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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duckietown_imitation_learning | duckietown_imitation_learning-main/models/squeezenet.py | """
Copyright Notice:
This is the official Duckietown implementation of the Squeezenet model:
https://github.com/duckietown/gym-duckietown/blob/daffy/learning/imitation/iil-dagger/model/squeezenet.py
"""
import torch
from torch import nn
from torchvision import models
import torch.nn.functional as F
import to... | 4,737 | 33.333333 | 109 | py |
duckietown_imitation_learning | duckietown_imitation_learning-main/models/model_gail.py | """
Copyright Notice:
The implementation of our GAIL network was created based on the following public repository:
https://github.com/Khrylx/PyTorch-RL
"""
import math
import os
import glob
import torch
import torch.nn as nn
import torch.nn.functional as F
import torchvision.models as models
import numpy as ... | 12,423 | 36.534743 | 135 | py |
duckietown_imitation_learning | duckietown_imitation_learning-main/models/model_dpl.py | import torch
import torch.nn as nn
import numpy as np
import os
import glob
# If this variable is true, the image preprocessing wrapper will not only resize and crop the image, but it will also perform
# a thresholding operation on the image to extract the white and yellow lines
# So if it's True, the agent will lea... | 8,137 | 44.719101 | 128 | py |
duckietown_imitation_learning | duckietown_imitation_learning-main/experiments/test.py | import argparse
import glob
import os
import torch
from utils.env import launch_env
from utils.wrappers import wheel_to_steering
from utils.wrappers import steering_to_wheel
from utils.wrappers import preprocess_observation, preprocess_observation_GAIL, preprocess_observation_unit
from utils.wrappers import ActionDela... | 4,881 | 34.635036 | 131 | py |
duckietown_imitation_learning | duckietown_imitation_learning-main/experiments/test_unit_img2img.py | """
Copyright Notice:
The implementation of our UNIT network was created based on the following public repository:
https://github.com/eriklindernoren/PyTorch-GAN#unit
"""
import os
import torch
from torch.autograd import Variable
from torch.utils.data import DataLoader
import torchvision.transforms as transfo... | 4,179 | 34.12605 | 96 | py |
duckietown_imitation_learning | duckietown_imitation_learning-main/experiments/train_unit_network.py | """
Copyright Notice:
The implementation of our UNIT network was created based on the following public repository:
https://github.com/eriklindernoren/PyTorch-GAN#unit
"""
import os
import numpy as np
import itertools
import datetime
import time
import sys
import torch
from torch.autograd import Variable
from ... | 9,542 | 31.681507 | 109 | py |
duckietown_imitation_learning | duckietown_imitation_learning-main/experiments/log.py | import os
import torch
import argparse
import numpy as np
from utils.env import launch_env
from utils.teacher import PurePursuitExpert
from utils.loggers import Logger
from utils.wrappers import steering_to_wheel
from utils.wrappers import preprocess_observation, preprocess_observation_GAIL, preprocess_observation_uni... | 9,099 | 37.559322 | 133 | py |
duckietown_imitation_learning | duckietown_imitation_learning-main/experiments/train_gail.py | """
Copyright Notice:
The implementation of our GAIL network was created based on the following public repository:
https://github.com/Khrylx/PyTorch-RL
"""
import os
import glob
import numpy as np
import math
from tqdm import tqdm
from utils.loggers import Reader, ReplayBuffer
from utils.wrappers import Action... | 8,192 | 37.464789 | 165 | py |
duckietown_imitation_learning | duckietown_imitation_learning-main/experiments/eval.py | import argparse
import torch
import glob
from utils.env import launch_env
from utils.wrappers import ActionDelayWrapper
from models.model_dpl import PytorchTrainer
from models.squeezenet import Squeezenet
from models.model_gail import Policy
from utils.duckietown_world_evaluator import DuckietownWorldEvaluator
# E... | 2,306 | 27.8375 | 108 | py |
duckietown_imitation_learning | duckietown_imitation_learning-main/experiments/train.py | import argparse
import time
import numpy as np
from tqdm import tqdm
from utils.loggers import Reader
from utils.gail_utils import extract_features_for_gail
from models.model_dpl import PytorchTrainer
from models.model_gail import PolicyPretrainer
from models.model_unit_controller import UnitControllerTrainer
from t... | 6,873 | 31.2723 | 116 | py |
duckietown_imitation_learning | duckietown_imitation_learning-main/utils/gail_utils.py | """
Copyright Notice:
The implementation of our GAIL network was created based on the following public repository:
https://github.com/Khrylx/PyTorch-RL
"""
import torch
import torch.nn as nn
import torchvision.models as models
import numpy as np
from tqdm import tqdm
from utils.loggers import Reader
from uti... | 5,871 | 33.745562 | 140 | py |
duckietown_imitation_learning | duckietown_imitation_learning-main/utils/unit_utils.py | """
Copyright Notice:
The implementation of our UNIT network was created based on the following public repository:
https://github.com/eriklindernoren/PyTorch-GAN#unit
"""
import glob
import random
import os
from torch.utils.data import Dataset
from PIL import Image
import torchvision.transforms as transforms
... | 1,172 | 31.583333 | 103 | py |
duckietown_imitation_learning | duckietown_imitation_learning-main/utils/wrappers.py | import gym
from gym import spaces
from gym_duckietown.simulator import Simulator
import numpy as np
import cv2
import torch
from torchvision.transforms import ToTensor, Normalize, Compose
# If this variable is true, the image preprocessing wrapper will not only resize and crop the image, but it will also perform
# a t... | 7,103 | 34.878788 | 185 | py |
PyDE | PyDE-master/doc/source/conf.py | # -*- coding: utf-8 -*-
#
# PyDE documentation build configuration file, created by
# sphinx-quickstart on Mon Sep 23 16:00:28 2013.
#
# 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 co... | 9,101 | 30.825175 | 166 | py |
PRD | PRD-master/Agent/MAA2C/a2c_agent.py | import numpy as np
import torch
import torch.optim as optim
from torch.distributions import Categorical
from a2c_model import *
import torch.nn.functional as F
class A2CAgent:
def __init__(
self,
env,
arguments
):
self.env = env
self.env_name = arguments.environment
self.value_lr = arguments.value_l... | 10,489 | 37.708487 | 181 | py |
PRD | PRD-master/Agent/MAA2C/maa2c.py | from comet_ml import Experiment
import os
import torch
import numpy as np
from torch.utils.tensorboard import SummaryWriter
from a2c_agent import A2CAgent
import datetime
class MAA2C:
def __init__(self, env, arguments):
self.device = arguments.device
self.env = env
self.save_gif = arguments.save_gif
self.s... | 14,881 | 40.921127 | 319 | py |
PRD | PRD-master/Agent/MAA2C/a2c_model.py | from typing import Any, List, Tuple, Union
import torch
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
import datetime
import math
class MLPPolicyNetwork(nn.Module):
def __init__(self,state_dim,num_agents,action_dim,device):
super(MLPPolicyNetwork,self).__init__()
self.state_dim = stat... | 9,998 | 39.979508 | 158 | py |
himp-gnn | himp-gnn-master/train_tox21.py | import argparse
import torch
from torch.optim import Adam
import numpy as np
from sklearn.metrics import roc_auc_score
from ogb.graphproppred import PygGraphPropPredDataset
from torch_geometric.data import DataLoader
from torch_geometric.transforms import Compose
from transform import JunctionTree
from model import ... | 3,928 | 30.18254 | 79 | py |
himp-gnn | himp-gnn-master/train_zinc_full.py | import argparse
import torch
from torch.optim import Adam
from torch.optim.lr_scheduler import ReduceLROnPlateau
from torch_geometric.datasets import ZINC
from torch_geometric.data import DataLoader
from transform import JunctionTree
from model import Net
parser = argparse.ArgumentParser()
parser.add_argument('--de... | 3,007 | 30.663158 | 79 | py |
himp-gnn | himp-gnn-master/transform.py | import torch
from torch_geometric.data import Data
from torch_geometric.utils import tree_decomposition
from rdkit import Chem
from rdkit.Chem.rdchem import BondType
bonds = [BondType.SINGLE, BondType.DOUBLE, BondType.TRIPLE, BondType.AROMATIC]
def mol_from_data(data):
mol = Chem.RWMol()
x = data.x if data... | 1,651 | 27.982456 | 78 | py |
himp-gnn | himp-gnn-master/model.py | import torch
import torch.nn.functional as F
from torch.nn import Embedding, ModuleList
from torch.nn import Sequential, Linear, BatchNorm1d, ReLU
from torch_scatter import scatter
from torch_geometric.nn import GINConv, GINEConv
class AtomEncoder(torch.nn.Module):
def __init__(self, hidden_channels):
sup... | 6,318 | 34.700565 | 79 | py |
himp-gnn | himp-gnn-master/train_zinc_subset.py | import argparse
import torch
from torch.optim import Adam
from torch.optim.lr_scheduler import ReduceLROnPlateau
from torch_geometric.datasets import ZINC
from torch_geometric.data import DataLoader
from transform import JunctionTree
from model import Net
parser = argparse.ArgumentParser()
parser.add_argument('--de... | 3,047 | 31.084211 | 79 | py |
himp-gnn | himp-gnn-master/train_ogbpcba.py | import argparse
import torch
from torch.optim import Adam
from ogb.graphproppred import PygGraphPropPredDataset, Evaluator
from torch_geometric.data import DataLoader
from torch_geometric.transforms import Compose
from transform import JunctionTree
from model import Net
parser = argparse.ArgumentParser()
parser.add... | 3,435 | 28.878261 | 79 | py |
himp-gnn | himp-gnn-master/train_muv.py | import argparse
import torch
from torch.optim import Adam
import numpy as np
from sklearn.metrics import roc_auc_score
from ogb.graphproppred import PygGraphPropPredDataset
from torch_geometric.data import DataLoader
from torch_geometric.transforms import Compose
from transform import JunctionTree
from model import ... | 3,924 | 30.150794 | 79 | py |
himp-gnn | himp-gnn-master/train_ogbhiv.py | import argparse
import torch
from torch.optim import Adam
from ogb.graphproppred import PygGraphPropPredDataset, Evaluator
from torch_geometric.data import DataLoader
from torch_geometric.transforms import Compose
from transform import JunctionTree
from model import Net
parser = argparse.ArgumentParser()
parser.add... | 3,368 | 28.814159 | 79 | py |
himp-gnn | himp-gnn-master/train_hiv.py | import argparse
import torch
from torch.optim import Adam
import numpy as np
from sklearn.metrics import roc_auc_score
from ogb.graphproppred import PygGraphPropPredDataset
from torch_geometric.data import DataLoader
from torch_geometric.transforms import Compose
from transform import JunctionTree
from model import ... | 3,926 | 30.166667 | 79 | py |
focal-frequency-loss | focal-frequency-loss-master/setup.py | import setuptools
with open("README.md", "r") as fh:
long_description = fh.read()
setuptools.setup(
name="focal_frequency_loss",
version="0.3.0",
author="Liming Jiang",
author_email="liming002@ntu.edu.sg",
description="Focal Frequency Loss for Image Reconstruction and Synthesis - Official PyTo... | 780 | 31.541667 | 112 | py |
focal-frequency-loss | focal-frequency-loss-master/VanillaAE/test.py | from __future__ import print_function
import argparse
import os
import random
import numpy as np
import torch
import torch.backends.cudnn as cudnn
from PIL import Image
from tqdm import tqdm
from networks import MLP
from utils import get_dataloader, print_and_write_log, set_random_seed
parser = argparse.ArgumentPar... | 5,344 | 39.801527 | 130 | py |
focal-frequency-loss | focal-frequency-loss-master/VanillaAE/utils.py | import os
import random
import numpy as np
import torch
import torch.nn.init as init
import torch.utils.data
import torchvision.transforms as transforms
from data import ImageFolderAll, ImageFilelist, ImagePairFilelist
def get_dataloader(opt):
if opt.dataroot is None:
raise ValueError('`dataroot` parame... | 4,870 | 42.882883 | 126 | py |
focal-frequency-loss | focal-frequency-loss-master/VanillaAE/data.py | ###############################################################################
# Code from
# https://github.com/pytorch/vision/blob/master/torchvision/datasets/folder.py
# Modified the original code so that `ImageFolderAll` also loads images from
# the current directory as well as the subdirectories
##################... | 4,016 | 29.9 | 94 | py |
focal-frequency-loss | focal-frequency-loss-master/VanillaAE/networks.py | ###############################################################################
# Part of code from
# https://github.com/NVlabs/MUNIT/blob/master/networks.py
###############################################################################
import torch.nn as nn
class MLP(nn.Module):
def __init__(self, input_dim, o... | 5,895 | 34.95122 | 150 | py |
focal-frequency-loss | focal-frequency-loss-master/VanillaAE/models.py | import os
import torch
import torch.nn as nn
import torch.optim as optim
from focal_frequency_loss import FocalFrequencyLoss as FFL
from networks import MLP
from utils import print_and_write_log, weights_init
class VanillaAE(nn.Module):
def __init__(self, opt):
super(VanillaAE, self).__init__()
... | 2,783 | 31 | 101 | py |
focal-frequency-loss | focal-frequency-loss-master/VanillaAE/train.py | from __future__ import print_function
import argparse
import os
import random
import torch
import torch.backends.cudnn as cudnn
import torchvision.utils as vutils
from tqdm import tqdm
from models import VanillaAE
from utils import get_dataloader, print_and_write_log, set_random_seed
parser = argparse.ArgumentParse... | 5,165 | 46.394495 | 143 | py |
focal-frequency-loss | focal-frequency-loss-master/metrics/metric_utils.py | #######################################################################################
# Part of code from
# https://github.com/open-mmlab/mmediting/blob/master/mmedit/core/evaluation/metrics.py
#######################################################################################
import cv2
import lpips as lpips_or... | 11,939 | 34.430267 | 108 | py |
focal-frequency-loss | focal-frequency-loss-master/focal_frequency_loss/focal_frequency_loss.py | import torch
import torch.nn as nn
# version adaptation for PyTorch > 1.7.1
IS_HIGH_VERSION = tuple(map(int, torch.__version__.split('+')[0].split('.'))) > (1, 7, 1)
if IS_HIGH_VERSION:
import torch.fft
class FocalFrequencyLoss(nn.Module):
"""The torch.nn.Module class that implements focal frequency loss - a... | 5,065 | 43.052174 | 125 | py |
pba | pba-master/pba/data_utils.py | # Copyright 2018 The TensorFlow Authors 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 copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicab... | 20,061 | 44.184685 | 109 | py |
Quatrain | Quatrain-master/learn/tool.py | """
sentence2vec : transfer sentences to vectors
loaddata: load train/test/valid datas
evaluation_metrics: return scores of (recall_p, recall_n, acc, prc, rc, f1, auc_)
"""
import numpy as np
import torch
from sklearn.metrics import roc_curve, auc, accuracy_score, recall_score, precision_score
from sklearn.metrics imp... | 3,856 | 35.733333 | 116 | py |
Quatrain | Quatrain-master/learn/test.py | import torch
import torch.nn as nn
import torch.optim
import numpy as np
seed = 3
torch.manual_seed(seed)
torch.backends.cudnn.deterministric = True
torch.backends.cudnn.benchmark = False
import sys
sys.path.append('../preprocess/')
from tool import sentence2vec, loaddata, rightness, evaluation_metrics
from constant... | 4,545 | 35.66129 | 207 | py |
Quatrain | Quatrain-master/experiment/ML4Prediciton.py | from sklearn.preprocessing import StandardScaler, MinMaxScaler, Normalizer
from sklearn.tree import DecisionTreeClassifier
from sklearn.linear_model import LogisticRegression
from sklearn.ensemble import RandomForestClassifier
from sklearn.naive_bayes import GaussianNB
from sklearn.model_selection import KFold, Stratif... | 6,175 | 45.787879 | 238 | py |
Quatrain | Quatrain-master/preprocess/data_util.py |
import json
import sys
import time
from constants import Output_DATA_DIR, Origin_DATA_DIR
<<<<<<< HEAD
import nltk
from nltk.corpus import stopwords
=======
>>>>>>> 45fdc9b5e77fd91508f00a1412fc0e9b4c6aaadd
#PyTorch-packages
import torch
import torch.nn as nn
import torch.optim
#from torch.autograd import Variable
#... | 6,876 | 29.295154 | 119 | py |
Quatrain | Quatrain-master/preprocess/bugReportDownloader.py | import sys
import json
import os
import pickle
import time
import requests
from urllib.request import urlopen
from urllib import error
import urllib
import logging
import socket
from bs4 import BeautifulSoup
from lxml import etree
timeout = 30
socket.setdefaulttimeout(timeout)
hdr = {
'User-Agent': 'Mozilla/5.0 ... | 6,251 | 32.079365 | 125 | py |
Quatrain | Quatrain-master/CC2Vec/lmg_cli.py | import argparse
import pickle
import re
from os import walk, path
from CC2Vec.lmg_padding import processing_data
from CC2Vec.lmg_utils import mini_batches, commit_msg_label
from tqdm import tqdm
import torch
from CC2Vec.lmg_cc2ftr_model import HierachicalRNN
def read_args_lmg():
parser = argparse.ArgumentParser()
... | 7,009 | 43.649682 | 149 | py |
Quatrain | Quatrain-master/CC2Vec/lmg_cc2ftr_train.py | from CC2Vec.lmg_utils import mini_batches, commit_msg_label
import os
import datetime
import torch.nn as nn
from tqdm import tqdm
import torch
from CC2Vec.lmg_cc2ftr_model import HierachicalRNN
from CC2Vec.lmg_utils import save
def train_model(data, params):
msg, pad_added_code, pad_removed_code, dict_msg, dict_c... | 2,170 | 39.962264 | 133 | py |
Quatrain | Quatrain-master/CC2Vec/lmg_cc2ftr_extracted.py | from CC2Vec.lmg_utils import mini_batches, commit_msg_label
from tqdm import tqdm
import torch
from CC2Vec.lmg_cc2ftr_model import HierachicalRNN
import pickle
def extracted_cc2ftr(data, params):
msg, pad_added_code, pad_removed_code, dict_msg, dict_code = data
labels = commit_msg_label(data=msg, dict_msg=dict... | 1,742 | 42.575 | 149 | py |
Quatrain | Quatrain-master/CC2Vec/lmg_cc2ftr_model.py | import torch
import torch.nn.functional as F
import torch.nn as nn
import numpy as np
from torch.autograd import Variable
# Make the the multiple attention with word vectors.
def attention_mul(rnn_outputs, att_weights):
attn_vectors = None
for i in range(rnn_outputs.size(0)):
h_i = rnn_outputs[i]
... | 10,209 | 41.719665 | 116 | py |
Quatrain | Quatrain-master/CC2Vec/lmg_utils.py | import os
import torch
import numpy as np
import math
import re
def commit_msg_label(data, dict_msg):
labels_ = np.array([1 if w in d.split() else 0 for d in data for w in dict_msg])
labels_ = np.reshape(labels_, (int(labels_.shape[0] / len(dict_msg)), len(dict_msg)))
return labels_
def save(model, save_... | 4,294 | 37.348214 | 115 | py |
Quatrain | Quatrain-master/representation/CC2Vec/lmg_cli.py | import argparse
import pickle
import re
from os import walk, path
from representation.CC2Vec.lmg_padding import processing_data
from representation.CC2Vec.lmg_utils import mini_batches, commit_msg_label
from tqdm import tqdm
import torch
from representation.CC2Vec.lmg_cc2ftr_model import HierachicalRNN
def read_args_l... | 7,821 | 44.213873 | 149 | py |
Quatrain | Quatrain-master/representation/CC2Vec/lmg_cc2ftr_train.py | from representation.CC2Vec.lmg_utils import mini_batches, commit_msg_label
import os
import datetime
import torch.nn as nn
from tqdm import tqdm
import torch
from representation.CC2Vec.lmg_cc2ftr_model import HierachicalRNN
from representation.CC2Vec.lmg_utils import save
def train_model(data, params):
msg, pad_a... | 2,215 | 40.811321 | 133 | py |
Quatrain | Quatrain-master/representation/CC2Vec/lmg_cc2ftr_extracted.py | from .lmg_utils import mini_batches, commit_msg_label
import tqdm
import torch
from representation.CC2Vec.lmg_cc2ftr_model import HierachicalRNN
import pickle
def extracted_cc2ftr(data, params):
msg, pad_added_code, pad_removed_code, dict_msg, dict_code = data
labels = commit_msg_label(data=msg, dict_msg=dict_... | 1,741 | 42.55 | 149 | py |
Quatrain | Quatrain-master/representation/CC2Vec/lmg_cc2ftr_model.py | import torch
import torch.nn.functional as F
import torch.nn as nn
import numpy as np
from torch.autograd import Variable
# Make the the multiple attention with word vectors.
def attention_mul(rnn_outputs, att_weights):
attn_vectors = None
for i in range(rnn_outputs.size(0)):
h_i = rnn_outputs[i]
... | 10,125 | 41.725738 | 116 | py |
Quatrain | Quatrain-master/representation/CC2Vec/lmg_utils.py | import os
import torch
import numpy as np
import math
import re
def commit_msg_label(data, dict_msg):
labels_ = np.array([1 if w in d.split() else 0 for d in data for w in dict_msg])
labels_ = np.reshape(labels_, (int(labels_.shape[0] / len(dict_msg)), len(dict_msg)))
return labels_
def save(model, save_... | 4,294 | 37.348214 | 115 | py |
MvMM-RegNet | MvMM-RegNet-master/src_3d/core/model_ddf_mvmm_label_base.py | # -*- coding: utf-8 -*-
"""
Unified Multi-Atlas Segmentation implementations using dense displacement fields for model construction and training.
The optimization is based on the multivariate mixture model of the target image and atlas probabilistic model.
@author: Xinzhe Luo
"""
from __future__ import print_function... | 67,761 | 59.447814 | 124 | py |
MvMM-RegNet | MvMM-RegNet-master/src_3d/core/radam.py | import tensorflow as tf
from tensorflow.python.eager import context
from tensorflow.python.framework import ops
from tensorflow.python.ops import control_flow_ops
from tensorflow.python.ops import math_ops, state_ops, array_ops
from tensorflow.python.ops import resource_variable_ops
from tensorflow.python.training impo... | 13,125 | 47.795539 | 119 | py |
MvMM-RegNet | MvMM-RegNet-master/src_3d/core/image_dataset.py | # -*- coding: utf-8 -*-
"""
Image IO and pre-processing for testing multi-stage registration network of the unified multi-atlas segmentation
framework.
@author: Xinzhe Luo
"""
from __future__ import print_function, division, absolute_import, unicode_literals
import glob
# import random
import itertools
import loggin... | 29,477 | 52.209386 | 179 | py |
MvMM-RegNet | MvMM-RegNet-master/src_3d/core/networks.py | # -*- coding: utf-8 -*-
"""
Network architectures for medical image registration.
@author: Xinzhe Luo
"""
from __future__ import print_function, division, absolute_import, unicode_literals
from core.layers import *
from collections import OrderedDict
import logging
logging.basicConfig(level=logging.INFO, format='%(a... | 20,757 | 56.501385 | 121 | py |
MvMM-RegNet | MvMM-RegNet-master/src_3d/core/layers.py | # -*- coding: utf-8 -*-
"""
Auxiliary functions and operations for network construction, some of which have
been deprecated for high-level modules in TensorFlow.
@author: Xinzhe Luo
"""
from __future__ import print_function, division, absolute_import, unicode_literals
import tensorflow as tf
import numpy as np
from c... | 42,974 | 45.309267 | 123 | py |
MvMM-RegNet | MvMM-RegNet-master/src_2d/core/image_2d_dataset.py | # -*- coding: utf-8 -*-
"""
Image IO and pre-processing for input pipeline.
@author: Xinzhe Luo
"""
from __future__ import print_function, division, absolute_import, unicode_literals
import glob
from random import sample
import itertools
import logging
# import cv2
import os
# import nibabel as nib
from PIL import I... | 27,785 | 50.55102 | 139 | py |
MvMM-RegNet | MvMM-RegNet-master/src_2d/core/model_2d_ddf_mvmm_label_base.py | # -*- coding: utf-8 -*-
"""
Unified Multi-Atlas Segmentation implementations using dense displacement fields for model construction and training.
The optimization is based on the multi-variate mixture model of the target image and atlas probabilistic labels.
@author: Xinzhe Luo
"""
from __future__ import print_functi... | 63,712 | 59.794847 | 129 | py |
MvMM-RegNet | MvMM-RegNet-master/src_2d/core/layers_2d.py | # -*- coding: utf-8 -*-
"""
Auxiliary functions and operations for network construction, some of which have
been deprecated for high-level modules in TensorFlow.
@author: Xinzhe Luo
"""
from __future__ import print_function, division, absolute_import, unicode_literals
import tensorflow as tf
import numpy as np
from c... | 41,961 | 42.80167 | 123 | py |
MvMM-RegNet | MvMM-RegNet-master/src_2d/core/networks_2d.py | # -*- coding: utf-8 -*-
"""
Network architectures for medical image registration.
@author: Xinzhe Luo
"""
from __future__ import print_function, division, absolute_import, unicode_literals
from core.layers_2d import *
from collections import OrderedDict
import logging
logging.basicConfig(level=logging.INFO, format='... | 19,976 | 61.040373 | 125 | py |
ImageNet21K | ImageNet21K-main/train_single_label_from_scratch.py | # --------------------------------------------------------
# ImageNet-21K Pretraining for The Masses
# Copyright 2021 Alibaba MIIL (c)
# Licensed under MIT License [see the LICENSE file for details]
# Written by Tal Ridnik
# --------------------------------------------------------
import argparse
import time
import to... | 4,730 | 35.392308 | 120 | py |
ImageNet21K | ImageNet21K-main/train_single_label.py | # --------------------------------------------------------
# ImageNet-21K Pretraining for The Masses
# Copyright 2021 Alibaba MIIL (c)
# Licensed under MIT License [see the LICENSE file for details]
# Written by Tal Ridnik
# --------------------------------------------------------
import argparse
import time
import to... | 4,691 | 35.092308 | 120 | py |
ImageNet21K | ImageNet21K-main/train_semantic_softmax.py | # --------------------------------------------------------
# ImageNet-21K Pretraining for The Masses
# Copyright 2021 Alibaba MIIL (c)
# Licensed under MIT License [see the LICENSE file for details]
# Written by Tal Ridnik
# --------------------------------------------------------
import argparse
import time
import to... | 4,747 | 36.385827 | 119 | py |
ImageNet21K | ImageNet21K-main/visualize_detector.py | # --------------------------------------------------------
# ImageNet-21K Pretraining for The Masses
# Copyright 2021 Alibaba MIIL (c)
# Licensed under MIT License [see the LICENSE file for details]
# Written by Tal Ridnik
# --------------------------------------------------------
import os
import urllib
from argparse... | 2,950 | 33.717647 | 127 | py |
ImageNet21K | ImageNet21K-main/tests/test_semantic_softmax_loss.py |
# test_semantic_softmax_loss.py
# tests auto-generated by https://www.codium.ai/
# testing https://github.com/Alibaba-MIIL/ImageNet21K/blob/2715baf0e38673809812409678f7d12e592cbaba/tests/test_semantic_softmax_loss.py#L5 class
import unittest
from src_files.semantic.semantic_loss import SemanticSoftmaxLoss
from src_f... | 4,847 | 52.866667 | 172 | py |
ImageNet21K | ImageNet21K-main/src_files/models/ofa/utils.py | # Adopted from https://github.com/mit-han-lab/once-for-all:
# Once for All: Train One Network and Specialize it for Efficient Deployment
# Han Cai, Chuang Gan, Tianzhe Wang, Zhekai Zhang, Song Han
# International Conference on Learning Representations (ICLR), 2020.
# ----------------------------------------------------... | 9,919 | 30.097179 | 118 | py |
ImageNet21K | ImageNet21K-main/src_files/models/ofa/layers.py | # Adopted from https://github.com/mit-han-lab/once-for-all:
# Once for All: Train One Network and Specialize it for Efficient Deployment
# Han Cai, Chuang Gan, Tianzhe Wang, Zhekai Zhang, Song Han
# International Conference on Learning Representations (ICLR), 2020.
# ----------------------------------------------------... | 17,619 | 32.120301 | 118 | py |
ImageNet21K | ImageNet21K-main/src_files/models/utils/factory.py | import torch
import timm
from ..ofa.model_zoo import ofa_flops_595m_s
from ..tresnet import TResnetM, TResnetL
from src_files.helper_functions.distributed import print_at_master
def load_model_weights(model, model_path):
state = torch.load(model_path, map_location='cpu')
for key in model.state_dict():
... | 2,490 | 41.220339 | 119 | py |
ImageNet21K | ImageNet21K-main/src_files/models/tresnet/tresnet.py | import torch
import torch.nn as nn
from torch.nn import Module as Module
from collections import OrderedDict
from src_files.models.tresnet.layers.anti_aliasing import AntiAliasDownsampleLayer
from .layers.avg_pool import FastAvgPool2d
from .layers.general_layers import SEModule, SpaceToDepthModule
from inplace_abn impo... | 9,343 | 39.803493 | 112 | py |
ImageNet21K | ImageNet21K-main/src_files/models/tresnet/layers/anti_aliasing.py | import torch
import torch.nn.parallel
import numpy as np
import torch.nn as nn
import torch.nn.functional as F
class AntiAliasDownsampleLayer(nn.Module):
def __init__(self, remove_model_jit: bool = False, filt_size: int = 3, stride: int = 2,
channels: int = 0):
super(AntiAliasDownsampleLa... | 2,035 | 32.377049 | 99 | py |
ImageNet21K | ImageNet21K-main/src_files/models/tresnet/layers/general_layers.py | import torch
import torch.nn as nn
import torch.nn.functional as F
from src_files.models.tresnet.layers.avg_pool import FastAvgPool2d
class Flatten(nn.Module):
def forward(self, x):
return x.view(x.size(0), -1)
class DepthToSpace(nn.Module):
def __init__(self, block_size):
super().__init__... | 2,996 | 30.882979 | 102 | py |
ImageNet21K | ImageNet21K-main/src_files/models/tresnet/layers/avg_pool.py | import torch
import torch.nn as nn
import torch.nn.functional as F
class FastAvgPool2d(nn.Module):
def __init__(self, flatten=False):
super(FastAvgPool2d, self).__init__()
self.flatten = flatten
def forward(self, x):
if self.flatten:
in_size = x.size()
return ... | 479 | 23 | 93 | py |
ImageNet21K | ImageNet21K-main/src_files/helper_functions/distributed.py | import os
import torch
import torch.distributed as dist
def get_dist_info():
initialized = dist.is_available() and dist.is_initialized()
if initialized:
rank = dist.get_rank()
world_size = dist.get_world_size()
else:
rank = 0
world_size = 1
return rank, world_size
def... | 1,023 | 20.333333 | 94 | py |
ImageNet21K | ImageNet21K-main/src_files/data_loading/data_loader.py | import os
import torch
from randaugment import RandAugment
from torchvision import transforms
from torchvision.datasets import ImageFolder
from src_files.helper_functions.augmentations import CutoutPIL
from src_files.helper_functions.distributed import num_distrib, print_at_master
from timm.data.loader import Ordered... | 3,393 | 33.282828 | 131 | py |
ImageNet21K | ImageNet21K-main/src_files/optimizers/create_optimizer.py | import torch
from torch.optim import lr_scheduler
from src_files.helper_functions.general_helper_functions import add_weight_decay
from src_files.loss_functions.losses import CrossEntropyLS
def create_optimizer(model, args):
parameters = add_weight_decay(model, args.weight_decay)
optimizer = torch.optim.Adam... | 652 | 35.277778 | 110 | py |
ImageNet21K | ImageNet21K-main/src_files/loss_functions/losses.py | import torch
import torch.nn as nn
class CrossEntropyLS(nn.Module):
def __init__(self, eps: float = 0.2):
super(CrossEntropyLS, self).__init__()
self.eps = eps
self.logsoftmax = nn.LogSoftmax(dim=-1)
def forward(self, inputs, target):
num_classes = inputs.size()[-1]
log... | 689 | 37.333333 | 93 | py |
ImageNet21K | ImageNet21K-main/src_files/semantic/semantics.py | import torch
import numpy as np
from torch import Tensor
@torch.jit.script
def stable_softmax(logits: torch.Tensor):
logits_m = logits - logits.max(dim=1)[0].unsqueeze(1)
exp = torch.exp(logits_m)
probs = exp / torch.sum(exp, dim=1).unsqueeze(1)
return probs
class ImageNet21kSemanticSoftmax:
def... | 6,255 | 46.037594 | 112 | py |
ImageNet21K | ImageNet21K-main/src_files/semantic/semantic_loss.py | import torch
import torch.nn.functional as F
class SemanticSoftmaxLoss(torch.nn.Module):
def __init__(self, semantic_softmax_processor):
super(SemanticSoftmaxLoss, self).__init__()
self.semantic_softmax_processor = semantic_softmax_processor
self.args = semantic_softmax_processor.args
... | 1,991 | 39.653061 | 110 | py |
ImageNet21K | ImageNet21K-main/src_files/semantic/metrics.py | import torch
from src_files.helper_functions.distributed import reduce_tensor, num_distrib
class AccuracySemanticSoftmaxMet:
"Average the values of `func` taking into account potential different batch sizes"
def __init__(self, semantic_softmax_processor):
self.semantic_softmax_processor = semantic_s... | 2,108 | 38.055556 | 112 | py |
DTAAD | DTAAD-main/main.py | import pickle
import os
import pandas as pd
from tqdm import tqdm
from src.models import *
from src.constants import *
from src.plotting import *
from src.pot import *
from src.utils import *
from src.diagnosis import *
from torch.utils.data import Dataset, DataLoader, TensorDataset
import torch.nn as nn
from time impo... | 18,608 | 41.583524 | 121 | py |
DTAAD | DTAAD-main/src/plotting.py | import matplotlib.pyplot as plt
from matplotlib.backends.backend_pdf import PdfPages
import statistics
import os, torch
import numpy as np
plt.style.use(['science', 'ieee'])
plt.rcParams["text.usetex"] = False
plt.rcParams['figure.figsize'] = 6, 2
os.makedirs('plots', exist_ok=True)
def smooth(y, box_pts=1):
bo... | 2,304 | 42.490566 | 119 | py |
DTAAD | DTAAD-main/src/gltcn.py | import math
import torch.nn as nn
from torch.nn.utils import weight_norm
class Chomp1d(nn.Module):
def __init__(self, chomp_size):
super(Chomp1d, self).__init__()
self.chomp_size = chomp_size
def forward(self, x):
"""
In fact, this is a cropping module, cropping the extra righ... | 5,530 | 45.478992 | 191 | py |
DTAAD | DTAAD-main/src/dlutils.py | import torch.nn as nn
import torch
import torch.nn.functional as F
from torch.autograd import Variable
import math
import numpy as np
from src.parser import args
class ConvLSTMCell(nn.Module):
def __init__(self, input_dim, hidden_dim, kernel_size, bias):
"""
Initialize ConvLSTM cell.
Par... | 12,827 | 36.182609 | 111 | py |
DTAAD | DTAAD-main/src/models.py | import torch
import torch.nn as nn
import torch.optim as optim
import pickle
import dgl.nn
from dgl.nn.pytorch import GATConv
from torch.nn import TransformerEncoder
from src.gltcn import *
from src.dlutils import *
from src.constants import *
torch.manual_seed(1)
torch.cuda.manual_seed(1)
## Separate LSTM for each ... | 22,721 | 39.003521 | 120 | py |
path-space-PDE-solver | path-space-PDE-solver-master/utilities.py | #pylint: disable=invalid-name, no-member, too-many-arguments, missing-docstring
#pylint: disable=too-many-branches
from datetime import date
import json
import matplotlib.pyplot as plt
import matplotlib.cm as cm
import numpy as np
import torch as pt
COLORS = ['tab:blue', 'tab:orange', 'tab:green', 'tab:red', 'tab:p... | 21,036 | 41.845214 | 213 | py |
path-space-PDE-solver | path-space-PDE-solver-master/problems.py | #pylint: disable=invalid-name, no-member, too-many-arguments, unused-argument, missing-docstring, too-many-instance-attributes
import numpy as np
import torch as pt
from numpy import exp, log
from scipy.linalg import expm, inv, solve_banded
device = pt.device('cuda')
class LLGC():
'''
Ornstein-Uhlenb... | 61,480 | 33.833428 | 177 | py |
path-space-PDE-solver | path-space-PDE-solver-master/solver.py | #pylint: disable=invalid-name, no-member, too-many-arguments, missing-docstring
#pylint: disable=too-many-instance-attributes, not-callable, no-else-return
#pylint: disable=inconsistent-return-statements, too-many-locals, too-many-return-statements
#pylint: disable=too-many-statements, too-many-public-methods
from cop... | 69,944 | 51.82855 | 264 | py |
path-space-PDE-solver | path-space-PDE-solver-master/function_space.py | #pylint: disable=invalid-name, no-member, too-many-arguments, missing-docstring, arguments-differ, unused-argument
import torch as pt
class SingleParam(pt.nn.Module):
def __init__(self, lr, initial=None, seed=42):
super(SingleParam, self).__init__()
pt.manual_seed(seed)
if initial is None... | 7,450 | 37.015306 | 127 | py |
LyDROO | LyDROO-main/memory.py | # #################################################################
# This file contains the main DROO operations, including building DNN,
# Storing data sample, Training DNN, and generating quantized binary offloading decisions.
# version 1.0 -- February 2020. Written based on Tensorflow 2 by Weijian Pan and
# ... | 5,141 | 30.937888 | 109 | py |
LyDROO | LyDROO-main/memoryTF2conv.py | # #################################################################
# This file contains the main LyDROO operations, including building convolutional DNN,
# Storing data sample, Training DNN, and generating quantized binary offloading decisions.
# version 1.0 -- January 2021. Written based on Tensorflow 2
# Lia... | 5,933 | 35.857143 | 146 | py |
CAFE | CAFE-master/train_neural_symbol.py | from __future__ import absolute_import, division, print_function
import os
import sys
import argparse
import numpy as np
import pickle
from tqdm import tqdm
import logging
import logging.handlers
import torch
import torch.nn as nn
from torch.nn import functional as F
import torch.optim as optim
from tensorboardX impor... | 4,183 | 36.026549 | 100 | py |
CAFE | CAFE-master/utils.py | from __future__ import absolute_import, division, print_function
import os
import random
import argparse
import pickle
import numpy as np
import gzip
import scipy.sparse as sp
from sklearn.feature_extraction.text import TfidfTransformer
import torch
BEAUTY = 'beauty'
CELL = 'cell'
CLOTH = 'clothing'
CD = 'cd'
DATA_D... | 6,173 | 34.687861 | 122 | py |
CAFE | CAFE-master/data_utils.py | from __future__ import absolute_import, division, print_function
import sys
import os
import argparse
import pickle
import random
import time
import numpy as np
from math import log
from tqdm import tqdm
import gzip
import torch
from torch.utils.data import RandomSampler, DataLoader
# from datasets import AmazonDatas... | 11,010 | 33.195652 | 108 | py |
CAFE | CAFE-master/execute_neural_symbol.py | from __future__ import absolute_import, division, print_function
import os
import sys
import argparse
import time
import random
import numpy as np
import pickle
import logging
import logging.handlers
import math
from tqdm import tqdm
import torch
import torch.nn as nn
from torch.nn import functional as F
import torch.... | 13,403 | 34.935657 | 108 | py |
CAFE | CAFE-master/symbolic_model.py | from __future__ import absolute_import, division, print_function
import numpy as np
import torch
from torch import nn
from torch.nn import functional as F
from my_knowledge_graph import *
import utils
class EntityEmbeddingModel(nn.Module):
def __init__(self, entity_info, embed_size, init_embed=None):
su... | 12,627 | 39.867314 | 111 | py |
ssl-chewing | ssl-chewing-master/src/experiments.py | """
Experiment based on the SimCLR idea [1]
- LOSO experiment
- dataset: wu2
- input: windows
- ground-truth: chewing vs non-chewing or eating vs non-eating
b) Training
- model: combination of [1] and [2]
b) Evaluate on wu2 LOSO
[1] https://github.com/google-research/simclr
[2] https://ieeexplore.ieee.org/abstract... | 15,639 | 42.085399 | 119 | py |
ssl-chewing | ssl-chewing-master/src/evaluation/callbacks.py | from tensorflow.keras.backend import get_value
from tensorflow.keras.callbacks import Callback
from dataset.wu1.subset import Subset
from evaluation.metrics import bml_accuracy, bml_f1score, bml_recall, bml_precision
class ValidationResultsPerEpoch(Callback):
def __init__(self, validation_set: Subset, show_only_... | 1,805 | 40.045455 | 110 | py |
ssl-chewing | ssl-chewing-master/src/evaluation/metrics.py | import numpy as np
import tensorflow as tf
from sklearn.metrics import confusion_matrix
from tensorflow.keras import backend as kbackend
class BMLAccuracy(tf.keras.metrics.Metric):
def __init__(self, name="bml_acc", **kwargs):
super(BMLAccuracy, self).__init__(name=name, **kwargs)
self.cm = self.a... | 3,960 | 30.188976 | 114 | py |
ssl-chewing | ssl-chewing-master/src/dataset/labeltransform.py | from abc import ABC, abstractmethod
import numpy as np
from tensorflow.keras.utils import to_categorical
from dataset.template.commons import PureAbstractError
class BaseLabelTransform(ABC):
@abstractmethod
def transform_batch(self, x: np.ndarray) -> np.ndarray:
raise PureAbstractError()
class Ca... | 961 | 24.315789 | 59 | py |
ssl-chewing | ssl-chewing-master/src/dataset/wu1/subset.py | from typing import List, Tuple, Union
import numpy as np
from dataset.commons import PartitionMode, SubsetType
from dataset.template.basesubset import BaseSubset
from dataset.wu1.commons import LabelMode
from dataset.wu1.wu1experiment import WU1Experiment
from utilities.numpyutils import is_numpy_1d_vector
def _spl... | 6,863 | 34.381443 | 119 | py |
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