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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PMTD | PMTD-master/maskrcnn_benchmark/structures/segmentation_mask.py | import cv2
import copy
import torch
import numpy as np
from maskrcnn_benchmark.layers.misc import interpolate
import pycocotools.mask as mask_utils
# transpose
FLIP_LEFT_RIGHT = 0
FLIP_TOP_BOTTOM = 1
""" ABSTRACT
Segmentations come in either:
1) Binary masks
2) Polygons
Binary masks can be represented in a contigu... | 17,501 | 31.411111 | 94 | py |
PMTD | PMTD-master/maskrcnn_benchmark/structures/bounding_box.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
import torch
# transpose
FLIP_LEFT_RIGHT = 0
FLIP_TOP_BOTTOM = 1
class BoxList(object):
"""
This class represents a set of bounding boxes.
The bounding boxes are represented as a Nx4 Tensor.
In order to uniquely determine the bou... | 9,646 | 35.131086 | 92 | py |
PMTD | PMTD-master/maskrcnn_benchmark/structures/boxlist_ops.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
import torch
from .bounding_box import BoxList
from maskrcnn_benchmark.layers import nms as _box_nms
def boxlist_nms(boxlist, nms_thresh, max_proposals=-1, score_field="scores"):
"""
Performs non-maximum suppression on a boxlist, with s... | 3,637 | 27.20155 | 97 | py |
PMTD | PMTD-master/maskrcnn_benchmark/structures/keypoint.py | import torch
# transpose
FLIP_LEFT_RIGHT = 0
FLIP_TOP_BOTTOM = 1
class Keypoints(object):
def __init__(self, keypoints, size, mode=None):
# FIXME remove check once we have better integration with device
# in my version this would consistently return a CPU tensor
device = keypoints.device ... | 6,555 | 33.687831 | 97 | py |
PMTD | PMTD-master/tests/checkpoint.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
from collections import OrderedDict
import os
from tempfile import TemporaryDirectory
import unittest
import torch
from torch import nn
from maskrcnn_benchmark.utils.model_serialization import load_state_dict
from maskrcnn_benchmark.utils.checkpo... | 4,645 | 38.042017 | 88 | py |
PMTD | PMTD-master/tests/test_detectors.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
import unittest
import glob
import os
import copy
import torch
from maskrcnn_benchmark.modeling.detector import build_detection_model
from maskrcnn_benchmark.structures.image_list import to_image_list
import utils
CONFIG_FILES = [
# bbox
... | 4,223 | 28.333333 | 71 | py |
PMTD | PMTD-master/tests/test_nms.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
import unittest
import numpy as np
import torch
from maskrcnn_benchmark.layers import nms as box_nms
class TestNMS(unittest.TestCase):
def test_nms_cpu(self):
""" Match unit test UtilsNMSTest.TestNMS in
caffe2/operators/... | 7,612 | 33.292793 | 76 | py |
PMTD | PMTD-master/tests/test_backbones.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
import unittest
import copy
import torch
# import modules to to register backbones
from maskrcnn_benchmark.modeling.backbone import build_backbone # NoQA
from maskrcnn_benchmark.modeling import registry
from maskrcnn_benchmark.config import cfg as... | 1,914 | 33.196429 | 75 | py |
PMTD | PMTD-master/tests/test_feature_extractors.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
import unittest
import copy
import torch
# import modules to to register feature extractors
from maskrcnn_benchmark.modeling.backbone import build_backbone # NoQA
from maskrcnn_benchmark.modeling.roi_heads.roi_heads import build_roi_heads # NoQA
f... | 3,070 | 31.670213 | 82 | py |
PMTD | PMTD-master/tests/test_fbnet.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
import unittest
import numpy as np
import torch
import maskrcnn_benchmark.modeling.backbone.fbnet_builder as fbnet_builder
TEST_CUDA = torch.cuda.is_available()
def _test_primitive(self, device, op_name, op_func, N, C_in, C_out, expand, strid... | 2,845 | 32.482353 | 84 | py |
PMTD | PMTD-master/tests/test_predictors.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
import unittest
import copy
import torch
# import modules to to register predictors
from maskrcnn_benchmark.modeling.backbone import build_backbone # NoQA
from maskrcnn_benchmark.modeling.roi_heads.roi_heads import build_roi_heads # NoQA
from mask... | 3,214 | 31.474747 | 82 | py |
PMTD | PMTD-master/tests/test_data_samplers.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
import itertools
import random
import unittest
from torch.utils.data.sampler import BatchSampler
from torch.utils.data.sampler import Sampler
from torch.utils.data.sampler import SequentialSampler
from torch.utils.data.sampler import RandomSampler... | 5,532 | 34.928571 | 88 | py |
PMTD | PMTD-master/tests/test_rpn_heads.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
import unittest
import copy
import torch
# import modules to to register rpn heads
from maskrcnn_benchmark.modeling.backbone import build_backbone # NoQA
from maskrcnn_benchmark.modeling.rpn.rpn import build_rpn # NoQA
from maskrcnn_benchmark.mode... | 1,992 | 30.634921 | 71 | py |
PMTD | PMTD-master/tests/test_segmentation_mask.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
import unittest
import torch
from maskrcnn_benchmark.structures.segmentation_mask import SegmentationMask
class TestSegmentationMask(unittest.TestCase):
def __init__(self, method_name='runTest'):
super(TestSegmentationMask, self).__in... | 2,419 | 31.266667 | 76 | py |
PMTD | PMTD-master/tests/test_box_coder.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
import unittest
import numpy as np
import torch
from maskrcnn_benchmark.modeling.box_coder import BoxCoder
class TestBoxCoder(unittest.TestCase):
def test_box_decoder(self):
""" Match unit test UtilsBoxesTest.TestBboxTransformRandom... | 3,112 | 27.3 | 80 | py |
PMTD | PMTD-master/demo/inference.py | import collections
import logging
import os
import torch
from tqdm import tqdm
from maskrcnn_benchmark.modeling.roi_heads.mask_head.inference import Masker
logger = logging.getLogger(__name__)
MASK_SCALE = 28
class PlaneClustering(Masker):
def __init__(self):
super().__init__()
assist_info = to... | 6,265 | 35.011494 | 104 | py |
PMTD | PMTD-master/demo/PMTD_predictor.py | import cv2
import numpy as np
from torchvision import transforms as T
from demo.inference import PlaneClustering
from demo.predictor import COCODemo
from demo.transforms import Resize
from maskrcnn_benchmark.modeling.roi_heads.mask_head.inference import Masker
from maskrcnn_benchmark.utils import cv2_util
class PMTD... | 2,226 | 32.238806 | 78 | py |
PMTD | PMTD-master/demo/predictor.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
import cv2
import torch
from torchvision import transforms as T
from maskrcnn_benchmark import layers as L
from maskrcnn_benchmark.modeling.detector import build_detection_model
from maskrcnn_benchmark.modeling.roi_heads.mask_head.inference import... | 15,317 | 33.813636 | 86 | py |
PMTD | PMTD-master/demo/utils/convert_results_to_icdar.py | import collections
import os
import zipfile
from typing import List, Dict, DefaultDict
from zipfile import ZipFile
import cv2
import numpy as np
import pyclipper
import torch
def get_results(path_list, merge_path, image_num, regenerate=True):
if regenerate or not os.path.exists(merge_path):
results_merge... | 4,226 | 36.078947 | 118 | py |
MR-GNN | MR-GNN-master/MR-GNN/para_test9_coLSTM.py | # based on para9
# concat to matmul
# concat in the end of matmul, different from para17
import deepchem as dc
from graph_topology import GraphTopology
import os
import tensorflow as tf
import numpy as np
# import models.layers as mylayer
import argparse
import layers_keras as mylayers
from data_load_2 import load_int... | 29,088 | 43.890432 | 177 | py |
MR-GNN | MR-GNN-master/MR-GNN/MR_GNN+memory_network.py | # based on para9
# concat to matmul
# concat in the end of matmul, different from para17
from graph_topology import GraphTopology
import os
import tensorflow as tf
import numpy as np
import argparse
import layers_keras as mylayers
from data_load_2 import load_interaction_data
from graph_topology import merge_dicts
imp... | 20,647 | 42.931915 | 135 | py |
MR-GNN | MR-GNN-master/MR-GNN/layers_keras.py | """Custom Keras Layers.
"""
from __future__ import print_function
from __future__ import division
from __future__ import unicode_literals
__author__ = "Han Altae-Tran and Bharath Ramsundar"
__copyright__ = "Copyright 2016, Stanford University"
__license__ = "MIT"
import warnings
import numpy as np
import tensorflow a... | 25,156 | 33.940278 | 165 | py |
MR-GNN | MR-GNN-master/MR-GNN/graph_topology.py | """Manages Placeholders for Graph convolution networks.
"""
from __future__ import print_function
from __future__ import division
from __future__ import unicode_literals
import warnings
import numpy as np
import tensorflow as tf
from keras.layers import Input
from deepchem.feat.mol_graphs import ConvMol
def merge_tw... | 19,880 | 32.190317 | 83 | py |
ns3-gym | ns3-gym-master/src/opengym/examples/interference-pattern/cognitive-agent-v1.py | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
import gym
import tensorflow as tf
import tensorflow.contrib.slim as slim
import numpy as np
import matplotlib as mpl
import matplotlib.pyplot as plt
from tensorflow import keras
from ns3gym import ns3env
env = gym.make('ns3-v0')
ob_space = env.observation_space
ac_space... | 2,948 | 27.355769 | 105 | py |
ns3-gym | ns3-gym-master/scratch/linear-mesh/dqn-agent-v1.py | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
import scipy.io as io
import gym
import tensorflow as tf
import tensorflow.contrib.slim as slim
import numpy as np
import matplotlib.pyplot as plt
from tensorflow import keras
from ns3gym import ns3env
class DqnAgent(object):
"""docstring for DqnAgent"""
def __i... | 5,127 | 28.813953 | 112 | py |
ns3-gym | ns3-gym-master/scratch/linear-mesh/dqn-agent-v2.py | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
import scipy.io as io
import gym
import tensorflow as tf
import tensorflow.contrib.slim as slim
import numpy as np
import matplotlib.pyplot as plt
from tensorflow import keras
from ns3gym import ns3env
class DqnAgent(object):
"""docstring for DqnAgent"""
def __i... | 5,181 | 29.127907 | 112 | py |
ns3-gym | ns3-gym-master/scratch/interference-pattern/cognitive-agent-v1.py | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
import gym
import tensorflow as tf
import tensorflow.contrib.slim as slim
import numpy as np
import matplotlib as mpl
import matplotlib.pyplot as plt
from tensorflow import keras
from ns3gym import ns3env
env = gym.make('ns3-v0')
ob_space = env.observation_space
ac_space... | 2,948 | 27.355769 | 105 | py |
Counterfactual-benchmark | Counterfactual-benchmark-main/lore/LOREWrapper.py | import tensorflow as tf
class LOREWarpper():
def __init__(self, model: tf.keras.Model):
super(LOREWarpper, self).__init__()
self.model = model
self.all_input = []
def predict(self, x):
self.all_input.append(x)
out = self.model(x)[:, 1:2]
return (out > .5).numpy(... | 343 | 27.666667 | 55 | py |
Counterfactual-benchmark | Counterfactual-benchmark-main/lore/lime/lime_tabular.py | """
Functions for explaining classifiers that use tabular data (matrices).
"""
import collections
import copy
import json
import warnings
import numpy as np
import sklearn
import sklearn.preprocessing
from sklearn.utils import check_random_state
from lime.discretize import QuartileDiscretizer
from lime.discretize imp... | 26,281 | 44.867365 | 94 | py |
Counterfactual-benchmark | Counterfactual-benchmark-main/dice/DiCESingleNeuronOutputWrapper.py | import tensorflow as tf
class DiCESingleNeuronOutputWrapper(tf.keras.layers.Layer):
def __init__(self, model):
super(DiCESingleNeuronOutputWrapper, self).__init__()
self.model = model
def call(self, inputs):
return self.model(inputs)[:, 1:2] | 275 | 29.666667 | 61 | py |
Counterfactual-benchmark | Counterfactual-benchmark-main/utils/load.py | from enum import Enum
from io import UnsupportedOperation
import pandas as pd
import numpy as np
from sklearn.preprocessing import OneHotEncoder, MinMaxScaler
from tensorflow.keras.models import model_from_json
from utils.exceptions import UnsupportedDataset
class SelectableDataset(Enum):
Diabetes = "Diabetes"
... | 3,943 | 32.423729 | 101 | py |
Counterfactual-benchmark | Counterfactual-benchmark-main/utils/models.py | import pickle
import tensorflow as tf
import numpy as np
from sklearn.tree import DecisionTreeClassifier
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import recall_score, precision_score, accuracy_score, f1_score, confusion_matrix
import matplotlib.pyplot as plt
import os
def train_thre... | 6,157 | 34.595376 | 130 | py |
Counterfactual-benchmark | Counterfactual-benchmark-main/surrogate/wrapper.py | import tensorflow as tf
class SurrogateWrapper():
def __init__(self, model: tf.keras.Model):
super(SurrogateWrapper, self).__init__()
self.model = model
self.all_input = []
def predict(self, x):
self.all_input.append(x)
out = self.model(x)[:, 1:2]
return out
... | 374 | 27.846154 | 57 | py |
deep-person-reid | deep-person-reid-master/setup.py | import numpy as np
import os.path as osp
from setuptools import setup, find_packages
from distutils.extension import Extension
from Cython.Build import cythonize
def readme():
with open('README.rst') as f:
content = f.read()
return content
def find_version():
version_file = 'torchreid/__init__.p... | 1,504 | 24.948276 | 78 | py |
deep-person-reid | deep-person-reid-master/tools/export.py | import argparse
import os
import sys
import numpy as np
from pathlib import Path
import torch
import pandas as pd
import subprocess
from torchreid.utils.feature_extractor import FeatureExtractor
from torchreid.models import build_model
__model_types = [
'resnet50', 'mlfn', 'hacnn', 'mobilenetv2_x1_0', 'mobilenetv... | 6,841 | 32.37561 | 131 | py |
deep-person-reid | deep-person-reid-master/tools/visualize_actmap.py | """Visualizes CNN activation maps to see where the CNN focuses on to extract features.
Reference:
- Zagoruyko and Komodakis. Paying more attention to attention: Improving the
performance of convolutional neural networks via attention transfer. ICLR, 2017
- Zhou et al. Omni-Scale Feature Learning for Pers... | 5,959 | 33.252874 | 92 | py |
deep-person-reid | deep-person-reid-master/tools/parse_test_res.py | """
This script aims to automate the process of calculating average results
stored in the test.log files over multiple splits.
How to use:
For example, you have done evaluation over 20 splits on VIPeR, leading to
the following file structure
log/
eval_viper/
split_0/
test.log-xxxx
spli... | 2,976 | 27.625 | 82 | py |
deep-person-reid | deep-person-reid-master/tools/compute_mean_std.py | """
Compute channel-wise mean and standard deviation of a dataset.
Usage:
$ python compute_mean_std.py DATASET_ROOT DATASET_KEY
- The first argument points to the root path where you put the datasets.
- The second argument means the specific dataset key.
For instance, your datasets are put under $DATA and you wanna
... | 1,509 | 24.166667 | 72 | py |
deep-person-reid | deep-person-reid-master/torchreid/__init__.py | from __future__ import print_function, absolute_import
from torchreid import data, optim, utils, engine, losses, models, metrics
__version__ = '1.4.0'
__author__ = 'Kaiyang Zhou'
__homepage__ = 'https://kaiyangzhou.github.io/'
__description__ = 'Deep learning person re-identification in PyTorch'
__url__ = 'https://gi... | 359 | 35 | 73 | py |
deep-person-reid | deep-person-reid-master/torchreid/models/shufflenetv2.py | """
Code source: https://github.com/pytorch/vision
"""
from __future__ import division, absolute_import
import torch
import torch.utils.model_zoo as model_zoo
from torch import nn
__all__ = [
'shufflenet_v2_x0_5', 'shufflenet_v2_x1_0', 'shufflenet_v2_x1_5',
'shufflenet_v2_x2_0'
]
model_urls = {
'shufflene... | 8,011 | 29.463878 | 103 | py |
deep-person-reid | deep-person-reid-master/torchreid/models/mudeep.py | from __future__ import division, absolute_import
import torch
from torch import nn
from torch.nn import functional as F
__all__ = ['MuDeep']
class ConvBlock(nn.Module):
"""Basic convolutional block.
convolution + batch normalization + relu.
Args:
in_c (int): number of input channels.
... | 6,297 | 29.425121 | 80 | py |
deep-person-reid | deep-person-reid-master/torchreid/models/inceptionv4.py | from __future__ import division, absolute_import
import torch
import torch.nn as nn
import torch.utils.model_zoo as model_zoo
__all__ = ['inceptionv4']
"""
Code imported from https://github.com/Cadene/pretrained-models.pytorch
"""
pretrained_settings = {
'inceptionv4': {
'imagenet': {
'url':
... | 11,271 | 28.507853 | 86 | py |
deep-person-reid | deep-person-reid-master/torchreid/models/inceptionresnetv2.py | """
Code imported from https://github.com/Cadene/pretrained-models.pytorch
"""
from __future__ import division, absolute_import
import torch
import torch.nn as nn
import torch.utils.model_zoo as model_zoo
__all__ = ['inceptionresnetv2']
pretrained_settings = {
'inceptionresnetv2': {
'imagenet': {
... | 11,194 | 29.925414 | 89 | py |
deep-person-reid | deep-person-reid-master/torchreid/models/resnet.py | """
Code source: https://github.com/pytorch/vision
"""
from __future__ import division, absolute_import
import torch.utils.model_zoo as model_zoo
from torch import nn
__all__ = [
'resnet18', 'resnet34', 'resnet50', 'resnet101', 'resnet152',
'resnext50_32x4d', 'resnext101_32x8d', 'resnet50_fc512'
]
model_urls ... | 15,154 | 27.54049 | 106 | py |
deep-person-reid | deep-person-reid-master/torchreid/models/mobilenetv2.py | from __future__ import division, absolute_import
import torch.utils.model_zoo as model_zoo
from torch import nn
from torch.nn import functional as F
__all__ = ['mobilenetv2_x1_0', 'mobilenetv2_x1_4']
model_urls = {
# 1.0: top-1 71.3
'mobilenetv2_x1_0':
'https://mega.nz/#!NKp2wAIA!1NH1pbNzY_M2hVk_hdsxNM1NU... | 8,387 | 29.501818 | 99 | py |
deep-person-reid | deep-person-reid-master/torchreid/models/squeezenet.py | """
Code source: https://github.com/pytorch/vision
"""
from __future__ import division, absolute_import
import torch
import torch.nn as nn
import torch.utils.model_zoo as model_zoo
__all__ = ['squeezenet1_0', 'squeezenet1_1', 'squeezenet1_0_fc512']
model_urls = {
'squeezenet1_0':
'https://download.pytorch.org... | 7,617 | 31.14346 | 99 | py |
deep-person-reid | deep-person-reid-master/torchreid/models/mlfn.py | from __future__ import division, absolute_import
import torch
import torch.utils.model_zoo as model_zoo
from torch import nn
from torch.nn import functional as F
__all__ = ['mlfn']
model_urls = {
# training epoch = 5, top1 = 51.6
'imagenet':
'https://mega.nz/#!YHxAhaxC!yu9E6zWl0x5zscSouTdbZu8gdFFytDdl-RAd... | 8,420 | 30.188889 | 86 | py |
deep-person-reid | deep-person-reid-master/torchreid/models/osnet_ain.py | from __future__ import division, absolute_import
import warnings
import torch
from torch import nn
from torch.nn import functional as F
__all__ = [
'osnet_ain_x1_0', 'osnet_ain_x0_75', 'osnet_ain_x0_5', 'osnet_ain_x0_25'
]
pretrained_urls = {
'osnet_ain_x1_0':
'https://drive.google.com/uc?id=1-CaioD9NaqbH... | 17,731 | 28.068852 | 91 | py |
deep-person-reid | deep-person-reid-master/torchreid/models/densenet.py | """
Code source: https://github.com/pytorch/vision
"""
from __future__ import division, absolute_import
import re
from collections import OrderedDict
import torch
import torch.nn as nn
from torch.nn import functional as F
from torch.utils import model_zoo
__all__ = [
'densenet121', 'densenet169', 'densenet201', 'd... | 11,627 | 29.519685 | 104 | py |
deep-person-reid | deep-person-reid-master/torchreid/models/senet.py | from __future__ import division, absolute_import
import math
from collections import OrderedDict
import torch.nn as nn
from torch.utils import model_zoo
__all__ = [
'senet154', 'se_resnet50', 'se_resnet101', 'se_resnet152',
'se_resnext50_32x4d', 'se_resnext101_32x4d', 'se_resnet50_fc512'
]
"""
Code imported fr... | 20,684 | 29.021771 | 91 | py |
deep-person-reid | deep-person-reid-master/torchreid/models/shufflenet.py | from __future__ import division, absolute_import
import torch
import torch.utils.model_zoo as model_zoo
from torch import nn
from torch.nn import functional as F
__all__ = ['shufflenet']
model_urls = {
# training epoch = 90, top1 = 61.8
'imagenet':
'https://mega.nz/#!RDpUlQCY!tr_5xBEkelzDjveIYBBcGcovNCOrg... | 6,264 | 30.482412 | 86 | py |
deep-person-reid | deep-person-reid-master/torchreid/models/nasnet.py | from __future__ import division, absolute_import
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.model_zoo as model_zoo
__all__ = ['nasnetamobile']
"""
NASNet Mobile
Thanks to Anastasiia (https://github.com/DagnyT) for the great help, support and motivation!
--------------------... | 36,186 | 30.967314 | 137 | py |
deep-person-reid | deep-person-reid-master/torchreid/models/__init__.py | from __future__ import absolute_import
import torch
from .pcb import *
from .mlfn import *
from .hacnn import *
from .osnet import *
from .senet import *
from .mudeep import *
from .nasnet import *
from .resnet import *
from .densenet import *
from .xception import *
from .osnet_ain import *
from .resnetmid import *
f... | 3,642 | 28.617886 | 81 | py |
deep-person-reid | deep-person-reid-master/torchreid/models/xception.py | from __future__ import division, absolute_import
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.model_zoo as model_zoo
__all__ = ['xception']
pretrained_settings = {
'xception': {
'imagenet': {
'url':
'http://data.lip6.fr/cadene/pretrainedmodels/xception-4... | 9,687 | 27.081159 | 124 | py |
deep-person-reid | deep-person-reid-master/torchreid/models/resnet_ibn_a.py | """
Credit to https://github.com/XingangPan/IBN-Net.
"""
from __future__ import division, absolute_import
import math
import torch
import torch.nn as nn
import torch.utils.model_zoo as model_zoo
__all__ = ['resnet50_ibn_a']
model_urls = {
'resnet50': 'https://download.pytorch.org/models/resnet50-19c8e357.pth',
... | 8,598 | 28.651724 | 99 | py |
deep-person-reid | deep-person-reid-master/torchreid/models/resnet_ibn_b.py | """
Credit to https://github.com/XingangPan/IBN-Net.
"""
from __future__ import division, absolute_import
import math
import torch.nn as nn
import torch.utils.model_zoo as model_zoo
__all__ = ['resnet50_ibn_b']
model_urls = {
'resnet50': 'https://download.pytorch.org/models/resnet50-19c8e357.pth',
'resnet101'... | 8,261 | 29.043636 | 99 | py |
deep-person-reid | deep-person-reid-master/torchreid/models/osnet.py | from __future__ import division, absolute_import
import warnings
import torch
from torch import nn
from torch.nn import functional as F
__all__ = [
'osnet_x1_0', 'osnet_x0_75', 'osnet_x0_5', 'osnet_x0_25', 'osnet_ibn_x1_0'
]
pretrained_urls = {
'osnet_x1_0':
'https://drive.google.com/uc?id=1LaG1EJpHrxdAxK... | 17,037 | 27.444073 | 108 | py |
deep-person-reid | deep-person-reid-master/torchreid/models/resnetmid.py | from __future__ import division, absolute_import
import torch
import torch.utils.model_zoo as model_zoo
from torch import nn
__all__ = ['resnet50mid']
model_urls = {
'resnet18': 'https://download.pytorch.org/models/resnet18-5c106cde.pth',
'resnet34': 'https://download.pytorch.org/models/resnet34-333f7ec4.pth'... | 9,165 | 28.75974 | 99 | py |
deep-person-reid | deep-person-reid-master/torchreid/models/pcb.py | from __future__ import division, absolute_import
import torch.utils.model_zoo as model_zoo
from torch import nn
from torch.nn import functional as F
__all__ = ['pcb_p6', 'pcb_p4']
model_urls = {
'resnet18': 'https://download.pytorch.org/models/resnet18-5c106cde.pth',
'resnet34': 'https://download.pytorch.org/... | 9,125 | 27.971429 | 86 | py |
deep-person-reid | deep-person-reid-master/torchreid/models/hacnn.py | from __future__ import division, absolute_import
import torch
from torch import nn
from torch.nn import functional as F
__all__ = ['HACNN']
class ConvBlock(nn.Module):
"""Basic convolutional block.
convolution + batch normalization + relu.
Args:
in_c (int): number of input channels.
... | 13,761 | 32.161446 | 105 | py |
deep-person-reid | deep-person-reid-master/torchreid/optim/lr_scheduler.py | from __future__ import print_function, absolute_import
import torch
AVAI_SCH = ['single_step', 'multi_step', 'cosine']
def build_lr_scheduler(
optimizer, lr_scheduler='single_step', stepsize=1, gamma=0.1, max_epoch=1
):
"""A function wrapper for building a learning rate scheduler.
Args:
optimize... | 2,461 | 34.681159 | 97 | py |
deep-person-reid | deep-person-reid-master/torchreid/optim/radam.py | """
Imported from: https://github.com/LiyuanLucasLiu/RAdam
Paper: https://arxiv.org/abs/1908.03265
@article{liu2019radam,
title={On the Variance of the Adaptive Learning Rate and Beyond},
author={Liu, Liyuan and Jiang, Haoming and He, Pengcheng and Chen, Weizhu and Liu, Xiaodong and Gao, Jianfeng and Han, Jiawei}... | 11,626 | 34.126888 | 129 | py |
deep-person-reid | deep-person-reid-master/torchreid/optim/optimizer.py | from __future__ import print_function, absolute_import
import warnings
import torch
import torch.nn as nn
from .radam import RAdam
AVAI_OPTIMS = ['adam', 'amsgrad', 'sgd', 'rmsprop', 'radam']
def build_optimizer(
model,
optim='adam',
lr=0.0003,
weight_decay=5e-04,
momentum=0.9,
sgd_dampening... | 5,307 | 32.594937 | 97 | py |
deep-person-reid | deep-person-reid-master/torchreid/metrics/rank.py | from __future__ import division, print_function, absolute_import
import numpy as np
import warnings
from collections import defaultdict
try:
from torchreid.metrics.rank_cylib.rank_cy import evaluate_cy
IS_CYTHON_AVAI = True
except ImportError:
IS_CYTHON_AVAI = False
warnings.warn(
'Cython evalu... | 6,955 | 32.442308 | 112 | py |
deep-person-reid | deep-person-reid-master/torchreid/metrics/accuracy.py | from __future__ import division, print_function, absolute_import
def accuracy(output, target, topk=(1, )):
"""Computes the accuracy over the k top predictions for
the specified values of k.
Args:
output (torch.Tensor): prediction matrix with shape (batch_size, num_classes).
target (torch.... | 1,134 | 28.868421 | 86 | py |
deep-person-reid | deep-person-reid-master/torchreid/metrics/distance.py | from __future__ import division, print_function, absolute_import
import torch
from torch.nn import functional as F
def compute_distance_matrix(input1, input2, metric='euclidean'):
"""A wrapper function for computing distance matrix.
Args:
input1 (torch.Tensor): 2-D feature matrix.
input2 (tor... | 2,446 | 29.209877 | 73 | py |
deep-person-reid | deep-person-reid-master/torchreid/metrics/rank_cylib/test_cython.py | from __future__ import print_function
import sys
import numpy as np
import timeit
import os.path as osp
from torchreid import metrics
sys.path.insert(0, osp.dirname(osp.abspath(__file__)) + '/../../..')
"""
Test the speed of cython-based evaluation code. The speed improvements
can be much bigger when using the real r... | 2,746 | 31.702381 | 125 | py |
deep-person-reid | deep-person-reid-master/torchreid/engine/engine.py | from __future__ import division, print_function, absolute_import
import time
import numpy as np
import os.path as osp
import datetime
from collections import OrderedDict
import torch
from torch.nn import functional as F
from torch.utils.tensorboard import SummaryWriter
from torchreid import metrics
from torchreid.util... | 17,368 | 35.26096 | 103 | py |
deep-person-reid | deep-person-reid-master/torchreid/engine/video/softmax.py | from __future__ import division, print_function, absolute_import
import torch
from torchreid.engine.image import ImageSoftmaxEngine
class VideoSoftmaxEngine(ImageSoftmaxEngine):
"""Softmax-loss engine for video-reid.
Args:
datamanager (DataManager): an instance of ``torchreid.data.ImageDataManager``... | 3,479 | 30.636364 | 93 | py |
deep-person-reid | deep-person-reid-master/torchreid/engine/video/triplet.py | from __future__ import division, print_function, absolute_import
import torch
from torchreid.engine.image import ImageTripletEngine
class VideoTripletEngine(ImageTripletEngine):
"""Triplet-loss engine for video-reid.
Args:
datamanager (DataManager): an instance of ``torchreid.data.ImageDataManager``... | 4,038 | 31.837398 | 93 | py |
deep-person-reid | deep-person-reid-master/torchreid/engine/image/softmax.py | from __future__ import division, print_function, absolute_import
from torchreid import metrics
from torchreid.losses import CrossEntropyLoss
from ..engine import Engine
class ImageSoftmaxEngine(Engine):
r"""Softmax-loss engine for image-reid.
Args:
datamanager (DataManager): an instance of ``torchr... | 2,860 | 28.193878 | 93 | py |
deep-person-reid | deep-person-reid-master/torchreid/engine/image/triplet.py | from __future__ import division, print_function, absolute_import
from torchreid import metrics
from torchreid.losses import TripletLoss, CrossEntropyLoss
from ..engine import Engine
class ImageTripletEngine(Engine):
r"""Triplet-loss engine for image-reid.
Args:
datamanager (DataManager): an instanc... | 3,877 | 30.528455 | 93 | py |
deep-person-reid | deep-person-reid-master/torchreid/utils/torchtools.py | from __future__ import division, print_function, absolute_import
import pickle
import shutil
import os.path as osp
import warnings
from functools import partial
from collections import OrderedDict
import torch
import torch.nn as nn
from .tools import mkdir_if_missing
__all__ = [
'save_checkpoint', 'load_checkpoin... | 9,672 | 29.904153 | 91 | py |
deep-person-reid | deep-person-reid-master/torchreid/utils/avgmeter.py | from __future__ import division, absolute_import
from collections import defaultdict
import torch
__all__ = ['AverageMeter', 'MetricMeter']
class AverageMeter(object):
"""Computes and stores the average and current value.
Examples::
>>> # Initialize a meter to record loss
>>> losses = Averag... | 1,983 | 25.810811 | 71 | py |
deep-person-reid | deep-person-reid-master/torchreid/utils/feature_extractor.py | from __future__ import absolute_import
import numpy as np
import torch
import torchvision.transforms as T
from PIL import Image
from torchreid.utils import (
check_isfile, load_pretrained_weights, compute_model_complexity
)
from torchreid.models import build_model
class FeatureExtractor(object):
"""A simple ... | 4,605 | 29.104575 | 83 | py |
deep-person-reid | deep-person-reid-master/torchreid/utils/tools.py | from __future__ import division, print_function, absolute_import
import os
import sys
import json
import time
import errno
import numpy as np
import random
import os.path as osp
import warnings
import PIL
import torch
from PIL import Image
__all__ = [
'mkdir_if_missing', 'check_isfile', 'read_json', 'write_json',
... | 3,556 | 23.701389 | 90 | py |
deep-person-reid | deep-person-reid-master/torchreid/utils/loggers.py | from __future__ import absolute_import
import os
import sys
import os.path as osp
from .tools import mkdir_if_missing
__all__ = ['Logger', 'RankLogger']
class Logger(object):
"""Writes console output to external text file.
Imported from `<https://github.com/Cysu/open-reid/blob/master/reid/utils/logging.py>... | 4,373 | 28.755102 | 90 | py |
deep-person-reid | deep-person-reid-master/torchreid/utils/__init__.py | from __future__ import absolute_import
from .tools import *
from .rerank import re_ranking
from .loggers import *
from .avgmeter import *
from .reidtools import *
from .torchtools import *
from .model_complexity import compute_model_complexity
from .feature_extractor import FeatureExtractor
| 293 | 25.727273 | 54 | py |
deep-person-reid | deep-person-reid-master/torchreid/utils/model_complexity.py | from __future__ import division, print_function, absolute_import
import math
import numpy as np
from itertools import repeat
from collections import namedtuple, defaultdict
import torch
__all__ = ['compute_model_complexity']
"""
Utility
"""
def _ntuple(n):
def parse(x):
if isinstance(x, int):
... | 9,470 | 25.019231 | 101 | py |
deep-person-reid | deep-person-reid-master/torchreid/utils/GPU-Re-Ranking/main.py | """
Understanding Image Retrieval Re-Ranking: A Graph Neural Network Perspective
Xuanmeng Zhang, Minyue Jiang, Zhedong Zheng, Xiao Tan, Errui Ding, Yi Yang
Project Page : https://github.com/Xuanmeng-Zhang/gnn-re-ranking
Paper: https://arxiv.org/abs/2012.07620v2
==================================... | 1,998 | 26.383562 | 91 | py |
deep-person-reid | deep-person-reid-master/torchreid/utils/GPU-Re-Ranking/utils.py | """
Understanding Image Retrieval Re-Ranking: A Graph Neural Network Perspective
Xuanmeng Zhang, Minyue Jiang, Zhedong Zheng, Xiao Tan, Errui Ding, Yi Yang
Project Page : https://github.com/Xuanmeng-Zhang/gnn-re-ranking
Paper: https://arxiv.org/abs/2012.07620v2
==================================... | 3,691 | 25.753623 | 91 | py |
deep-person-reid | deep-person-reid-master/torchreid/utils/GPU-Re-Ranking/gnn_reranking.py | """
Understanding Image Retrieval Re-Ranking: A Graph Neural Network Perspective
Xuanmeng Zhang, Minyue Jiang, Zhedong Zheng, Xiao Tan, Errui Ding, Yi Yang
Project Page : https://github.com/Xuanmeng-Zhang/gnn-re-ranking
Paper: https://arxiv.org/abs/2012.07620v2
==================================... | 1,804 | 29.083333 | 91 | py |
deep-person-reid | deep-person-reid-master/torchreid/utils/GPU-Re-Ranking/extension/propagation/setup.py | """
Understanding Image Retrieval Re-Ranking: A Graph Neural Network Perspective
Xuanmeng Zhang, Minyue Jiang, Zhedong Zheng, Xiao Tan, Errui Ding, Yi Yang
Project Page : https://github.com/Xuanmeng-Zhang/gnn-re-ranking
Paper: https://arxiv.org/abs/2012.07620v2
==================================... | 1,200 | 31.459459 | 91 | py |
deep-person-reid | deep-person-reid-master/torchreid/utils/GPU-Re-Ranking/extension/adjacency_matrix/setup.py | """
Understanding Image Retrieval Re-Ranking: A Graph Neural Network Perspective
Xuanmeng Zhang, Minyue Jiang, Zhedong Zheng, Xiao Tan, Errui Ding, Yi Yang
Project Page : https://github.com/Xuanmeng-Zhang/gnn-re-ranking
Paper: https://arxiv.org/abs/2012.07620v2
==================================... | 1,236 | 32.432432 | 91 | py |
deep-person-reid | deep-person-reid-master/torchreid/data/sampler.py | from __future__ import division, absolute_import
import copy
import numpy as np
import random
from collections import defaultdict
from torch.utils.data.sampler import Sampler, RandomSampler, SequentialSampler
AVAI_SAMPLERS = [
'RandomIdentitySampler', 'SequentialSampler', 'RandomSampler',
'RandomDomainSampler'... | 8,417 | 33.219512 | 90 | py |
deep-person-reid | deep-person-reid-master/torchreid/data/datamanager.py | from __future__ import division, print_function, absolute_import
import torch
from torchreid.data.sampler import build_train_sampler
from torchreid.data.datasets import init_image_dataset, init_video_dataset
from torchreid.data.transforms import build_transforms
class DataManager(object):
r"""Base data manager.
... | 20,212 | 35.41982 | 104 | py |
deep-person-reid | deep-person-reid-master/torchreid/data/transforms.py | from __future__ import division, print_function, absolute_import
import math
import random
from collections import deque
import torch
from PIL import Image
from torchvision.transforms import (
Resize, Compose, ToTensor, Normalize, ColorJitter, RandomHorizontalFlip
)
class Random2DTranslation(object):
"""Rando... | 10,596 | 31.406728 | 97 | py |
deep-person-reid | deep-person-reid-master/torchreid/data/datasets/dataset.py | from __future__ import division, print_function, absolute_import
import copy
import numpy as np
import os.path as osp
import tarfile
import zipfile
import torch
from torchreid.utils import read_image, download_url, mkdir_if_missing
class Dataset(object):
"""An abstract class representing a Dataset.
This is ... | 16,244 | 32.63354 | 86 | py |
deep-person-reid | deep-person-reid-master/torchreid/data/datasets/__init__.py | from __future__ import print_function, absolute_import
from .image import (
GRID, PRID, CUHK01, CUHK02, CUHK03, MSMT17, CUHKSYSU, VIPeR, SenseReID,
Market1501, DukeMTMCreID, University1652, iLIDS
)
from .video import PRID2011, Mars, DukeMTMCVidReID, iLIDSVID
from .dataset import Dataset, ImageDataset, VideoDat... | 3,534 | 28.458333 | 75 | py |
deep-person-reid | deep-person-reid-master/torchreid/data/datasets/video/prid2011.py | from __future__ import division, print_function, absolute_import
import glob
import os.path as osp
from torchreid.utils import read_json
from ..dataset import VideoDataset
class PRID2011(VideoDataset):
"""PRID2011.
Reference:
Hirzer et al. Person Re-Identification by Descriptive and
Discrim... | 2,824 | 33.876543 | 93 | py |
deep-person-reid | deep-person-reid-master/torchreid/data/datasets/video/dukemtmcvidreid.py | from __future__ import division, print_function, absolute_import
import glob
import os.path as osp
import warnings
from torchreid.utils import read_json, write_json
from ..dataset import VideoDataset
class DukeMTMCVidReID(VideoDataset):
"""DukeMTMCVidReID.
Reference:
- Ristani et al. Performance Me... | 4,725 | 35.635659 | 87 | py |
deep-person-reid | deep-person-reid-master/torchreid/data/datasets/video/ilidsvid.py | from __future__ import division, print_function, absolute_import
import glob
import os.path as osp
from scipy.io import loadmat
from torchreid.utils import read_json, write_json
from ..dataset import VideoDataset
class iLIDSVID(VideoDataset):
"""iLIDS-VID.
Reference:
Wang et al. Person Re-Identific... | 5,368 | 36.284722 | 102 | py |
deep-person-reid | deep-person-reid-master/torchreid/data/datasets/image/viper.py | from __future__ import division, print_function, absolute_import
import glob
import numpy as np
import os.path as osp
from torchreid.utils import read_json, write_json
from ..dataset import ImageDataset
class VIPeR(ImageDataset):
"""VIPeR.
Reference:
Gray et al. Evaluating appearance models for rec... | 5,021 | 37.930233 | 105 | py |
deep-person-reid | deep-person-reid-master/torchreid/data/datasets/image/university1652.py | from __future__ import division, print_function, absolute_import
import os
import glob
import os.path as osp
import gdown
from ..dataset import ImageDataset
class University1652(ImageDataset):
"""University-1652.
Reference:
- Zheng et al. University-1652: A Multi-view Multi-source Benchmark for Dron... | 4,135 | 36.261261 | 142 | py |
deep-person-reid | deep-person-reid-master/torchreid/data/datasets/image/cuhk01.py | from __future__ import division, print_function, absolute_import
import glob
import numpy as np
import os.path as osp
import zipfile
from torchreid.utils import read_json, write_json
from ..dataset import ImageDataset
class CUHK01(ImageDataset):
"""CUHK01.
Reference:
Li et al. Human Reidentificatio... | 4,951 | 34.884058 | 85 | py |
deep-person-reid | deep-person-reid-master/torchreid/data/datasets/image/prid.py | from __future__ import division, print_function, absolute_import
import random
import os.path as osp
from torchreid.utils import read_json, write_json
from ..dataset import ImageDataset
class PRID(ImageDataset):
"""PRID (single-shot version of prid-2011)
Reference:
Hirzer et al. Person Re-Identific... | 3,955 | 35.62963 | 93 | py |
deep-person-reid | deep-person-reid-master/torchreid/data/datasets/image/ilids.py | from __future__ import division, print_function, absolute_import
import copy
import glob
import random
import os.path as osp
from collections import defaultdict
from torchreid.utils import read_json, write_json
from ..dataset import ImageDataset
class iLIDS(ImageDataset):
"""QMUL-iLIDS.
Reference:
... | 4,991 | 35.705882 | 88 | py |
deep-person-reid | deep-person-reid-master/torchreid/data/datasets/image/grid.py | from __future__ import division, print_function, absolute_import
import glob
import os.path as osp
from scipy.io import loadmat
from torchreid.utils import read_json, write_json
from ..dataset import ImageDataset
class GRID(ImageDataset):
"""GRID.
Reference:
Loy et al. Multi-camera activity correla... | 4,675 | 34.424242 | 93 | py |
deep-person-reid | deep-person-reid-master/torchreid/data/datasets/image/cuhk03.py | from __future__ import division, print_function, absolute_import
import os.path as osp
from torchreid.utils import read_json, write_json, mkdir_if_missing
from ..dataset import ImageDataset
class CUHK03(ImageDataset):
"""CUHK03.
Reference:
Li et al. DeepReID: Deep Filter Pairing Neural Network for ... | 12,177 | 38.538961 | 115 | py |
deep-person-reid | deep-person-reid-master/torchreid/losses/hard_mine_triplet_loss.py | from __future__ import division, absolute_import
import torch
import torch.nn as nn
class TripletLoss(nn.Module):
"""Triplet loss with hard positive/negative mining.
Reference:
Hermans et al. In Defense of the Triplet Loss for Person Re-Identification. arXiv:1703.07737.
Imported from `<h... | 1,770 | 35.142857 | 101 | py |
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