestCase(TestCase):
"""
A custom subclass of :class:`~unittest.TestCase` that disables some of the
more verbose AllenNLP logging and that creates and... | 1,379 | 35.315789 | 98 | py |
contextual-repr-analysis | contextual-repr-analysis-master/contexteval/common/util.py | from typing import Any, List
import torch
from allennlp.nn.util import get_mask_from_sequence_lengths
from allennlp.training.metrics.metric import Metric
from allennlp.training.metrics import CategoricalAccuracy, F1Measure, SpanBasedF1Measure
from contexteval.training.metrics import Perplexity
def get_text_mask_from_... | 2,571 | 35.742857 | 88 | py |
contextual-repr-analysis | contextual-repr-analysis-master/contexteval/training/null_optimizer.py | from collections import defaultdict
import logging
import torch
from allennlp.training.optimizers import Optimizer
logger = logging.getLogger(__name__) # pylint: disable=invalid-name
@Optimizer.register('null')
class NullOptimizer(torch.optim.Optimizer):
"""
A dummy optimizer that does not update anything... | 1,256 | 27.568182 | 79 | py |
contextual-repr-analysis | contextual-repr-analysis-master/contexteval/models/word_conditional_majority_tagger.py | from collections import Counter
import logging
import typing
from typing import Dict, List, Optional
from allennlp.common.checks import ConfigurationError
from allennlp.data import Vocabulary
from allennlp.models.model import Model
from allennlp.nn import InitializerApplicator, RegularizerApplicator
from allennlp.nn.u... | 12,721 | 46.64794 | 102 | py |
contextual-repr-analysis | contextual-repr-analysis-master/contexteval/models/tagger.py | import logging
from typing import Dict, List, Optional, Union
from allennlp.common import Params
from allennlp.common.checks import check_dimensions_match, ConfigurationError
from allennlp.common.file_utils import cached_path
from allennlp.data import Vocabulary
from allennlp.modules import ConditionalRandomField, Fee... | 24,550 | 51.571734 | 109 | py |
contextual-repr-analysis | contextual-repr-analysis-master/contexteval/models/word_conditional_majority_pairwise_tagger.py | from collections import Counter
import logging
from typing import Dict, List, Optional, Tuple
import typing
import numpy
from overrides import overrides
import torch
from allennlp.data import Vocabulary
from allennlp.models.model import Model
from allennlp.nn import InitializerApplicator, RegularizerApplicator
from a... | 11,282 | 46.407563 | 102 | py |
contextual-repr-analysis | contextual-repr-analysis-master/contexteval/models/word_conditional_majority_selective_tagger.py | from collections import Counter
import logging
import typing
from typing import Dict, List, Optional
from allennlp.data import Vocabulary
from allennlp.models.model import Model
from allennlp.nn import InitializerApplicator, RegularizerApplicator
from allennlp.training.metrics import CategoricalAccuracy, F1Measure
im... | 12,147 | 45.544061 | 102 | py |
contextual-repr-analysis | contextual-repr-analysis-master/contexteval/models/pairwise_tagger.py | import logging
from typing import Dict, List, Optional, Union
import numpy
from overrides import overrides
import torch
import torch.nn.functional as F
from allennlp.common import Params
from allennlp.common.checks import check_dimensions_match, ConfigurationError
from allennlp.common.file_utils import cached_path
fr... | 21,982 | 50.846698 | 109 | py |
contextual-repr-analysis | contextual-repr-analysis-master/contexteval/models/selective_regressor.py | import logging
from typing import Dict, List, Optional, Union
from overrides import overrides
import torch
from allennlp.common import Params
from allennlp.common.checks import check_dimensions_match, ConfigurationError
from allennlp.common.file_utils import cached_path
from allennlp.data import Vocabulary
from allen... | 17,584 | 50.873156 | 109 | py |
contextual-repr-analysis | contextual-repr-analysis-master/contexteval/models/selective_tagger.py | import logging
from typing import Dict, List, Optional, Union
import numpy
from overrides import overrides
import torch
import torch.nn.functional as F
from allennlp.common import Params
from allennlp.common.checks import check_dimensions_match, ConfigurationError
from allennlp.common.file_utils import cached_path
fr... | 20,890 | 50.83871 | 109 | py |
contextual-repr-analysis | contextual-repr-analysis-master/contexteval/data/dataset_readers/event_factuality.py | from typing import Dict, List, Optional, Set, Union
import json
import logging
import numpy as np
from overrides import overrides
from allennlp.data.dataset_readers.dataset_reader import DatasetReader
from allennlp.data.fields import ArrayField, Field, ListField, MetadataField
from allennlp.data.instance import Instan... | 7,826 | 43.220339 | 93 | py |
contextual-repr-analysis | contextual-repr-analysis-master/contexteval/data/dataset_readers/adposition_supersense_tagging.py | from typing import Dict, List, Optional, Set, Union
import json
import logging
from overrides import overrides
from allennlp.common.checks import ConfigurationError
from allennlp.data.dataset_readers.dataset_reader import DatasetReader
from allennlp.data.fields import ArrayField, Field, ListField, MetadataField, Seque... | 8,622 | 42.550505 | 96 | py |
contextual-repr-analysis | contextual-repr-analysis-master/contexteval/data/dataset_readers/coreference_arc_prediction.py | import logging
import collections
from typing import DefaultDict, Dict, List, Optional, Set, Tuple, Union
import random
from overrides import overrides
from allennlp.common.file_utils import cached_path
from allennlp.data.dataset_readers.dataset_reader import DatasetReader
from allennlp.data.fields import ArrayField,... | 15,880 | 46.690691 | 114 | py |
contextual-repr-analysis | contextual-repr-analysis-master/contexteval/data/dataset_readers/tagging.py | from typing import Dict, List, Optional, Set, Union
import logging
from overrides import overrides
from allennlp.data.fields import ArrayField, Field, ListField, MetadataField, SequenceLabelField
from allennlp.data.instance import Instance
from torch import FloatTensor
from contexteval.contextualizers import Contextu... | 4,758 | 41.873874 | 98 | py |
contextual-repr-analysis | contextual-repr-analysis-master/contexteval/data/dataset_readers/semantic_dependency_arc_classification.py | from typing import Dict, List, Optional, Set, Tuple, Union
import logging
from allennlp.common.file_utils import cached_path
from allennlp.data.dataset_readers.dataset_reader import DatasetReader
from allennlp.data.fields import (
ArrayField,
Field,
ListField,
MetadataField,
SequenceLabelField)
fro... | 10,885 | 44.548117 | 96 | py |
contextual-repr-analysis | contextual-repr-analysis-master/contexteval/data/dataset_readers/syntactic_dependency_arc_classification.py | from typing import Dict, List, Optional, Set, Tuple, Union
import logging
from allennlp.common.file_utils import cached_path
from allennlp.data.dataset_readers.dataset_reader import DatasetReader
from allennlp.data.fields import ArrayField, Field, ListField, MetadataField, SequenceLabelField
from allennlp.data.instanc... | 10,574 | 46.421525 | 96 | py |
contextual-repr-analysis | contextual-repr-analysis-master/contexteval/data/dataset_readers/dependency_arc_prediction.py | from typing import Dict, List, Optional, Tuple, Union
import logging
import random
from allennlp.common.checks import ConfigurationError
from allennlp.data.fields import ArrayField, Field, ListField, MetadataField, SequenceLabelField
from allennlp.data.instance import Instance
import numpy as np
from torch import Floa... | 8,222 | 46.80814 | 96 | py |
neuralcoref | neuralcoref-master/neuralcoref/train/learn.py | """Conll training algorithm"""
import os
import time
import argparse
import socket
from datetime import datetime
import numpy as np
import torch
import torch.nn as nn
from torch.autograd import Variable
from torch.optim import RMSprop
from torch.utils.data import DataLoader
from tensorboardX import SummaryWriter
fro... | 19,819 | 34.017668 | 111 | py |
neuralcoref | neuralcoref-master/neuralcoref/train/model.py | """Conll training algorithm"""
import os
import numpy as np
import torch
import torch.nn as nn
import torch.utils.data
class Model(nn.Module):
def __init__(
self, vocab_size, embedding_dim, H1, H2, H3, D_pair_in, D_single_in, dropout=0.5
):
super(Model, self).__init__()
self.word_emb... | 4,946 | 39.219512 | 104 | py |
neuralcoref | neuralcoref-master/neuralcoref/train/dataset.py | """Conll training algorithm"""
import os
import io
import numpy as np
import torch
import torch.utils.data
from torch.utils.data.sampler import Sampler
from torch.utils.data import Dataset
from neuralcoref.train.utils import (
encode_distance,
BATCH_SIZE_PATH,
SIZE_FP,
SIZE_FP_COMPRESSED,
SIZE_F... | 19,896 | 36.052142 | 110 | py |
neuralcoref | neuralcoref-master/neuralcoref/train/evaluator.py | """Conll Evaluation - Scoring"""
import os
import subprocess
import io
import pickle
import torch
from torch.utils.data import DataLoader
from neuralcoref.train.conllparser import FEATURES_NAMES
from neuralcoref.train.dataset import NCBatchSampler, padder_collate
from neuralcoref.train.compat import unicode_
PACKAG... | 13,503 | 38.028902 | 88 | py |
fetch | fetch-master/bin/train.py | #!/usr/bin/env python3
import argparse
import logging
import os
import string
import pandas as pd
from keras.callbacks import EarlyStopping, CSVLogger, ModelCheckpoint
from keras.models import Model
from sklearn.model_selection import train_test_split
from fetch.data_sequence import DataGenerator
from fetch.utils im... | 5,463 | 45.700855 | 118 | py |
fetch | fetch-master/fetch/utils.py | #!/usr/bin/env python3
import glob
import logging
import os
import string
import numpy as np
import pandas as pd
from keras.models import Model
from keras.models import model_from_yaml
from keras.optimizers import Adam
from keras.utils import get_file
os.environ["HDF5_USE_FILE_LOCKING"] = "FALSE"
PATH_TO_WEIGHTS = '... | 3,075 | 29.455446 | 110 | py |
fetch | fetch-master/fetch/data_sequence.py | """
https://stanford.edu/~shervine/blog/keras-how-to-generate-data-on-the-fly
"""
import logging
import os
import h5py
import keras
import numpy as np
import scipy.signal as s
os.environ["HDF5_USE_FILE_LOCKING"] = "FALSE"
logger = logging.getLogger(__name__)
class DataGenerator(keras.utils.Sequence):
def __ini... | 4,992 | 33.19863 | 114 | py |
USC-DS-RelationExtraction | USC-DS-RelationExtraction-master/code/Model/baselines/hypenet/sdp.py | import csv
import datetime
import json
import time
from os.path import expanduser, exists
from pprint import pprint
from zipfile import ZipFile
import numpy as np
SEED = np.random.randint(1,1001)
print('Using Random Seed: '+str(SEED))
np.random.seed(SEED)
from keras.utils.np_utils import to_categorical
from keras im... | 11,777 | 33.641176 | 119 | py |
USC-DS-RelationExtraction | USC-DS-RelationExtraction-master/code/Model/baselines/sentence-level-models/train-cv.py | '''
Training script with ramdom splitting dev set
'''
__author__ = 'Maosen'
import torch
from model import Model
import utils
from utils import Dataset, CVDataset, get_cv_dataset
import argparse
import pickle
import numpy as np
from tqdm import tqdm
import logging
import os
import random
if __name__ == '__main__':
p... | 5,989 | 45.076923 | 120 | py |
USC-DS-RelationExtraction | USC-DS-RelationExtraction-master/code/Model/baselines/sentence-level-models/utils.py | '''
Data Loader for Position-Aware LSTM for Relation Extraction
'''
__author__ = 'Maosen'
import torch
import torch.utils.data
import torch.nn as nn
from tqdm import tqdm
import pickle
import json
import math
import random
import os
# vocab
PAD_TOKEN = '<PAD>'
PAD_ID = 0
UNK_TOKEN = '<UNK>'
UNK_ID = 1
VOCAB_PREFIX = [... | 10,665 | 36.293706 | 1,088 | py |
USC-DS-RelationExtraction | USC-DS-RelationExtraction-master/code/Model/baselines/sentence-level-models/model.py | '''
Model wrapper for Relation Extraction
'''
__author__ = 'Maosen'
import torch
import torch.nn as nn
from tqdm import tqdm
import utils
from models.position_aware_lstm import PositionAwareLSTM
from models.bgru import BGRU
from models.cnn import CNN
from models.pcnn import PCNN
from models.lstm import LSTM
class Mo... | 3,736 | 27.526718 | 121 | py |
USC-DS-RelationExtraction | USC-DS-RelationExtraction-master/code/Model/baselines/sentence-level-models/train.py | __author__ = 'Maosen'
import torch
from model import Model
import utils
from utils import Dataset, CVDataset, get_cv_dataset
import argparse
import pickle
import numpy as np
from tqdm import tqdm
import logging
import os
import random
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument... | 5,988 | 46.912 | 120 | py |
USC-DS-RelationExtraction | USC-DS-RelationExtraction-master/code/Model/baselines/sentence-level-models/models/lstm.py | __author__ = 'Maosen'
import torch
import torch.nn as nn
import torch.nn.functional as F
import utils
from utils import pos2id, ner2id
import sys
from tqdm import tqdm
class LSTM(nn.Module):
def __init__(self, args, rel2id, word_emb=None):
super(LSTM, self).__init__()
# arguments
hidden, vocab_size, emb_dim, po... | 3,277 | 32.111111 | 122 | py |
USC-DS-RelationExtraction | USC-DS-RelationExtraction-master/code/Model/baselines/sentence-level-models/models/cnn.py | __author__ = 'Maosen'
import torch
import torch.nn as nn
import torch.nn.functional as F
import utils
from utils import pos2id, ner2id
import sys
from tqdm import tqdm
class CNN(nn.Module):
def __init__(self, args, rel2id, word_emb=None):
super(CNN, self).__init__()
# arguments
hidden, vocab_size, emb_dim, pos_... | 3,130 | 32.666667 | 122 | py |
USC-DS-RelationExtraction | USC-DS-RelationExtraction-master/code/Model/baselines/sentence-level-models/models/position_aware_lstm.py | __author__ = 'Maosen'
import torch
import torch.nn as nn
import torch.nn.functional as F
import utils
from utils import pos2id, ner2id
import sys
from tqdm import tqdm
class PositionAwareLSTM(nn.Module):
def __init__(self, args, rel2id, word_emb=None):
super(PositionAwareLSTM, self).__init__()
# arguments
hidde... | 3,916 | 32.478632 | 122 | py |
USC-DS-RelationExtraction | USC-DS-RelationExtraction-master/code/Model/baselines/sentence-level-models/models/bgru.py | __author__ = 'Maosen'
import torch
import torch.nn as nn
import torch.nn.functional as F
import utils
from utils import pos2id, ner2id
import sys
from tqdm import tqdm
class BGRU(nn.Module):
def __init__(self, args, rel2id, word_emb=None):
super(BGRU, self).__init__()
# arguments
hidden, vocab_size, emb_dim, po... | 3,309 | 32.77551 | 140 | py |
USC-DS-RelationExtraction | USC-DS-RelationExtraction-master/code/Model/baselines/sentence-level-models/models/pcnn.py | __author__ = 'Maosen'
import torch
import torch.nn as nn
import torch.nn.functional as F
import utils
from utils import pos2id, ner2id
import sys
from tqdm import tqdm
class PCNN(nn.Module):
def __init__(self, args, rel2id, word_emb=None):
super(PCNN, self).__init__()
# arguments
hidden, vocab_size, emb_dim, po... | 3,729 | 32.909091 | 122 | py |
OSSL | OSSL-main/utils.py | import torch
import numpy as np
#import torch.nn as nn
#import torch.utils.data
#import itertools
import random
# Assumes that tensor is (nchannels, height, width)
def tensor_rot_90(x):
return x.flip(2).transpose(1, 2)
def tensor_rot_180(x):
return x.flip(2).flip(1)
def tensor_rot_270(x):
return x.tr... | 1,525 | 25.77193 | 93 | py |
OSSL | OSSL-main/densenet.py | import math
import torch
import torch.nn as nn
import torch.nn.functional as F
class BasicBlock(nn.Module):
def __init__(self, in_planes, out_planes, dropRate=0.0):
super(BasicBlock, self).__init__()
self.bn1 = nn.BatchNorm2d(in_planes)
self.relu = nn.ReLU(inplace=True)
self.conv1 ... | 5,392 | 39.548872 | 107 | py |
OSSL | OSSL-main/cifar_new.py | import numpy as np
import torch.utils.data as data
from PIL import Image
class CIFAR_New(data.Dataset):
def __init__(self, root, transform=None, target_transform=None, version='v6'):
self.data = np.load('%s/cifar10.1_%s_data.npy' % (root, version))
self.targets = np.load('%s/cifar10.1_%s_labels.np... | 828 | 35.043478 | 93 | py |
OSSL | OSSL-main/train.py | import argparse
import os
import shutil
import time
import torch
import torch.backends.cudnn as cudnn
import torch.nn as nn
import torch.nn.parallel
import torch.optim
import torch.utils.data
from torch.utils.tensorboard import SummaryWriter
import torchvision.datasets as datasets
import torchvision.transforms as tr... | 12,727 | 36 | 115 | py |
CLIPSep | CLIPSep-main/music/preprocess.py | """Preprocess the data."""
import argparse
import logging
import pathlib
import pprint
import sys
import imageio
import joblib
import numpy as np
import torchvision.transforms
import tqdm
from PIL import Image
try:
from torchvision.transforms import InterpolationMode
BICUBIC = InterpolationMode.BICUBIC
excep... | 4,766 | 25.192308 | 79 | py |
CLIPSep | CLIPSep-main/clipsep/evaluate.py | """Evaluate the model."""
import argparse
import collections
import logging
import pathlib
import pprint
import random
import sys
import types
import clip
import mir_eval.separation
import museval.metrics
import numpy as np
import scipy.io.wavfile
import scipy.stats
import torch
import torch.nn.functional as F
import ... | 25,451 | 32.845745 | 173 | py |
CLIPSep | CLIPSep-main/clipsep/dataset.py | """Datasets."""
import argparse
import csv
import logging
import pathlib
import random
import librosa
import numpy as np
import scipy.io.wavfile
import torch
import torchvision
from PIL import Image
import utils
def _convert_image_to_rgb(image):
return image.convert("RGB")
def transform():
"""Preprocessin... | 31,721 | 30.849398 | 80 | py |
CLIPSep | CLIPSep-main/clipsep/clipsep.py | """Define the models."""
import functools
import torch
import torch.nn as nn
import torch.nn.functional as F
import utils
def init_weights(net):
classname = net.__class__.__name__
if classname.find("Conv") != -1:
net.weight.data.normal_(0.0, 0.001)
elif classname.find("BatchNorm") != -1:
... | 62,386 | 30.668528 | 120 | py |
CLIPSep | CLIPSep-main/clipsep/infer.py | """Infer from a trained model."""
import argparse
import logging
import pathlib
import pprint
import random
import sys
import types
import clip
import librosa
import numpy as np
import scipy.io.wavfile
import torch
import torch.nn.functional as F
import torch.optim
import torch.utils.data
import torchvision
import cl... | 14,718 | 30.585837 | 139 | py |
CLIPSep | CLIPSep-main/clipsep/train.py | """Train the model."""
import argparse
import logging
import pathlib
import pprint
import random
import shutil
import sys
import types
import clip
import numpy as np
import torch
import torch.optim
import torch.utils.data
import torchvision.models
import tqdm
import clipsep
import dataset
import utils
@utils.resolv... | 26,809 | 31.97663 | 81 | py |
CLIPSep | CLIPSep-main/clipsep/preprocess.py | """Preprocess the data."""
import argparse
import logging
import pathlib
import pprint
import sys
import imageio
import joblib
import numpy as np
import torchvision.transforms
import tqdm
from PIL import Image
import utils
@utils.resolve_paths
def parse_args(args=None, namespace=None):
"""Parse command-line arg... | 4,614 | 25.371429 | 79 | py |
CLIPSep | CLIPSep-main/clipsep/visualize.py | """Visualize the results."""
import argparse
import logging
import pathlib
import pprint
import random
import sys
import types
import clip
import imageio
import numpy as np
import scipy.io.wavfile
import torch
import torch.nn.functional as F
import torch.optim
import torch.utils.data
import torchvision
import tqdm
im... | 26,774 | 32.096415 | 139 | py |
CLIPSep | CLIPSep-main/vggsound/preprocess.py | """Preprocess the data."""
import argparse
import logging
import pathlib
import pprint
import sys
import imageio
import joblib
import numpy as np
import torchvision.transforms
import tqdm
from PIL import Image
try:
from torchvision.transforms import InterpolationMode
BICUBIC = InterpolationMode.BICUBIC
excep... | 4,766 | 25.192308 | 79 | py |
direct-ie | direct-ie-master/code/main.py | import os
import torch
import random
import logging
import pickle
import json
from tqdm import tqdm, trange
from random import shuffle
from time import time
from torch.cuda.amp import autocast
import numpy as np
import utils
from model import Direct
from batch_loader import BatchLoader
from bert.optimization import B... | 15,256 | 48.535714 | 117 | py |
direct-ie | direct-ie-master/code/utils.py | import json
import logging
import os
import shutil
import torch
import numpy as np
from tqdm import tqdm
class RunningAverage():
"""A simple class that maintains the running average of a quantity
Example:
```
loss_avg = RunningAverage()
loss_avg.update(2)
loss_avg.update(4)
loss_avg() = 3
... | 4,665 | 29.900662 | 109 | py |
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