repo stringlengths 2 99 | file stringlengths 13 225 | code stringlengths 0 18.3M | file_length int64 0 18.3M | avg_line_length float64 0 1.36M | max_line_length int64 0 4.26M | extension_type stringclasses 1
value |
|---|---|---|---|---|---|---|
pke | pke-master/docs/source/conf.py | # -*- coding: utf-8 -*-
#
# Configuration file for the Sphinx documentation builder.
#
# This file does only contain a selection of the most common options. For a
# full list see the documentation:
# http://www.sphinx-doc.org/en/stable/config
# -- Path setup ------------------------------------------------------------... | 3,678 | 30.715517 | 79 | py |
vadesc | vadesc-main/main.py | """
Runs the VaDeSC model.
"""
import argparse
from pathlib import Path
import yaml
import logging
import tensorflow as tf
import tensorflow_probability as tfp
import os
from models.losses import Losses
from train import run_experiment
tfd = tfp.distributions
tfkl = tf.keras.layers
tfpl = tfp.layers
tfk = tf.keras
#... | 5,142 | 37.380597 | 112 | py |
vadesc | vadesc-main/train.py | import time
from pathlib import Path
import tensorflow as tf
import tensorflow_probability as tfp
import numpy as np
from sklearn.mixture import GaussianMixture
from sklearn.cluster import KMeans
from sklearn.metrics.cluster import normalized_mutual_info_score, adjusted_rand_score
import uuid
import math
from utils.eva... | 16,068 | 46.54142 | 166 | py |
vadesc | vadesc-main/baselines/aft/main_aft.py | """
Runs Weibull AFT model.
"""
import argparse
import os
import numpy as np
import pandas as pd
import time
import uuid
from lifelines import WeibullAFTFitter
import sys
sys.path.insert(0, '../../')
from datasets.support.support_data import generate_support
from datasets.hgg.hgg_data import generate_hgg
from ... | 8,240 | 41.699482 | 119 | py |
vadesc | vadesc-main/baselines/km/main_km.py | """
Runs k-means clustering.
"""
import argparse
import numpy as np
import time
import uuid
from sklearn.preprocessing import StandardScaler
from sklearn.model_selection import train_test_split
from sklearn.metrics import normalized_mutual_info_score, adjusted_rand_score
from sklearn.cluster import KMeans
import sys
... | 6,062 | 40.813793 | 151 | py |
vadesc | vadesc-main/baselines/coxph/main_coxph.py | """
Runs Cox PH regression.
"""
import argparse
import os
import numpy as np
import time
import uuid
from sklearn.preprocessing import StandardScaler
from sklearn.model_selection import train_test_split
from lifelines import CoxPHFitter
import sys
sys.path.insert(0, '../../')
from datasets.survivalMNIST.survivalMN... | 8,516 | 44.063492 | 151 | py |
vadesc | vadesc-main/baselines/coxph/coxph.py | """
Wrapper for Cox PH model as implemented by lifelines.
"""
import numpy as np
from lifelines import CoxPHFitter
import sys
sys.path.insert(0, '../../')
from utils.data_utils import construct_surv_df
from utils.eval_utils import cindex
def fit_coxph(X: np.ndarray, t: np.ndarray, d: np.ndarray, penalty_weight=0.0,... | 1,914 | 43.534884 | 121 | py |
vadesc | vadesc-main/baselines/ssc/sscBair.py | """
A Python implementation of the semi-supervised survival data clustering described by Bair & Tibshirani in
https://journals.plos.org/plosbiology/article?id=10.1371/journal.pbio.0020108
"""
import numpy as np
from lifelines import CoxPHFitter
from sklearn.cluster import KMeans
from sklearn.metrics import norma... | 3,382 | 32.49505 | 116 | py |
vadesc | vadesc-main/baselines/ssc/main_ssc_bair.py | """
Runs semi-supervised clustering of survival data as described by Bair & Tibshirani.
"""
import argparse
import numpy as np
import time
import uuid
from sklearn.preprocessing import StandardScaler
from sklearn.model_selection import train_test_split
from sklearn.metrics import normalized_mutual_info_score, adju... | 7,423 | 40.943503 | 128 | py |
vadesc | vadesc-main/baselines/sca/sca_utils/pre_processing.py | """
Some utility functions for data preprocessing taken from Chapfuwa et al.:
https://github.com/paidamoyo/survival_cluster_analysis
"""
import numpy as np
import pandas
def one_hot_encoder(data, encode):
print("Encoding data:{}".format(data.shape))
data_encoded = data.copy()
encoded = pandas.get_dumm... | 4,311 | 35.542373 | 101 | py |
vadesc | vadesc-main/models/losses.py | """
Loss functions for the reconstruction term of the ELBO.
"""
import tensorflow as tf
class Losses:
def __init__(self, configs):
self.input_dim = configs['training']['inp_shape']
self.tuple = False
if isinstance(self.input_dim, list):
print("\nData is tuple!\n")
s... | 1,721 | 43.153846 | 120 | py |
vadesc | vadesc-main/models/model.py | """
VaDeSC model.
"""
import tensorflow as tf
import tensorflow_probability as tfp
import os
from models.networks import (VGGEncoder, VGGDecoder, Encoder, Decoder, Encoder_small, Decoder_small)
from utils.utils import weibull_scale, weibull_log_pdf, tensor_slice
# Pretrain autoencoder
checkpoint_path = "autoencoder/... | 9,434 | 48.657895 | 124 | py |
vadesc | vadesc-main/models/networks.py | """
Encoder and decoder architectures used by VaDeSC.
"""
import tensorflow as tf
import tensorflow_probability as tfp
from tensorflow.keras import layers
tfd = tfp.distributions
tfkl = tf.keras.layers
tfpl = tfp.layers
tfk = tf.keras
# Wide MLP encoder and decoder architectures
class Encoder(layers.Layer):
def... | 5,648 | 32.229412 | 119 | py |
vadesc | vadesc-main/datasets/simulated_data.py | """
Returns the synthetic data.
"""
from datasets.simulations import format_profile_surv_data_tf
def generate_data():
preproc = format_profile_surv_data_tf(p=100, n=1000, k=5, p_cens=0.2, seed=42, clust_mean=False, clust_cov=False,
clust_intercepts=False, density=0.2, wei... | 427 | 34.666667 | 117 | py |
vadesc | vadesc-main/datasets/simulations.py | """
Numerical simulations and utility functions for constructing the synthetic dataset.
"""
import numpy as np
from numpy.random import multivariate_normal, uniform, choice
from sklearn.datasets import make_spd_matrix
from scipy.stats import weibull_min
from utils.sim_utils import random_nonlin_map
from baselines.s... | 13,134 | 37.632353 | 120 | py |
vadesc | vadesc-main/datasets/flchain/flchain_data.py | """
FLChain dataset.
Based on the code from Chapfuwa et al.:
https://github.com/paidamoyo/survival_cluster_analysis
"""
# age: age in years
# sex: F=female, M=male
# sample.yr: the calendar year in which a blood sample was obtained
# kappa: serum free light chain, kappa portion
# lambda: serum free light chain, lam... | 4,967 | 38.744 | 113 | py |
vadesc | vadesc-main/datasets/hemodialysis/hemo_data.py | """
Dataset of children undergoing hemodialysis.
"""
import numpy as np
import pandas as pd
import os
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
def generate_hemo(seed=42, label=3):
dir_path = os.path.dirname(os.path.realpath(__file__))
path = os.path... | 2,403 | 32.859155 | 115 | py |
vadesc | vadesc-main/datasets/support/support_data.py | """
SUPPORT dataset.
Based on the code from Chapfuwa et al.:
https://github.com/paidamoyo/survival_cluster_analysis
"""
import os
import numpy as np
import pandas
from baselines.sca.sca_utils.pre_processing import one_hot_encoder, formatted_data, missing_proportion, \
one_hot_indices, get_train_median_mode, l... | 4,591 | 37.588235 | 114 | py |
vadesc | vadesc-main/datasets/nsclc_lung/CT_preproc_utils.py | """
Utility functions for CT scan preprocessing.
"""
import numpy as np
import pandas as pd
import os
import glob
import cv2
import progressbar
import re
from PIL import Image, ImageOps
# Libraries for DICOM data handling
import pydicom
import pydicom_seg
LUNG1_N_PATIENTS = 422
RADIOGENOMICS_N_PATIENTS = 96
IGNORED_... | 55,312 | 45.132611 | 120 | py |
vadesc | vadesc-main/datasets/nsclc_lung/nsclc_lung_data.py | """
Data loaders for NSCLC datasets.
"""
import os
import re
import numpy as np
import progressbar
import pandas as pd
from sklearn.preprocessing import StandardScaler
from sklearn.model_selection import train_test_split
from datasets.nsclc_lung.CT_preproc_utils import (preprocess_lung1_images, preprocess_radiogenom... | 17,556 | 43.002506 | 120 | py |
vadesc | vadesc-main/datasets/survivalMNIST/survivalMNIST_data.py | """
Survival MNIST dataset.
Based on Pölsterl's tutorial:
https://k-d-w.org/blog/2019/07/survival-analysis-for-deep-learning/
https://github.com/sebp/survival-cnn-estimator
"""
import numpy as np
from numpy.random import choice, uniform, normal
import tensorflow as tf
import tensorflow.keras.datasets.mnist as ... | 4,151 | 34.487179 | 122 | py |
vadesc | vadesc-main/datasets/hgg/hgg_data.py | """
Dataset of high-grade glioma patients (HGG).
Based on the code from Chapfuwa et al.:
https://github.com/paidamoyo/survival_cluster_analysis
"""
import os
import numpy as np
import pandas
from baselines.sca.sca_utils.pre_processing import one_hot_encoder, formatted_data, missing_proportion, \
one_hot_indic... | 8,022 | 44.327684 | 119 | py |
vadesc | vadesc-main/utils/sim_utils.py | """
Utility functions for numerical simulations.
"""
import numpy as np
from sklearn.datasets import make_low_rank_matrix
import pandas as pd
def random_nonlin_map(n_in, n_out, n_hidden, rank=1000):
# Random MLP mapping
W_0 = make_low_rank_matrix(n_in, n_hidden, effective_rank=rank)
W_1 = make_low_rank_... | 1,132 | 29.621622 | 106 | py |
vadesc | vadesc-main/utils/constants.py | # Project-wide constants:
ROOT_LOGGER_STR = "VaDeSC"
LOGGER_RESULT_FILE = "logs.txt" | 84 | 27.333333 | 31 | py |
vadesc | vadesc-main/utils/plotting.py | """
Utility functions for plotting.
"""
import os
import numpy as np
from lifelines import KaplanMeierFitter
import matplotlib.pyplot as plt
from matplotlib import rc
from openTSNE import TSNE as fastTSNE
import sys
sys.path.insert(0, '../')
CB_COLOR_CYCLE = ['#377eb8', '#ff7f00', '#4daf4a', '#f781bf', '#a65628'... | 6,572 | 30.151659 | 120 | py |
vadesc | vadesc-main/utils/utils.py | """
miscellaneous utility functions.
"""
import matplotlib
import matplotlib.pyplot as plt
import logging
from sklearn.utils.linear_assignment_ import linear_assignment
import numpy as np
from scipy.stats import weibull_min, fisk
import sys
from utils.constants import ROOT_LOGGER_STR
import tensorflow as tf
impor... | 5,806 | 34.408537 | 133 | py |
vadesc | vadesc-main/utils/data_utils.py | """
Utility functions for data loading.
"""
import tensorflow as tf
import tensorflow_probability as tfp
import numpy as np
from sklearn.model_selection import train_test_split
import pandas as pd
from sklearn.preprocessing import StandardScaler
from tensorflow.keras.utils import to_categorical
from datasets.survivalM... | 14,038 | 54.710317 | 150 | py |
vadesc | vadesc-main/utils/radiomics_utils.py | """
Utility functions for extracting radiomics features.
"""
import os
import shutil
import numpy as np
import cv2
import logging
import progressbar
from radiomics import featureextractor
def extract_radiomics_features(data_file, masks, verbose=1):
# Set logging for the radiomics library
logger = loggin... | 2,116 | 29.242857 | 114 | py |
vadesc | vadesc-main/utils/eval_utils.py | """
Utility functions for model evaluation.
"""
import numpy as np
from lifelines.utils import concordance_index
import sys
from sklearn.utils.linear_assignment_ import linear_assignment
from sklearn.metrics.cluster import normalized_mutual_info_score
import tensorflow as tf
from lifelines import KaplanMeierFitter
... | 5,546 | 31.063584 | 120 | py |
vadesc | vadesc-main/posthoc_explanations/explainer_utils.py | import pandas as pd
import numpy as np
from sklearn.preprocessing import MinMaxScaler, StandardScaler
import keras
import math
import seaborn as sns
import matplotlib.pyplot as plt
import matplotlib
############### PROTOTYPES SAMPLING UTILITY FUNCTIONS #####################################
def Prototypes_sampler... | 7,993 | 32.033058 | 158 | py |
sdmgrad | sdmgrad-main/toy/toy.py | from copy import deepcopy
from mpl_toolkits.mplot3d import Axes3D
from matplotlib import cm, ticker
from matplotlib.colors import LogNorm
from tqdm import tqdm
from scipy.optimize import minimize, Bounds, minimize_scalar
import matplotlib.pyplot as plt
import numpy as np
import time
import torch
import torch.nn as nn
... | 13,100 | 28.308725 | 110 | py |
sdmgrad | sdmgrad-main/mtrl/mtrl_files/sdmgrad.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
from copy import deepcopy
from typing import Iterable, List, Optional, Tuple
import numpy as np
import time
import torch
from omegaconf import OmegaConf
from mtrl.agent import grad_manipulation as grad_manipulation_agent
from mtrl.utils.types impo... | 11,791 | 35.965517 | 163 | py |
sdmgrad | sdmgrad-main/mtrl/mtrl_files/config.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
"""Code to interface with the config."""
import datetime
import hashlib
import os
from copy import deepcopy
from typing import Any, Dict, cast
import hydra
from omegaconf import OmegaConf
from mtrl.utils import utils
from mtrl.utils.types import C... | 5,961 | 26.99061 | 152 | py |
sdmgrad | sdmgrad-main/nyuv2/model_segnet_single.py | import torch.nn as nn
import torch.optim as optim
import torch.nn.functional as F
import argparse
from create_dataset import *
from utils import *
parser = argparse.ArgumentParser(description='Single-task: One Task')
parser.add_argument('--task', default='semantic', type=str, help='choose task: semantic, depth, norma... | 6,820 | 43.292208 | 120 | py |
sdmgrad | sdmgrad-main/nyuv2/evaluate.py | import matplotlib
import matplotlib.pyplot as plt
import seaborn as sns
import numpy as np
import torch
import itertools
methods = [
"sdmgrad-1e-1", "sdmgrad-2e-1", "sdmgrad-3e-1", "sdmgrad-4e-1", "sdmgrad-5e-1", "sdmgrad-6e-1", "sdmgrad-7e-1",
"sdmgrad-8e-1", "sdmgrad-9e-1", "sdmgrad-1e0"
]
colors = ["C0", "... | 3,777 | 30.747899 | 117 | py |
sdmgrad | sdmgrad-main/nyuv2/model_segnet_stan.py | import torch.nn as nn
import torch.optim as optim
import torch.nn.functional as F
import argparse
from create_dataset import *
from utils import *
parser = argparse.ArgumentParser(description='Single-task: Attention Network')
parser.add_argument('--task', default='semantic', type=str, help='choose task: semantic, dep... | 11,017 | 49.310502 | 119 | py |
sdmgrad | sdmgrad-main/nyuv2/utils.py | import numpy as np
import time
import torch
import torch.nn.functional as F
from copy import deepcopy
from min_norm_solvers import MinNormSolver
from scipy.optimize import minimize, Bounds, minimize_scalar
def euclidean_proj_simplex(v, s=1):
""" Compute the Euclidean projection on a positive simplex
Solves t... | 31,500 | 43.242978 | 130 | py |
sdmgrad | sdmgrad-main/nyuv2/model_segnet_split.py | import torch.nn as nn
import torch.optim as optim
import torch.nn.functional as F
import argparse
import torch.utils.data.sampler as sampler
from create_dataset import *
from utils import *
parser = argparse.ArgumentParser(description='Multi-task: Split')
parser.add_argument('--type', default='standard', type=str, he... | 7,942 | 44.649425 | 119 | py |
sdmgrad | sdmgrad-main/nyuv2/min_norm_solvers.py | # This code is from
# Multi-Task Learning as Multi-Objective Optimization
# Ozan Sener, Vladlen Koltun
# Neural Information Processing Systems (NeurIPS) 2018
# https://github.com/intel-isl/MultiObjectiveOptimization
import numpy as np
import torch
class MinNormSolver:
MAX_ITER = 20
STOP_CRIT = 1e-5
def ... | 7,358 | 35.979899 | 147 | py |
sdmgrad | sdmgrad-main/nyuv2/model_segnet_mtan.py | import numpy as np
import random
import torch.nn as nn
import torch.optim as optim
import torch.nn.functional as F
import argparse
import torch.utils.data.sampler as sampler
from create_dataset import *
from utils import *
parser = argparse.ArgumentParser(description='Multi-task: Attention Network')
parser.add_argume... | 11,617 | 49.077586 | 119 | py |
sdmgrad | sdmgrad-main/nyuv2/create_dataset.py | from torch.utils.data.dataset import Dataset
import os
import torch
import torch.nn.functional as F
import fnmatch
import numpy as np
import random
class RandomScaleCrop(object):
"""
Credit to Jialong Wu from https://github.com/lorenmt/mtan/issues/34.
"""
def __init__(self, scale=[1.0, 1.2, 1.5]):
... | 3,568 | 40.988235 | 127 | py |
sdmgrad | sdmgrad-main/nyuv2/model_segnet_cross.py | import numpy as np
import random
import torch.nn as nn
import torch.optim as optim
import torch.nn.functional as F
import argparse
import torch.utils.data.sampler as sampler
from create_dataset import *
from utils import *
parser = argparse.ArgumentParser(description='Multi-task: Cross')
parser.add_argument('--weight... | 9,335 | 47.879581 | 119 | py |
sdmgrad | sdmgrad-main/nyuv2/model_segnet_mt.py | import numpy as np
import random
import torch.nn as nn
import torch.optim as optim
import torch.nn.functional as F
import argparse
import torch.utils.data.sampler as sampler
from create_dataset import *
from utils import *
parser = argparse.ArgumentParser(description='Multi-task: Split')
parser.add_argument('--type',... | 18,041 | 48.027174 | 119 | py |
sdmgrad | sdmgrad-main/consistency/model_resnet.py | # resnet18 base model for Pareto MTL
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.nn.modules.loss import CrossEntropyLoss
from torchvision import models
class RegressionTrainResNet(torch.nn.Module):
def __init__(self, model, init_weight):
super(RegressionTrainResNet, sel... | 2,346 | 31.150685 | 80 | py |
sdmgrad | sdmgrad-main/consistency/utils.py | import numpy as np
from min_norm_solvers import MinNormSolver
from scipy.optimize import minimize, Bounds, minimize_scalar
import torch
from torch import linalg as LA
from torch.nn import functional as F
def euclidean_proj_simplex(v, s=1):
""" Compute the Euclidean projection on a positive simplex
Solves the... | 5,435 | 34.070968 | 113 | py |
sdmgrad | sdmgrad-main/consistency/model_lenet.py | # lenet base model for Pareto MTL
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.nn.modules.loss import CrossEntropyLoss
class RegressionTrain(torch.nn.Module):
def __init__(self, model, init_weight):
super(RegressionTrain, self).__init__()
self.model = model
... | 2,006 | 26.875 | 80 | py |
sdmgrad | sdmgrad-main/consistency/min_norm_solvers.py | # This code is from
# Multi-Task Learning as Multi-Objective Optimization
# Ozan Sener, Vladlen Koltun
# Neural Information Processing Systems (NeurIPS) 2018
# https://github.com/intel-isl/MultiObjectiveOptimization
import numpy as np
import torch
class MinNormSolver:
MAX_ITER = 20
STOP_CRIT = 1e-5
def ... | 7,364 | 36.01005 | 147 | py |
sdmgrad | sdmgrad-main/consistency/train.py | import numpy as np
import torch
import torch.utils.data
from torch import linalg as LA
from torch.autograd import Variable
from model_lenet import RegressionModel, RegressionTrain
from model_resnet import MnistResNet, RegressionTrainResNet
from utils import *
import pickle
import argparse
parser = argparse.Argument... | 7,010 | 36.292553 | 118 | py |
sdmgrad | sdmgrad-main/cityscapes/model_segnet_single.py | import torch.nn as nn
import torch.optim as optim
import torch.nn.functional as F
import argparse
from create_dataset import *
from utils import *
parser = argparse.ArgumentParser(description='Single-task: One Task')
parser.add_argument('--task', default='semantic', type=str, help='choose task: semantic, depth')
pars... | 6,370 | 43.552448 | 120 | py |
sdmgrad | sdmgrad-main/cityscapes/evaluate.py | import matplotlib.pyplot as plt
import seaborn as sns
import numpy as np
import torch
methods = [
"sdmgrad-1e-1", "sdmgrad-2e-1", "sdmgrad-3e-1", "sdmgrad-4e-1", "sdmgrad-5e-1", "sdmgrad-6e-1", "sdmgrad-7e-1",
"sdmgrad-8e-1", "sdmgrad-9e-1", "sdmgrad-1e0"
]
colors = ["C0", "C1", "C2", "C3", "C4", "C5", "C6", ... | 3,545 | 30.380531 | 117 | py |
sdmgrad | sdmgrad-main/cityscapes/model_segnet_stan.py | import torch.nn as nn
import torch.optim as optim
import torch.nn.functional as F
import argparse
from create_dataset import *
from utils import *
parser = argparse.ArgumentParser(description='Single-task: Attention Network')
parser.add_argument('--task', default='semantic', type=str, help='choose task: semantic, dep... | 11,156 | 49.713636 | 119 | py |
sdmgrad | sdmgrad-main/cityscapes/utils.py | import torch
import torch.nn.functional as F
import numpy as np
import random
import time
from copy import deepcopy
from min_norm_solvers import MinNormSolver
from scipy.optimize import minimize, Bounds, minimize_scalar
def euclidean_proj_simplex(v, s=1):
""" Compute the Euclidean projection on a positive simple... | 27,394 | 40.25753 | 148 | py |
sdmgrad | sdmgrad-main/cityscapes/model_segnet_split.py | import torch.nn as nn
import torch.optim as optim
import torch.nn.functional as F
import argparse
import torch.utils.data.sampler as sampler
from create_dataset import *
from utils import *
parser = argparse.ArgumentParser(description='Multi-task: Split')
parser.add_argument('--type', default='standard', type=str, he... | 11,395 | 50.103139 | 119 | py |
sdmgrad | sdmgrad-main/cityscapes/min_norm_solvers.py | # This code is from
# Multi-Task Learning as Multi-Objective Optimization
# Ozan Sener, Vladlen Koltun
# Neural Information Processing Systems (NeurIPS) 2018
# https://github.com/intel-isl/MultiObjectiveOptimization
import numpy as np
import torch
class MinNormSolver:
MAX_ITER = 20
STOP_CRIT = 1e-5
def ... | 7,358 | 35.979899 | 147 | py |
sdmgrad | sdmgrad-main/cityscapes/model_segnet_mtan.py | import torch.nn as nn
import torch.optim as optim
import torch.nn.functional as F
import argparse
import torch.utils.data.sampler as sampler
from create_dataset import *
from utils import *
parser = argparse.ArgumentParser(description='Multi-task: Attention Network')
parser.add_argument('--weight', default='equal', t... | 11,396 | 49.879464 | 119 | py |
sdmgrad | sdmgrad-main/cityscapes/create_dataset.py | from torch.utils.data.dataset import Dataset
import os
import torch
import torch.nn.functional as F
import fnmatch
import numpy as np
import random
class RandomScaleCrop(object):
"""
Credit to Jialong Wu from https://github.com/lorenmt/mtan/issues/34.
"""
def __init__(self, scale=[1.0, 1.2, 1.5]):
... | 6,513 | 41.298701 | 127 | py |
sdmgrad | sdmgrad-main/cityscapes/model_segnet_cross.py | import torch.nn as nn
import torch.optim as optim
import torch.nn.functional as F
import argparse
import torch.utils.data.sampler as sampler
from create_dataset import *
from utils import *
parser = argparse.ArgumentParser(description='Multi-task: Cross')
parser.add_argument('--weight', default='equal', type=str, hel... | 9,044 | 48.42623 | 119 | py |
sdmgrad | sdmgrad-main/cityscapes/model_segnet_mt.py | import torch.nn as nn
import torch.optim as optim
import torch.nn.functional as F
import argparse
import torch.utils.data.sampler as sampler
from create_dataset import *
from utils import *
parser = argparse.ArgumentParser(description='Multi-task: Attention Network')
parser.add_argument('--method', default='sdmgrad',... | 12,105 | 49.865546 | 119 | py |
SyNet | SyNet-master/CenterNet/src/main.py | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import _init_paths
import os
import torch
import torch.utils.data
from opts import opts
from models.model import create_model, load_model, save_model
from models.data_parallel import DataParallel
from logger ... | 3,348 | 31.833333 | 78 | py |
SyNet | SyNet-master/CenterNet/src/test.py | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import _init_paths
import os
import json
import cv2
import numpy as np
import time
from progress.bar import Bar
import torch
from external.nms import soft_nms
from opts import opts
from logger import Logger
f... | 4,092 | 31.484127 | 78 | py |
SyNet | SyNet-master/CenterNet/src/_init_paths.py | import os.path as osp
import sys
def add_path(path):
if path not in sys.path:
sys.path.insert(0, path)
this_dir = osp.dirname(__file__)
# Add lib to PYTHONPATH
lib_path = osp.join(this_dir, 'lib')
add_path(lib_path)
| 231 | 16.846154 | 36 | py |
SyNet | SyNet-master/CenterNet/src/demo.py | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import _init_paths
import os
import cv2
from opts import opts
from detectors.detector_factory import detector_factory
image_ext = ['jpg', 'jpeg', 'png', 'webp']
video_ext = ['mp4', 'mov', 'avi', 'mkv']
time_... | 1,674 | 28.385965 | 70 | py |
SyNet | SyNet-master/CenterNet/src/tools/merge_pascal_json.py | import json
# ANNOT_PATH = '/home/zxy/Datasets/VOC/annotations/'
ANNOT_PATH = 'voc/annotations/'
OUT_PATH = ANNOT_PATH
INPUT_FILES = ['pascal_train2012.json', 'pascal_val2012.json',
'pascal_train2007.json', 'pascal_val2007.json']
OUTPUT_FILE = 'pascal_trainval0712.json'
KEYS = ['images', 'type', 'annota... | 1,058 | 33.16129 | 62 | py |
SyNet | SyNet-master/CenterNet/src/tools/eval_coco_hp.py | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import pycocotools.coco as coco
from pycocotools.cocoeval import COCOeval
import sys
import cv2
import numpy as np
import pickle
import os
this_dir = os.path.dirname(__file__)
ANN_PATH = this_dir + '../../data... | 795 | 24.677419 | 81 | py |
SyNet | SyNet-master/CenterNet/src/tools/reval.py | #!/usr/bin/env python
# --------------------------------------------------------
# Fast R-CNN
# Copyright (c) 2015 Microsoft
# Licensed under The MIT License [see LICENSE for details]
# Written by Ross Girshick
# Modified by Xingyi Zhou
# --------------------------------------------------------
# Reval = re-eval. Re-... | 2,331 | 28.518987 | 74 | py |
SyNet | SyNet-master/CenterNet/src/tools/convert_kitti_to_coco.py | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import pickle
import json
import numpy as np
import cv2
DATA_PATH = '../../data/kitti/'
DEBUG = False
# VAL_PATH = DATA_PATH + 'training/label_val/'
import os
SPLITS = ['3dop', 'subcnn']
import _init_paths
fro... | 5,935 | 37.797386 | 80 | py |
SyNet | SyNet-master/CenterNet/src/tools/_init_paths.py | import os.path as osp
import sys
def add_path(path):
if path not in sys.path:
sys.path.insert(0, path)
this_dir = osp.dirname(__file__)
# Add lib to PYTHONPATH
lib_path = osp.join(this_dir, '../lib')
add_path(lib_path)
| 234 | 17.076923 | 39 | py |
SyNet | SyNet-master/CenterNet/src/tools/calc_coco_overlap.py | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import pycocotools.coco as COCO
import cv2
import numpy as np
from pycocotools import mask as maskUtils
ANN_PATH = '../../data/coco/annotations/'
IMG_PATH = '../../data/coco/'
ANN_FILES = {'train': 'instances_t... | 10,869 | 32.653251 | 101 | py |
SyNet | SyNet-master/CenterNet/src/tools/convert_hourglass_weight.py | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
MODEL_PATH = '../../models/ExtremeNet_500000.pkl'
OUT_PATH = '../../models/ExtremeNet_500000.pth'
import torch
state_dict = torch.load(MODEL_PATH)
key_map = {'t_heats': 'hm_t', 'l_heats': 'hm_l', 'b_heats': 'h... | 905 | 28.225806 | 69 | py |
SyNet | SyNet-master/CenterNet/src/tools/voc_eval_lib/datasets/ds_utils.py | # --------------------------------------------------------
# Fast/er R-CNN
# Licensed under The MIT License [see LICENSE for details]
# Written by Ross Girshick
# --------------------------------------------------------
from __future__ import absolute_import
from __future__ import division
from __future__ import print_... | 1,402 | 27.06 | 70 | py |
SyNet | SyNet-master/CenterNet/src/lib/opts.py | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import argparse
import os
import sys
class opts(object):
def __init__(self):
self.parser = argparse.ArgumentParser()
# basic experiment setting
self.parser.add_argument('task', default='ctdet',
... | 18,703 | 50.526171 | 115 | py |
SyNet | SyNet-master/CenterNet/src/lib/logger.py | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
# Code referenced from https://gist.github.com/gyglim/1f8dfb1b5c82627ae3efcfbbadb9f514
import os
import time
import sys
import torch
USE_TENSORBOARD = True
try:
import tensorboardX
print('Using tensorboardX... | 2,228 | 29.534247 | 86 | py |
SyNet | SyNet-master/CenterNet/src/lib/external/setup.py | import numpy
from distutils.core import setup
from distutils.extension import Extension
from Cython.Build import cythonize
extensions = [
Extension(
"nms",
["nms.pyx"])
]
setup(
name="coco",
ext_modules=cythonize(extensions),
include_dirs=[numpy.get_include()]
)
| 298 | 16.588235 | 41 | py |
SyNet | SyNet-master/CenterNet/src/lib/detectors/exdet.py | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import _init_paths
import os
import cv2
import numpy as np
from progress.bar import Bar
import time
import torch
from models.decode import exct_decode, agnex_ct_decode
from models.utils import flip_tensor
fr... | 5,063 | 37.363636 | 80 | py |
SyNet | SyNet-master/CenterNet/src/lib/detectors/ctdet.py | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import cv2
import numpy as np
from progress.bar import Bar
import time
import torch
try:
from external.nms import soft_nms
except:
print('NMS not imported! If you need it,'
' do \n cd $CenterNet_RO... | 3,674 | 36.886598 | 90 | py |
SyNet | SyNet-master/CenterNet/src/lib/detectors/ddd.py | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import cv2
import numpy as np
from progress.bar import Bar
import time
import torch
from models.decode import ddd_decode
from models.utils import flip_tensor
from utils.image import get_affine_transform
from ... | 4,013 | 36.867925 | 73 | py |
SyNet | SyNet-master/CenterNet/src/lib/detectors/multi_pose.py | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import cv2
import numpy as np
from progress.bar import Bar
import time
import torch
try:
from external.nms import soft_nms_39
except:
print('NMS not imported! If you need it,'
' do \n cd $CenterNet... | 3,923 | 37.097087 | 79 | py |
SyNet | SyNet-master/CenterNet/src/lib/detectors/detector_factory.py | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from .exdet import ExdetDetector
from .ddd import DddDetector
from .ctdet import CtdetDetector
from .multi_pose import MultiPoseDetector
detector_factory = {
'exdet': ExdetDetector,
'ddd': DddDetector,
... | 382 | 22.9375 | 41 | py |
SyNet | SyNet-master/CenterNet/src/lib/detectors/base_detector.py | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import cv2
import numpy as np
from progress.bar import Bar
import time
import torch
from models.model import create_model, load_model
from utils.image import get_affine_transform
from utils.debugger import Deb... | 5,061 | 34.152778 | 78 | py |
SyNet | SyNet-master/CenterNet/src/lib/models/decode.py | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import torch
import torch.nn as nn
from .utils import _gather_feat, _transpose_and_gather_feat
def _nms(heat, kernel=3):
pad = (kernel - 1) // 2
hmax = nn.functional.max_pool2d(
heat, (kernel,... | 21,763 | 37.115587 | 79 | py |
SyNet | SyNet-master/CenterNet/src/lib/models/losses.py | # ------------------------------------------------------------------------------
# Portions of this code are from
# CornerNet (https://github.com/princeton-vl/CornerNet)
# Copyright (c) 2018, University of Michigan
# Licensed under the BSD 3-Clause License
# -------------------------------------------------------------... | 7,843 | 31.957983 | 80 | py |
SyNet | SyNet-master/CenterNet/src/lib/models/data_parallel.py | import torch
from torch.nn.modules import Module
from torch.nn.parallel.scatter_gather import gather
from torch.nn.parallel.replicate import replicate
from torch.nn.parallel.parallel_apply import parallel_apply
from .scatter_gather import scatter_kwargs
class _DataParallel(Module):
r"""Implements data parallelis... | 5,176 | 39.445313 | 101 | py |
SyNet | SyNet-master/CenterNet/src/lib/models/utils.py | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import torch
import torch.nn as nn
def _sigmoid(x):
y = torch.clamp(x.sigmoid_(), min=1e-4, max=1-1e-4)
return y
def _gather_feat(feat, ind, mask=None):
dim = feat.size(2)
ind = ind.unsqueeze(2)... | 1,571 | 30.44 | 65 | py |
SyNet | SyNet-master/CenterNet/src/lib/models/model.py | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import torchvision.models as models
import torch
import torch.nn as nn
import os
from .networks.msra_resnet import get_pose_net
from .networks.dlav0 import get_pose_net as get_dlav0
from .networks.pose_dla_dcn... | 3,415 | 34.216495 | 80 | py |
SyNet | SyNet-master/CenterNet/src/lib/models/scatter_gather.py | import torch
from torch.autograd import Variable
from torch.nn.parallel._functions import Scatter, Gather
def scatter(inputs, target_gpus, dim=0, chunk_sizes=None):
r"""
Slices variables into approximately equal chunks and
distributes them across given GPUs. Duplicates
references to objects that are n... | 1,535 | 38.384615 | 77 | py |
SyNet | SyNet-master/CenterNet/src/lib/models/networks/resnet_dcn.py | # ------------------------------------------------------------------------------
# Copyright (c) Microsoft
# Licensed under the MIT License.
# Written by Bin Xiao (Bin.Xiao@microsoft.com)
# Modified by Dequan Wang and Xingyi Zhou
# ------------------------------------------------------------------------------
from __f... | 10,054 | 33.553265 | 80 | py |
SyNet | SyNet-master/CenterNet/src/lib/models/networks/pose_dla_dcn.py | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import os
import math
import logging
import numpy as np
from os.path import join
import torch
from torch import nn
import torch.nn.functional as F
import torch.utils.model_zoo as model_zoo
from .DCNv2.dcn_v2 ... | 17,594 | 34.617409 | 106 | py |
SyNet | SyNet-master/CenterNet/src/lib/models/networks/msra_resnet.py | # ------------------------------------------------------------------------------
# Copyright (c) Microsoft
# Licensed under the MIT License.
# Written by Bin Xiao (Bin.Xiao@microsoft.com)
# Modified by Xingyi Zhou
# ------------------------------------------------------------------------------
from __future__ import a... | 10,167 | 35.185053 | 94 | py |
SyNet | SyNet-master/CenterNet/src/lib/models/networks/large_hourglass.py | # ------------------------------------------------------------------------------
# This code is base on
# CornerNet (https://github.com/princeton-vl/CornerNet)
# Copyright (c) 2018, University of Michigan
# Licensed under the BSD 3-Clause License
# ----------------------------------------------------------------------... | 9,942 | 32.033223 | 118 | py |
SyNet | SyNet-master/CenterNet/src/lib/models/networks/dlav0.py | #!/usr/bin/env python
# -*- coding: utf-8 -*-
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import math
from os.path import join
import torch
from torch import nn
import torch.utils.model_zoo as model_zoo
import numpy as np
BatchNorm = nn.BatchNorm2d
d... | 22,682 | 34.00463 | 86 | py |
SyNet | SyNet-master/CenterNet/src/lib/models/networks/DCNv2/setup.py | #!/usr/bin/env python
import os
import glob
import torch
from torch.utils.cpp_extension import CUDA_HOME
from torch.utils.cpp_extension import CppExtension
from torch.utils.cpp_extension import CUDAExtension
from setuptools import find_packages
from setuptools import setup
requirements = ["torch", "torchvision"]
... | 2,035 | 27.676056 | 73 | py |
SyNet | SyNet-master/CenterNet/src/lib/trains/train_factory.py | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from .ctdet import CtdetTrainer
from .ddd import DddTrainer
from .exdet import ExdetTrainer
from .multi_pose import MultiPoseTrainer
train_factory = {
'exdet': ExdetTrainer,
'ddd': DddTrainer,
'ctdet': ... | 371 | 22.25 | 40 | py |
SyNet | SyNet-master/CenterNet/src/lib/trains/exdet.py | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import torch
import numpy as np
import cv2
import sys
import time
from utils.debugger import Debugger
from models.data_parallel import DataParallel
from models.losses import FocalLoss, RegL1Loss
from models.dec... | 3,605 | 40.930233 | 79 | py |
SyNet | SyNet-master/CenterNet/src/lib/trains/ctdet.py | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import torch
import numpy as np
from models.losses import FocalLoss
from models.losses import RegL1Loss, RegLoss, NormRegL1Loss, RegWeightedL1Loss
from models.decode import ctdet_decode
from models.utils impor... | 5,518 | 40.810606 | 78 | py |
SyNet | SyNet-master/CenterNet/src/lib/trains/ddd.py | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import torch
import numpy as np
from models.losses import FocalLoss, L1Loss, BinRotLoss
from models.decode import ddd_decode
from models.utils import _sigmoid
from utils.debugger import Debugger
from utils.pos... | 6,919 | 43.645161 | 80 | py |
SyNet | SyNet-master/CenterNet/src/lib/trains/multi_pose.py | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import torch
import numpy as np
from models.losses import FocalLoss, RegL1Loss, RegLoss, RegWeightedL1Loss
from models.decode import multi_pose_decode
from models.utils import _sigmoid, flip_tensor, flip_lr_of... | 7,252 | 44.049689 | 82 | py |
SyNet | SyNet-master/CenterNet/src/lib/trains/base_trainer.py | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import time
import torch
from progress.bar import Bar
from models.data_parallel import DataParallel
from utils.utils import AverageMeter
class ModelWithLoss(torch.nn.Module):
def __init__(self, model, loss)... | 3,913 | 31.890756 | 80 | py |
SyNet | SyNet-master/CenterNet/src/lib/datasets/dataset_factory.py | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from .sample.ddd import DddDataset
from .sample.exdet import EXDetDataset
from .sample.ctdet import CTDetDataset
from .sample.multi_pose import MultiPoseDataset
from .dataset.visdrone import Visdrone
from .dat... | 885 | 22.315789 | 65 | py |
SyNet | SyNet-master/CenterNet/src/lib/datasets/sample/exdet.py | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import torch.utils.data as data
import pycocotools.coco as coco
import numpy as np
import torch
import json
import cv2
import os
from utils.image import flip, color_aug
from utils.image import get_affine_transf... | 5,722 | 40.773723 | 81 | py |
SyNet | SyNet-master/CenterNet/src/lib/datasets/sample/ctdet.py | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import torch.utils.data as data
import numpy as np
import torch
import json
import cv2
import os
from utils.image import flip, color_aug
from utils.image import get_affine_transform, affine_transform
from utils... | 5,803 | 39.027586 | 80 | py |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.