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
value |
|---|---|---|---|---|---|---|
ADaPTION | ADaPTION-master/frcnn/lib/rpn/anchor_target_layer.py | # --------------------------------------------------------
# Faster R-CNN
# Copyright (c) 2015 Microsoft
# Licensed under The MIT License [see LICENSE for details]
# Written by Ross Girshick and Sean Bell
# --------------------------------------------------------
import os
import caffe
import yaml
from fast_rcnn.confi... | 11,575 | 39.055363 | 95 | py |
ADaPTION | ADaPTION-master/frcnn/lib/transform/torch_image_transform_layer.py | # --------------------------------------------------------
# Fast/er R-CNN
# Licensed under The MIT License [see LICENSE for details]
# --------------------------------------------------------
""" Transform images for compatibility with models trained with
https://github.com/facebook/fb.resnet.torch.
Usage in model p... | 2,000 | 29.784615 | 72 | py |
selectionfunctions | selectionfunctions-main/docs/conf.py | # -*- coding: utf-8 -*-
#
# dustmaps documentation build configuration file, created by
# sphinx-quickstart on Fri Oct 14 17:20:58 2016.
#
# This file is execfile()d with the current directory set to its
# containing dir.
#
# Note that not all possible configuration values are present in this
# autogenerated file.
#
# ... | 11,441 | 27.605 | 80 | py |
stancy | stancy-master/run_classifier.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... | 32,594 | 41.607843 | 144 | py |
stancy | stancy-master/modeling.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... | 40,433 | 45.475862 | 139 | py |
stancy | stancy-master/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 shutil
im... | 8,295 | 32.184 | 112 | py |
thermo | thermo-master/docs/conf.py | #
# thermo documentation build configuration file, created by
# sphinx-quickstart on Sat Jan 2 17:15:23 2016.
#
# This file is execfile()d with the current directory set to its
# containing dir.
#
# Note that not all possible configuration values are present in this
# autogenerated file.
#
# All configuration values h... | 11,870 | 30.571809 | 127 | py |
FeatureRE | FeatureRE-main/reverse_engineering.py | import torch
from torch import Tensor, nn
import torchvision
import os
import numpy as np
from resnet_nole import *
from models import meta_classifier_cifar10_model,lenet,ULP_model,preact_resnet
import torch.nn.functional as F
import unet_model
import random
import pilgram
from PIL import Image
from functools import r... | 19,453 | 35.914611 | 239 | py |
FeatureRE | FeatureRE-main/unet_blocks.py | """
Class definitions for a standard U-Net Up-and Down-sampling blocks
http://arxiv.org/abs/1505.0.397
"""
import torch
import torch.nn as nn
import torch.nn.functional as F
class EncoderBlock(nn.Module):
"""
Instances the Encoder block that forms a part of a U-Net
Parameters:
in_channels (int): D... | 12,064 | 43.356618 | 130 | py |
FeatureRE | FeatureRE-main/unet_model.py | """
A PyTorch Implementation of a U-Net.
Supports 2D (https://arxiv.org/abs/1505.04597) and 3D(https://arxiv.org/abs/1606.06650) variants
Author: Ishaan Bhat
Email: ishaan@isi.uu.nl
"""
from unet_blocks import *
from math import pow
class UNet(nn.Module):
"""
PyTorch class definition for the U-Net architectu... | 10,176 | 49.381188 | 162 | py |
FeatureRE | FeatureRE-main/dataloader.py | import torch.utils.data as data
import torch
import torchvision
import torchvision.transforms as transforms
import os
import csv
import random
import numpy as np
from PIL import Image
from torch.utils.tensorboard import SummaryWriter
from torch.utils.data import Dataset
from io import BytesIO
def get_transform(opt... | 8,265 | 37.990566 | 142 | py |
FeatureRE | FeatureRE-main/models.py | import torch
import torch.nn.functional as F
import torchvision
from torch import nn
from torch.nn import Module
from torchvision import transforms
from .blocks import *
class Normalize:
def __init__(self, opt, expected_values, variance):
self.n_channels = opt.input_channel
self.expected_values =... | 4,338 | 32.898438 | 106 | py |
FeatureRE | FeatureRE-main/resnet_nole.py | import torch.nn as nn
import math
def conv3x3(in_planes, out_planes, stride=1):
# 3x3 convolution with padding
return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride, padding=1, bias=False)
class BasicBlock(nn.Module):
expansion = 1
def __init__(self, inplanes, planes, stride=1, downsa... | 5,495 | 29.703911 | 109 | py |
FeatureRE | FeatureRE-main/detection.py | from reverse_engineering import *
from config import get_argument
from dataloader import get_dataloader_label_remove, get_dataloader_partial_split
import time
def main():
start_time = time.time()
opt = get_argument().parse_args()
if opt.dataset == "cifar10":
opt.input_height = 32
opt.input... | 4,155 | 36.107143 | 140 | py |
FeatureRE | FeatureRE-main/mitigation.py | from reverse_engineering import *
from config import get_argument
from dataloader import get_dataloader_label_remove, get_dataloader_partial_split
import time
def main():
start_time = time.time()
opt = get_argument().parse_args()
if opt.dataset == "cifar10":
opt.input_height = 32
opt.input... | 3,433 | 35.924731 | 140 | py |
FeatureRE | FeatureRE-main/train_models/dataloader.py | import torch.utils.data as data
import torch
import torchvision
import torchvision.transforms as transforms
import os
import csv
import kornia.augmentation as A
import random
import numpy as np
from PIL import Image
from torch.utils.tensorboard import SummaryWriter
from torch.utils.data import Dataset
from natsort im... | 6,855 | 36.26087 | 119 | py |
FeatureRE | FeatureRE-main/train_models/train_model.py | import json
import os
import shutil
from time import time
import config
import numpy as np
import torch
import torch.nn.functional as F
import torchvision
from torch import nn
from torch.utils.tensorboard import SummaryWriter
from torchvision.transforms import RandomErasing
from dataloader import PostTensorTransform, ... | 16,640 | 34.107595 | 125 | py |
FeatureRE | FeatureRE-main/train_models/resnet_nole.py | import torch.nn as nn
import math
def conv3x3(in_planes, out_planes, stride=1):
# 3x3 convolution with padding
return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride, padding=1, bias=False)
'''class BasicBlock(nn.Module):
expansion = 1
def __init__(self, inplanes, planes, stride=1, do... | 26,787 | 28.21265 | 109 | py |
FeatureRE | FeatureRE-main/models/meta_classifier_cifar10_model.py | import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
class MetaClassifierCifar10Model(nn.Module):
def __init__(self):
super(MetaClassifierCifar10Model, self).__init__()
#self.gpu = gpu
self.conv1 = nn.Conv2d(3, 32, kernel_size=3, padding=1)
self.con... | 2,441 | 31.131579 | 70 | py |
FeatureRE | FeatureRE-main/models/preact_resnet.py | """Pre-activation ResNet in PyTorch.
Reference:
[1] Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun
Identity Mappings in Deep Residual Networks. arXiv:1603.05027
"""
import torch
import torch.nn as nn
import torch.nn.functional as F
class PreActBlock(nn.Module):
"""Pre-activation version of the BasicBlock.... | 7,366 | 29.316872 | 103 | py |
FeatureRE | FeatureRE-main/models/ULP_model.py | import torch
import torch.nn as nn
import torch.nn.functional as F
#from utils.stn import STN
class CNN_classifier(nn.Module):
""" MNIST Encoder from Original Paper's Keras based Implementation.
Args:
init_num_filters (int): initial number of filters from encoder image channels
lrel... | 2,934 | 36.628205 | 121 | py |
FeatureRE | FeatureRE-main/models/lenet.py | # This part is borrowed from https://github.com/huawei-noah/Data-Efficient-Model-Compression
import torch.nn as nn
class LeNet5(nn.Module):
def __init__(self,in_channels=1):
super(LeNet5, self).__init__()
self.conv1 = nn.Conv2d(in_channels, 6, kernel_size=(5, 5))
self.relu1 = nn.ReLU()
... | 4,665 | 31.859155 | 102 | py |
MRL-CQA | MRL-CQA-master/S2SRL/train_scst_nsm.py | #!/usr/bin/env python3
import os
import sys
import random
import argparse
import logging
import numpy as np
from tensorboardX import SummaryWriter
from libbots import data, model, utils
import torch
import torch.optim as optim
import torch.nn.functional as F
import time
import ptan
SAVES_DIR = "../data/saves"
BATCH_... | 27,125 | 57.461207 | 153 | py |
MRL-CQA | MRL-CQA-master/S2SRL/data_test_maml.py | # !/usr/bin/env python3
# The file is used to predict the action sequences for full-data test dataset.
import argparse
import logging
import sys
from libbots import data, model, utils, metalearner
import torch
log = logging.getLogger("data_test")
DIC_PATH = '../data/auto_QA_data/share.question'
TRAIN_944K_QUESTION_A... | 8,582 | 58.193103 | 211 | py |
MRL-CQA | MRL-CQA-master/S2SRL/train_reptile_maml_true_reward.py | #!/usr/bin/env python3
import os
import sys
import random
import argparse
import logging
import numpy as np
from tensorboardX import SummaryWriter
from libbots import data, model, utils, metalearner
import torch
import time
import ptan
SAVES_DIR = "../data/saves"
MAX_EPOCHES = 30
MAX_TOKENS = 40
TRAIN_RATIO = 0.985
... | 15,550 | 59.984314 | 436 | py |
MRL-CQA | MRL-CQA-master/S2SRL/libbots/adabound.py | import math
import torch
from torch.optim import Optimizer
class AdaBound(Optimizer):
"""Implements AdaBound algorithm.
It has been proposed in `Adaptive Gradient Methods with Dynamic Bound of Learning Rate`_.
Arguments:
params (iterable): iterable of parameters to optimize or dicts defining
... | 11,340 | 47.465812 | 101 | py |
MRL-CQA | MRL-CQA-master/S2SRL/libbots/reparam_module.py | import torch
import torch.nn as nn
import warnings
import types
from collections import namedtuple
from contextlib import contextmanager
# A module is a container from which layers, model subparts (e.g. BasicBlock in resnet in torchvision) and models should inherit.
# Why should they? Because the inheritance from nn.M... | 12,442 | 53.336245 | 152 | py |
MRL-CQA | MRL-CQA-master/S2SRL/libbots/bert_model.py | import numpy as np
import operator
import torch
import torch.nn as nn
import torch.nn.utils.rnn as rnn_utils
import torch.nn.functional as F
from transformers import BertModel, BertTokenizer, AdamW, get_linear_schedule_with_warmup
from . import utils
from . import attention
from . import beam_search_node
from queue im... | 23,511 | 46.595142 | 177 | py |
MRL-CQA | MRL-CQA-master/S2SRL/libbots/model.py | import numpy as np
import operator
import torch
import torch.nn as nn
import torch.nn.utils.rnn as rnn_utils
import torch.nn.functional as F
from collections import OrderedDict
from . import utils
from . import attention
from . import beam_search_node
from queue import PriorityQueue
HIDDEN_STATE_SIZE = 128
EMBEDDING_... | 22,030 | 47.10262 | 177 | py |
MRL-CQA | MRL-CQA-master/S2SRL/libbots/data.py | import collections
import os
import sys
import logging
import itertools
import pickle
import json
import torch
from . import cornell
UNKNOWN_TOKEN = '#UNK'
BEGIN_TOKEN = "#BEG"
END_TOKEN = "#END"
MAX_TOKENS = 30
MIN_TOKEN_FEQ = 1
SHUFFLE_SEED = 1987
LINE_SIZE = 50000
EMB_DICT_NAME = "emb_dict.dat"
EMB_NAME = "emb.np... | 21,655 | 35.705085 | 257 | py |
MRL-CQA | MRL-CQA-master/S2SRL/libbots/metalearner.py | import torch
from torch.nn.utils.convert_parameters import (vector_to_parameters,
parameters_to_vector)
from . import data, model, utils, retriever, reparam_module, adabound
import torch.optim as optim
import torch.nn.functional as F
import random
import logging
from torch... | 91,609 | 58.603123 | 328 | py |
MRL-CQA | MRL-CQA-master/S2SRL/libbots/attention.py | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.nn.utils.rnn as rnn_utils
class Attention(nn.Module):
r"""
Applies an attention mechanism on the output features from the decoder.
.. math::
\begin{array}{ll}
x = context*output \\
attn = ex... | 4,204 | 45.722222 | 191 | py |
graph-rcnn.pytorch | graph-rcnn.pytorch-master/main.py | """
Implementation of ECCV 2018 paper "Graph R-CNN for Scene Graph Generation".
Author: Jianwei Yang, Jiasen Lu, Stefan Lee, Dhruv Batra, Devi Parikh
Contact: jw2yang@gatech.edu
"""
import os
import pprint
import argparse
import numpy as np
import torch
import datetime
from lib.config import cfg
from lib.model import... | 3,200 | 33.419355 | 87 | py |
graph-rcnn.pytorch | graph-rcnn.pytorch-master/lib/model.py | import os
import datetime
import logging
import time
import numpy as np
import torch
import cv2
from .data.build import build_data_loader
from .scene_parser.parser import build_scene_parser
from .scene_parser.parser import build_scene_parser_optimizer
from .scene_parser.rcnn.utils.metric_logger import MetricLogger
from... | 13,581 | 43.097403 | 132 | py |
graph-rcnn.pytorch | graph-rcnn.pytorch-master/lib/scene_parser/parser.py | """
Main code of scene parser
"""
import os
import logging
import torch
import copy
import torch.nn as nn
from .rcnn.modeling.detector.generalized_rcnn import GeneralizedRCNN
from .rcnn.solver import make_lr_scheduler
from .rcnn.solver import make_optimizer
from .rcnn.utils.checkpoint import SceneParserCheckpointer
fr... | 8,133 | 40.28934 | 119 | py |
graph-rcnn.pytorch | graph-rcnn.pytorch-master/lib/scene_parser/rcnn/setup.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
#!/usr/bin/env python
import glob
import os
import torch
from setuptools import find_packages
from setuptools import setup
from torch.utils.cpp_extension import CUDA_HOME
from torch.utils.cpp_extension import CppExtension
from torch.utils.cpp_ext... | 2,027 | 27.971429 | 100 | py |
graph-rcnn.pytorch | graph-rcnn.pytorch-master/lib/scene_parser/rcnn/solver/lr_scheduler.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
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.optim.lr_s... | 1,817 | 33.301887 | 80 | py |
graph-rcnn.pytorch | graph-rcnn.pytorch-master/lib/scene_parser/rcnn/solver/build.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
import torch
from .lr_scheduler import WarmupMultiStepLR
def make_optimizer(cfg, model):
params = []
lr = cfg.SOLVER.BASE_LR
for key, value in model.named_parameters():
if not value.requires_grad:
continue
... | 972 | 29.40625 | 79 | py |
graph-rcnn.pytorch | graph-rcnn.pytorch-master/lib/scene_parser/rcnn/layers/batch_norm.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
import torch
from torch import nn
class FrozenBatchNorm2d(nn.Module):
"""
BatchNorm2d where the batch statistics and the affine parameters
are fixed
"""
def __init__(self, n):
super(FrozenBatchNorm2d, self).__init__()... | 1,094 | 33.21875 | 71 | py |
graph-rcnn.pytorch | graph-rcnn.pytorch-master/lib/scene_parser/rcnn/layers/roi_pool.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
import torch
from torch import nn
from torch.autograd import Function
from torch.autograd.function import once_differentiable
from torch.nn.modules.utils import _pair
from lib.scene_parser.rcnn import _C
# from apex import amp
class _ROIPool(Fun... | 1,907 | 27.909091 | 74 | py |
graph-rcnn.pytorch | graph-rcnn.pytorch-master/lib/scene_parser/rcnn/layers/roi_align.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
import torch
from torch import nn
from torch.autograd import Function
from torch.autograd.function import once_differentiable
from torch.nn.modules.utils import _pair
from lib.scene_parser.rcnn import _C
# from apex import amp
class _ROIAlign(Fu... | 2,161 | 29.885714 | 85 | py |
graph-rcnn.pytorch | graph-rcnn.pytorch-master/lib/scene_parser/rcnn/layers/smooth_l1_loss.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
import torch
# TODO maybe push this to nn?
def smooth_l1_loss(input, target, beta=1. / 9, size_average=True):
"""
very similar to the smooth_l1_loss from pytorch, but with
the extra beta parameter
"""
n = torch.abs(input - tar... | 481 | 27.352941 | 71 | py |
graph-rcnn.pytorch | graph-rcnn.pytorch-master/lib/scene_parser/rcnn/layers/sigmoid_focal_loss.py | import torch
from torch import nn
from torch.autograd import Function
from torch.autograd.function import once_differentiable
from lib.scene_parser.rcnn import _C
# TODO: Use JIT to replace CUDA implementation in the future.
class _SigmoidFocalLoss(Function):
@staticmethod
def forward(ctx, logits, targets, ga... | 2,345 | 29.467532 | 118 | py |
graph-rcnn.pytorch | graph-rcnn.pytorch-master/lib/scene_parser/rcnn/layers/_utils.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
import glob
import os.path
import torch
try:
from torch.utils.cpp_extension import load as load_ext
from torch.utils.cpp_extension import CUDA_HOME
except ImportError:
raise ImportError("The cpp layer extensions requires PyTorch 0.4 o... | 1,165 | 28.15 | 80 | py |
graph-rcnn.pytorch | graph-rcnn.pytorch-master/lib/scene_parser/rcnn/layers/misc.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
"""
helper class that supports empty tensors on some nn functions.
Ideally, add support directly in PyTorch to empty tensors in
those functions.
This can be removed once https://github.com/pytorch/pytorch/issues/12013
is implemented
"""
import m... | 6,625 | 31.480392 | 88 | py |
graph-rcnn.pytorch | graph-rcnn.pytorch-master/lib/scene_parser/rcnn/layers/__init__.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
import torch
from .batch_norm import FrozenBatchNorm2d
from .misc import Conv2d
from .misc import DFConv2d
from .misc import ConvTranspose2d
from .misc import BatchNorm2d
from .misc import interpolate
from .nms import nms
from .roi_align import RO... | 1,327 | 26.666667 | 105 | py |
graph-rcnn.pytorch | graph-rcnn.pytorch-master/lib/scene_parser/rcnn/layers/dcn/deform_conv_func.py | import torch
from torch.autograd import Function
from torch.autograd.function import once_differentiable
from torch.nn.modules.utils import _pair
from lib.scene_parser.rcnn import _C
class DeformConvFunction(Function):
@staticmethod
def forward(
ctx,
input,
offset,
weight,
... | 8,312 | 30.608365 | 83 | py |
graph-rcnn.pytorch | graph-rcnn.pytorch-master/lib/scene_parser/rcnn/layers/dcn/deform_pool_func.py | import torch
from torch.autograd import Function
from torch.autograd.function import once_differentiable
from lib.scene_parser.rcnn import _C
class DeformRoIPoolingFunction(Function):
@staticmethod
def forward(
ctx,
data,
rois,
offset,
spatial_scale,
out_size,
... | 2,597 | 26.347368 | 99 | py |
graph-rcnn.pytorch | graph-rcnn.pytorch-master/lib/scene_parser/rcnn/layers/dcn/deform_pool_module.py | from torch import nn
from .deform_pool_func import deform_roi_pooling
class DeformRoIPooling(nn.Module):
def __init__(self,
spatial_scale,
out_size,
out_channels,
no_trans,
group_size=1,
part_size=None,
... | 6,306 | 41.046667 | 79 | py |
graph-rcnn.pytorch | graph-rcnn.pytorch-master/lib/scene_parser/rcnn/layers/dcn/deform_conv_module.py | import math
import torch
import torch.nn as nn
from torch.nn.modules.utils import _pair
from .deform_conv_func import deform_conv, modulated_deform_conv
class DeformConv(nn.Module):
def __init__(
self,
in_channels,
out_channels,
kernel_size,
stride=1,
padding=0,
... | 5,802 | 31.601124 | 78 | py |
graph-rcnn.pytorch | graph-rcnn.pytorch-master/lib/scene_parser/rcnn/engine/inference.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
import logging
import time
import os
import torch
from tqdm import tqdm
from lib.config import cfg
from lib.data.datasets.evaluation import evaluate
from ..utils.comm import is_main_process, get_world_size
from ..utils.comm import all_gather
from... | 4,027 | 32.289256 | 96 | py |
graph-rcnn.pytorch | graph-rcnn.pytorch-master/lib/scene_parser/rcnn/engine/bbox_aug.py | import torch
import torchvision.transforms as TT
from lib.config import cfg
from lib.data import transforms as T
from lib.scene_parser.rcnn.structures.image_list import to_image_list
from lib.scene_parser.rcnn.structures.bounding_box import BoxList
from lib.scene_parser.rcnn.modeling.roi_heads.box_head.inference impor... | 4,418 | 36.449153 | 99 | py |
graph-rcnn.pytorch | graph-rcnn.pytorch-master/lib/scene_parser/rcnn/engine/trainer.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
import datetime
import logging
import time
import torch
import torch.distributed as dist
from lib.scene_parser.rcnn.utils.comm import get_world_size
from lib.scene_parser.rcnn.utils.metric_logger import MetricLogger
from apex import amp
def red... | 4,251 | 33.290323 | 146 | py |
graph-rcnn.pytorch | graph-rcnn.pytorch-master/lib/scene_parser/rcnn/utils/c2_model_loading.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
import logging
import pickle
from collections import OrderedDict
import torch
from .model_serialization import load_state_dict
from .registry import Registry
def _rename_basic_resnet_weights(layer_keys):
layer_keys = [k.replace("_", ".") fo... | 8,444 | 39.023697 | 129 | py |
graph-rcnn.pytorch | graph-rcnn.pytorch-master/lib/scene_parser/rcnn/utils/metric_logger.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
from collections import defaultdict
from collections import deque
import torch
class SmoothedValue(object):
"""Track a series of values and provide access to smoothed values over a
window or the global series average.
"""
def __... | 1,862 | 26.80597 | 82 | py |
graph-rcnn.pytorch | graph-rcnn.pytorch-master/lib/scene_parser/rcnn/utils/checkpoint.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
import logging
import os
import torch
from .model_serialization import load_state_dict
from .c2_model_loading import load_c2_format
from .imports import import_file
from .model_zoo import cache_url
class Checkpointer(object):
def __init__(
... | 6,815 | 34.5 | 108 | py |
graph-rcnn.pytorch | graph-rcnn.pytorch-master/lib/scene_parser/rcnn/utils/comm.py | """
This file contains primitives for multi-gpu communication.
This is useful when doing distributed training.
"""
import pickle
import time
import torch
import torch.distributed as dist
def get_world_size():
if not dist.is_available():
return 1
if not dist.is_initialized():
return 1
ret... | 3,372 | 27.584746 | 84 | py |
graph-rcnn.pytorch | graph-rcnn.pytorch-master/lib/scene_parser/rcnn/utils/model_zoo.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
import os
import sys
try:
from torch.hub import _download_url_to_file
from torch.hub import urlparse
from torch.hub import HASH_REGEX
except ImportError:
from torch.utils.model_zoo import _download_url_to_file
from torch.utils.... | 2,997 | 47.354839 | 135 | py |
graph-rcnn.pytorch | graph-rcnn.pytorch-master/lib/scene_parser/rcnn/utils/collect_env.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
import PIL
from torch.utils.collect_env import get_pretty_env_info
def get_pil_version():
return "\n Pillow ({})".format(PIL.__version__)
def collect_env_info():
env_str = get_pretty_env_info()
env_str += get_pil_version()
... | 338 | 21.6 | 71 | py |
graph-rcnn.pytorch | graph-rcnn.pytorch-master/lib/scene_parser/rcnn/utils/model_serialization.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
from collections import OrderedDict
import logging
import torch
from .imports import import_file
def align_and_update_state_dicts(model_state_dict, loaded_state_dict):
"""
Strategy: suppose that the models that we will create will have ... | 3,683 | 42.857143 | 91 | py |
graph-rcnn.pytorch | graph-rcnn.pytorch-master/lib/scene_parser/rcnn/utils/visualize.py | import cv2
import torch
def select_top_predictions(predictions, confidence_threshold=0.2):
"""
Select only predictions which have a `score` > self.confidence_threshold,
and returns the predictions in descending order of score
Arguments:
predictions (BoxList): the result of the computation by th... | 3,482 | 35.663158 | 103 | py |
graph-rcnn.pytorch | graph-rcnn.pytorch-master/lib/scene_parser/rcnn/utils/imports.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
import torch
if torch._six.PY3:
import importlib
import importlib.util
import sys
# from https://stackoverflow.com/questions/67631/how-to-import-a-module-given-the-full-path?utm_medium=organic&utm_source=google_rich_qa&utm_campai... | 843 | 34.166667 | 168 | py |
graph-rcnn.pytorch | graph-rcnn.pytorch-master/lib/scene_parser/rcnn/data/datasets/voc.py | import os
import torch
import torch.utils.data
from PIL import Image
import sys
if sys.version_info[0] == 2:
import xml.etree.cElementTree as ET
else:
import xml.etree.ElementTree as ET
from maskrcnn_benchmark.structures.bounding_box import BoxList
class PascalVOCDataset(torch.utils.data.Dataset):
CL... | 4,168 | 29.654412 | 118 | py |
graph-rcnn.pytorch | graph-rcnn.pytorch-master/lib/scene_parser/rcnn/data/datasets/concat_dataset.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
import bisect
from torch.utils.data.dataset import ConcatDataset as _ConcatDataset
class ConcatDataset(_ConcatDataset):
"""
Same as torch.utils.data.dataset.ConcatDataset, but exposes an extra
method for querying the sizes of the ima... | 766 | 30.958333 | 72 | py |
graph-rcnn.pytorch | graph-rcnn.pytorch-master/lib/scene_parser/rcnn/data/datasets/coco.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
import torch
import torchvision
from maskrcnn_benchmark.structures.bounding_box import BoxList
from maskrcnn_benchmark.structures.segmentation_mask import SegmentationMask
from maskrcnn_benchmark.structures.keypoint import PersonKeypoints
min_ke... | 3,783 | 35.038095 | 85 | py |
graph-rcnn.pytorch | graph-rcnn.pytorch-master/lib/scene_parser/rcnn/data/datasets/evaluation/coco/coco_eval.py | import logging
import tempfile
import os
import torch
from collections import OrderedDict
from tqdm import tqdm
from maskrcnn_benchmark.modeling.roi_heads.mask_head.inference import Masker
from maskrcnn_benchmark.structures.bounding_box import BoxList
from maskrcnn_benchmark.structures.boxlist_ops import boxlist_iou
... | 14,055 | 34.405542 | 88 | py |
graph-rcnn.pytorch | graph-rcnn.pytorch-master/lib/scene_parser/rcnn/modeling/pair_matcher.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
import torch
class PairMatcher(object):
"""
This class assigns to each predicted "element" (e.g., a box) a ground-truth
element. Each predicted element will have exactly zero or one matches; each
ground-truth element may be assign... | 5,155 | 44.628319 | 88 | py |
graph-rcnn.pytorch | graph-rcnn.pytorch-master/lib/scene_parser/rcnn/modeling/matcher.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
import torch
class Matcher(object):
"""
This class assigns to each predicted "element" (e.g., a box) a ground-truth
element. Each predicted element will have exactly zero or one matches; each
ground-truth element may be assigned t... | 5,129 | 44.39823 | 88 | py |
graph-rcnn.pytorch | graph-rcnn.pytorch-master/lib/scene_parser/rcnn/modeling/make_layers.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
"""
Miscellaneous utility functions
"""
import torch
from torch import nn
from torch.nn import functional as F
from ..config import cfg
from ..layers import Conv2d
from .poolers import Pooler
def get_group_gn(dim, dim_per_gp, num_groups):
""... | 3,496 | 27.430894 | 78 | py |
graph-rcnn.pytorch | graph-rcnn.pytorch-master/lib/scene_parser/rcnn/modeling/utils.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
"""
Miscellaneous utility functions
"""
import torch
def cat(tensors, dim=0):
"""
Efficient version of torch.cat that avoids a copy if there is only a single element in a list
"""
assert isinstance(tensors, (list, tuple))
if ... | 400 | 22.588235 | 97 | py |
graph-rcnn.pytorch | graph-rcnn.pytorch-master/lib/scene_parser/rcnn/modeling/balanced_positive_negative_pair_sampler.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
# object pair sampler, implemented by Jianwei Yang
import torch
class BalancedPositiveNegativePairSampler(object):
"""
This class samples batches, ensuring that they contain a fixed proportion of positives
"""
def __init__(self, ... | 2,773 | 38.628571 | 90 | py |
graph-rcnn.pytorch | graph-rcnn.pytorch-master/lib/scene_parser/rcnn/modeling/poolers.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
import torch
import torch.nn.functional as F
from torch import nn
from ..layers import ROIAlign
from .utils import cat
class LevelMapper(object):
"""Determine which FPN level each RoI in a set of RoIs should map to based
on the heuristi... | 4,544 | 32.91791 | 90 | py |
graph-rcnn.pytorch | graph-rcnn.pytorch-master/lib/scene_parser/rcnn/modeling/balanced_positive_negative_sampler.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
import torch
class BalancedPositiveNegativeSampler(object):
"""
This class samples batches, ensuring that they contain a fixed proportion of positives
"""
def __init__(self, batch_size_per_image, positive_fraction):
"""
... | 2,718 | 38.405797 | 90 | py |
graph-rcnn.pytorch | graph-rcnn.pytorch-master/lib/scene_parser/rcnn/modeling/box_coder.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
import math
import torch
class BoxCoder(object):
"""
This class encodes and decodes a set of bounding boxes into
the representation used for training the regressors.
"""
def __init__(self, weights, bbox_xform_clip=math.log(1... | 3,367 | 34.083333 | 86 | py |
graph-rcnn.pytorch | graph-rcnn.pytorch-master/lib/scene_parser/rcnn/modeling/backbone/resnet.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
"""
Variant of the resnet module that takes cfg as an argument.
Example usage. Strings may be specified in the config file.
model = ResNet(
"StemWithFixedBatchNorm",
"BottleneckWithFixedBatchNorm",
"ResNet50StagesTo4",
... | 14,321 | 30.476923 | 85 | py |
graph-rcnn.pytorch | graph-rcnn.pytorch-master/lib/scene_parser/rcnn/modeling/backbone/fbnet_builder.py | """
FBNet model builder
"""
from __future__ import absolute_import, division, print_function, unicode_literals
import copy
import logging
import math
from collections import OrderedDict
import torch
import torch.nn as nn
from lib.scene_parser.rcnn.layers import (
BatchNorm2d,
Conv2d,
FrozenBatchNorm2d,
... | 24,970 | 29.085542 | 88 | py |
graph-rcnn.pytorch | graph-rcnn.pytorch-master/lib/scene_parser/rcnn/modeling/backbone/fbnet.py | from __future__ import absolute_import, division, print_function, unicode_literals
import copy
import json
import logging
from collections import OrderedDict
from . import (
fbnet_builder as mbuilder,
fbnet_modeldef as modeldef,
)
import torch.nn as nn
from lib.scene_parser.rcnn.modeling import registry
from ... | 7,854 | 30.047431 | 83 | py |
graph-rcnn.pytorch | graph-rcnn.pytorch-master/lib/scene_parser/rcnn/modeling/backbone/backbone.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
from collections import OrderedDict
from torch import nn
from .. import registry
from lib.scene_parser.rcnn.modeling.make_layers import conv_with_kaiming_uniform
from . import fpn as fpn_module
from . import resnet
@registry.BACKBONES.register(... | 2,868 | 33.566265 | 81 | py |
graph-rcnn.pytorch | graph-rcnn.pytorch-master/lib/scene_parser/rcnn/modeling/backbone/fpn.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
import torch
import torch.nn.functional as F
from torch import nn
class FPN(nn.Module):
"""
Module that adds FPN on top of a list of feature maps.
The feature maps are currently supposed to be in increasing depth
order, and must b... | 3,939 | 38.4 | 86 | py |
graph-rcnn.pytorch | graph-rcnn.pytorch-master/lib/scene_parser/rcnn/modeling/detector/generalized_rcnn.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
"""
Implements the Generalized R-CNN framework
"""
import torch
from torch import nn
from lib.scene_parser.rcnn.structures.image_list import to_image_list
from ..backbone import build_backbone
from ..rpn.rpn import build_rpn
from ..roi_heads.roi... | 2,233 | 33.369231 | 87 | py |
graph-rcnn.pytorch | graph-rcnn.pytorch-master/lib/scene_parser/rcnn/modeling/relation_heads/inference.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
import torch
import torch.nn.functional as F
from torch import nn
from lib.scene_parser.rcnn.structures.bounding_box import BoxList
from lib.scene_parser.rcnn.structures.boxlist_ops import boxlist_nms
from lib.scene_parser.rcnn.structures.boxlist_... | 6,176 | 37.12963 | 88 | py |
graph-rcnn.pytorch | graph-rcnn.pytorch-master/lib/scene_parser/rcnn/modeling/relation_heads/roi_relation_feature_extractors.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
import torch
from torch import nn
from torch.nn import functional as F
from lib.scene_parser.rcnn.modeling import registry
from lib.scene_parser.rcnn.modeling.backbone import resnet
from lib.scene_parser.rcnn.modeling.poolers import Pooler
from li... | 6,349 | 36.797619 | 95 | py |
graph-rcnn.pytorch | graph-rcnn.pytorch-master/lib/scene_parser/rcnn/modeling/relation_heads/relation_heads.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
# Relation head for predicting relationship between object pairs.
# Written by Jianwei Yang (jw2yang@gatech.edu).
import numpy as np
import torch
from torch import nn
from lib.scene_parser.rcnn.structures.bounding_box_pair import BoxPairList
from l... | 11,698 | 47.745833 | 136 | py |
graph-rcnn.pytorch | graph-rcnn.pytorch-master/lib/scene_parser/rcnn/modeling/relation_heads/roi_relation_box_predictors.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
from lib.scene_parser.rcnn.modeling import registry
from torch import nn
@registry.ROI_RELATION_BOX_PREDICTOR.register("FastRCNNPredictor")
class FastRCNNPredictor(nn.Module):
def __init__(self, config, in_channels):
super(FastRCNNPre... | 2,371 | 36.0625 | 89 | py |
graph-rcnn.pytorch | graph-rcnn.pytorch-master/lib/scene_parser/rcnn/modeling/relation_heads/loss.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
import torch
from torch.nn import functional as F
from lib.scene_parser.rcnn.layers import smooth_l1_loss
from lib.scene_parser.rcnn.modeling.box_coder import BoxCoder
from lib.scene_parser.rcnn.modeling.matcher import Matcher
from lib.scene_parse... | 13,391 | 41.514286 | 136 | py |
graph-rcnn.pytorch | graph-rcnn.pytorch-master/lib/scene_parser/rcnn/modeling/relation_heads/roi_relation_predictors.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
from lib.scene_parser.rcnn.modeling import registry
import torch
from torch import nn
@registry.ROI_RELATION_PREDICTOR.register("FastRCNNRelationPredictor")
class FastRCNNPredictor(nn.Module):
def __init__(self, config, in_channels):
... | 2,129 | 35.724138 | 94 | py |
graph-rcnn.pytorch | graph-rcnn.pytorch-master/lib/scene_parser/rcnn/modeling/relation_heads/roi_relation_box_feature_extractors.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
import torch
from torch import nn
from torch.nn import functional as F
from lib.scene_parser.rcnn.modeling import registry
from lib.scene_parser.rcnn.modeling.backbone import resnet
from lib.scene_parser.rcnn.modeling.poolers import Pooler
from li... | 5,464 | 34.953947 | 90 | py |
graph-rcnn.pytorch | graph-rcnn.pytorch-master/lib/scene_parser/rcnn/modeling/relation_heads/sparse_targets.py | import torch
import torch.nn as nn
class FrequencyBias(nn.Module):
"""
The goal of this is to provide a simplified way of computing
P(predicate | obj1, obj2, img).
"""
def __init__(self, pred_dist):
# pred_dist: [num_classes, num_classes, num_preds] numpy array
super(FrequencyBias... | 3,594 | 35.313131 | 87 | py |
graph-rcnn.pytorch | graph-rcnn.pytorch-master/lib/scene_parser/rcnn/modeling/relation_heads/imp/imp.py | # Scene Graph Generation by Iterative Message Passing
# Reimplemented by Jianwei Yang (jw2yang@gatech.edu)
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.nn import Parameter
from ..roi_relation_feature_extractors import make_roi_relation_feature_extractor
from ..roi_re... | 6,009 | 49.083333 | 129 | py |
graph-rcnn.pytorch | graph-rcnn.pytorch-master/lib/scene_parser/rcnn/modeling/relation_heads/baseline/baseline.py | # Scene Graph Generation with baseline (vanilla) model
# Reimnplemetned by Jianwei Yang (jw2yang@gatech.edu)
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.autograd import Variable
from torch.nn import Parameter
from ..roi_relation_feature_extractors import make_roi_rel... | 1,668 | 41.794872 | 99 | py |
graph-rcnn.pytorch | graph-rcnn.pytorch-master/lib/scene_parser/rcnn/modeling/relation_heads/grcnn/grcnn.py | # Graph R-CNN for scene graph generation
# Reimnplemetned by Jianwei Yang (jw2yang@gatech.edu)
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.nn import Parameter
from ..roi_relation_feature_extractors import make_roi_relation_feature_extractor
from ..roi_relation_box_f... | 7,173 | 49.521127 | 106 | py |
graph-rcnn.pytorch | graph-rcnn.pytorch-master/lib/scene_parser/rcnn/modeling/relation_heads/grcnn/agcn/agcn.py | import torch
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
import math
import time
def normal_init(m, mean, stddev, truncated=False):
if truncated:
m.weight.data.normal_().fmod_(2).mul_(stddev).add_(mean) # not a perfect approximation
else:
m.weight.data.normal_(mean,... | 3,579 | 43.75 | 110 | py |
graph-rcnn.pytorch | graph-rcnn.pytorch-master/lib/scene_parser/rcnn/modeling/relation_heads/reldn/reldn.py | # Scene Graph Generation by Iterative Message Passing
# Reimnplemetned by Jianwei Yang (jw2yang@gatech.edu)
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.nn import Parameter
from ..roi_relation_feature_extractors import make_roi_relation_feature_extractor
from ..roi_r... | 6,871 | 47.055944 | 133 | py |
graph-rcnn.pytorch | graph-rcnn.pytorch-master/lib/scene_parser/rcnn/modeling/relation_heads/reldn/spatial.py | import torch
import torch.nn as nn
import numpy as np
from lib.scene_parser.rcnn.utils.boxes import bbox_transform_inv, boxes_union
class SpatialFeature(nn.Module):
def __init__(self, cfg, dim):
super(SpatialFeature, self).__init__()
self.model = nn.Sequential(
nn.Linear(28, 64), nn.Lea... | 2,141 | 41.84 | 142 | py |
graph-rcnn.pytorch | graph-rcnn.pytorch-master/lib/scene_parser/rcnn/modeling/relation_heads/reldn/visual.py | import torch
import torch.nn as nn
class VisualFeature(nn.Module):
def __init__(self, dim):
self.subj_branch = nn.Sequential(nn.Linear())
def forward(self, subj_feat, obj_feat, rel_feat):
pass
def build_visual_feature(cfg, in_channels):
return VisualFeature(cfg, in_channels)
| 307 | 22.692308 | 53 | py |
graph-rcnn.pytorch | graph-rcnn.pytorch-master/lib/scene_parser/rcnn/modeling/relation_heads/msdn/msdn_base.py | import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.autograd import Variable
from torch.nn import Parameter
import pdb
class Message_Passing_Unit_v2(nn.Module):
def __init__(self, fea_size, filter_size = 128):
super(Message_Passing_Unit_v2, self).__init__()
self.w = n... | 4,594 | 37.291667 | 106 | py |
graph-rcnn.pytorch | graph-rcnn.pytorch-master/lib/scene_parser/rcnn/modeling/relation_heads/msdn/msdn.py | # MSDN for scene graph generation
# Reimnplemetned by Jianwei Yang (jw2yang@gatech.edu)
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.autograd import Variable
from torch.nn import Parameter
from .msdn_base import MSDN_BASE
from ..roi_relation_feature_extractors import... | 4,365 | 42.227723 | 123 | py |
graph-rcnn.pytorch | graph-rcnn.pytorch-master/lib/scene_parser/rcnn/modeling/relation_heads/relpn/utils.py | import torch
def box_pos_encoder(bboxes, width, height):
"""
bounding box encoding
"""
bboxes_enc = bboxes.clone()
dim0 = bboxes_enc[:, 0] / width
dim1 = bboxes_enc[:, 1] / height
dim2 = bboxes_enc[:, 2] / width
dim3 = bboxes_enc[:, 3] / height
dim4 = (bboxes_enc[:, 2] - bboxes_enc... | 538 | 30.705882 | 105 | py |
graph-rcnn.pytorch | graph-rcnn.pytorch-master/lib/scene_parser/rcnn/modeling/relation_heads/relpn/relpn.py | import torch
import torch.nn as nn
import torch.nn.functional as F
from lib.scene_parser.rcnn.modeling.box_coder import BoxCoder
from lib.scene_parser.rcnn.modeling.matcher import Matcher
from lib.scene_parser.rcnn.modeling.pair_matcher import PairMatcher
from lib.scene_parser.rcnn.structures.boxlist_ops import boxlist... | 17,740 | 45.080519 | 142 | py |
graph-rcnn.pytorch | graph-rcnn.pytorch-master/lib/scene_parser/rcnn/modeling/relation_heads/relpn/relationshipness.py | import torch
import torch.nn as nn
from .utils import box_pos_encoder
from ..auxilary.multi_head_att import MultiHeadAttention
class Relationshipness(nn.Module):
"""
compute relationshipness between subjects and objects
"""
def __init__(self, dim, pos_encoding=False):
super(Relationshipness, se... | 3,681 | 31.298246 | 86 | py |
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