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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Traffic-Benchmark | Traffic-Benchmark-master/methods/MTGNN/train_multi_step.py | import torch
import numpy as np
import argparse
import time
from util import *
from trainer import Trainer
from net import gtnet
import setproctitle
setproctitle.setproctitle("MTGNN@lifuxian")
def str_to_bool(value):
if isinstance(value, bool):
return value
if value.lower() in {'false', 'f', '0', 'no', ... | 11,855 | 38.52 | 178 | py |
Traffic-Benchmark | Traffic-Benchmark-master/methods/MTGNN/trainer.py | import torch.optim as optim
import math
from net import *
import util
class Trainer():
def __init__(self, model, lrate, wdecay, clip, step_size, seq_out_len, scaler, device, cl=True):
self.scaler = scaler
self.model = model
self.model.to(device)
self.optimizer = optim.Adam(self.model... | 4,312 | 34.644628 | 102 | py |
Traffic-Benchmark | Traffic-Benchmark-master/methods/LSTM/dcrnn_train_pytorch.py | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import argparse
import yaml
from lib.utils import load_graph_data
from model.pytorch.dcrnn_supervisor import DCRNNSupervisor
import setproctitle
setproctitle.setproctitle("stmetanet@lifuxian")
def main(args):... | 1,459 | 38.459459 | 129 | py |
Traffic-Benchmark | Traffic-Benchmark-master/methods/LSTM/run_demo_pytorch.py | import argparse
import numpy as np
import os
import sys
import yaml
from lib.utils import load_graph_data
from model.pytorch.dcrnn_supervisor import DCRNNSupervisor
def run_dcrnn(args):
with open(args.config_filename) as f:
supervisor_config = yaml.load(f)
graph_pkl_filename = supervisor_config[... | 1,264 | 36.205882 | 108 | py |
Traffic-Benchmark | Traffic-Benchmark-master/methods/LSTM/model/pytorch/dcrnn_model.py | import numpy as np
import torch
import torch.nn as nn
from model.pytorch.dcrnn_cell import DCGRUCell
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
def count_parameters(model):
return sum(p.numel() for p in model.parameters() if p.requires_grad)
class Seq2SeqAttrs:
def __init__(self... | 29,536 | 40.31049 | 124 | py |
Traffic-Benchmark | Traffic-Benchmark-master/methods/LSTM/model/pytorch/dcrnn_cell.py | import numpy as np
import torch
from lib import utils
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
class LayerParams:
def __init__(self, rnn_network: torch.nn.Module, layer_type: str):
self._rnn_network = rnn_network
self._params_dict = {}
self._biases_dict = {}
... | 6,939 | 41.576687 | 105 | py |
Traffic-Benchmark | Traffic-Benchmark-master/methods/LSTM/model/pytorch/utils.py | import torch
import numpy as np
def masked_mae_loss(y_pred, y_true):
mask = (y_true != 0).float()
mask /= mask.mean()
loss = torch.abs(y_pred - y_true)
loss = loss * mask
# trick for nans: https://discuss.pytorch.org/t/how-to-set-nan-in-tensor-to-0/3918/3
loss[loss != loss] = 0
return loss... | 2,390 | 30.051948 | 88 | py |
Traffic-Benchmark | Traffic-Benchmark-master/methods/LSTM/model/pytorch/dcrnn_supervisor.py | import os
import time
import numpy as np
import torch
import torch.nn as nn
# from torch.utils.tensorboard import SummaryWriter
from lib import utils
# from model.pytorch.dcrnn_model import DCRNNModel
from model.pytorch.dcrnn_model import STMetaNet
from model.pytorch.utils import masked_mae_loss, metric, get_normaliz... | 16,974 | 40.605392 | 129 | py |
Traffic-Benchmark | Traffic-Benchmark-master/methods/STGCN/dcrnn_train_pytorch.py | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import argparse
import yaml
from lib.utils import load_graph_data
from model.pytorch.dcrnn_supervisor import DCRNNSupervisor
import setproctitle
setproctitle.setproctitle("stgcn@lifuxian")
def main(args):
... | 1,455 | 38.351351 | 129 | py |
Traffic-Benchmark | Traffic-Benchmark-master/methods/STGCN/run_demo_pytorch.py | import argparse
import numpy as np
import os
import sys
import yaml
from lib.utils import load_graph_data
from model.pytorch.dcrnn_supervisor import DCRNNSupervisor
def run_dcrnn(args):
with open(args.config_filename) as f:
supervisor_config = yaml.load(f)
graph_pkl_filename = supervisor_config[... | 1,264 | 36.205882 | 108 | py |
Traffic-Benchmark | Traffic-Benchmark-master/methods/STGCN/model/pytorch/dcrnn_model.py | import numpy as np
import torch
import torch.nn as nn
from model.pytorch.dcrnn_cell import DCGRUCell
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
def count_parameters(model):
return sum(p.numel() for p in model.parameters() if p.requires_grad)
class Seq2SeqAttrs:
def __init__(self... | 13,218 | 41.779935 | 119 | py |
Traffic-Benchmark | Traffic-Benchmark-master/methods/STGCN/model/pytorch/dcrnn_cell.py | import numpy as np
import torch
from lib import utils
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
class LayerParams:
def __init__(self, rnn_network: torch.nn.Module, layer_type: str):
self._rnn_network = rnn_network
self._params_dict = {}
self._biases_dict = {}
... | 6,939 | 41.576687 | 105 | py |
Traffic-Benchmark | Traffic-Benchmark-master/methods/STGCN/model/pytorch/utils.py | import torch
import numpy as np
def masked_mae_loss(y_pred, y_true):
mask = (y_true != 0).float()
mask /= mask.mean()
loss = torch.abs(y_pred - y_true)
loss = loss * mask
# trick for nans: https://discuss.pytorch.org/t/how-to-set-nan-in-tensor-to-0/3918/3
loss[loss != loss] = 0
return loss... | 3,175 | 30.76 | 88 | py |
Traffic-Benchmark | Traffic-Benchmark-master/methods/STGCN/model/pytorch/dcrnn_supervisor.py | import os
import time
import numpy as np
import torch
import torch.nn as nn
# from torch.utils.tensorboard import SummaryWriter
from lib import utils
# from model.pytorch.dcrnn_model import DCRNNModel
from model.pytorch.dcrnn_model import STGCN
from model.pytorch.utils import masked_mae_loss, metric, get_normalized_a... | 17,411 | 41.8867 | 129 | py |
Traffic-Benchmark | Traffic-Benchmark-master/methods/DGCRN/layer.py | from __future__ import division
import torch
import torch.nn as nn
from torch.nn import init
import numbers
import torch.nn.functional as F
from collections import OrderedDict
class gconv_RNN(nn.Module):
def __init__(self):
super(gconv_RNN, self).__init__()
def forward(self, x, A):
x = torch... | 2,007 | 27.28169 | 79 | py |
Traffic-Benchmark | Traffic-Benchmark-master/methods/DGCRN/net.py | import torch.utils.data as utils
import torch.nn.functional as F
import torch
import torch.nn as nn
from torch.autograd import Variable
from torch.nn.parameter import Parameter
import numpy as np
import pandas as pd
import math
import time
from layer import *
import sys
from collections import OrderedDict
class DGCRN... | 10,196 | 36.215328 | 79 | py |
Traffic-Benchmark | Traffic-Benchmark-master/methods/DGCRN/util.py | import pickle
import numpy as np
import os
import scipy.sparse as sp
import torch
from scipy.sparse import linalg
from torch.autograd import Variable
def normal_std(x):
return x.std() * np.sqrt((len(x) - 1.) / (len(x)))
class DataLoaderS(object):
def __init__(self,
file_name,
... | 12,210 | 31.562667 | 112 | py |
Traffic-Benchmark | Traffic-Benchmark-master/methods/DGCRN/train.py | import torch
import numpy as np
import argparse
import time
from util import *
from trainer import Trainer
from net import DGCRN
import setproctitle
import os
setproctitle.setproctitle("DGCRN@lifuxian")
def str_to_bool(value):
if isinstance(value, bool):
return value
if value.lower() in {'false', 'f... | 15,124 | 35.184211 | 186 | py |
Traffic-Benchmark | Traffic-Benchmark-master/methods/DGCRN/trainer.py | import torch.optim as optim
import math
from net import *
import util
class Trainer():
def __init__(self,
model,
lrate,
wdecay,
clip,
step_size,
seq_out_len,
scaler,
device,
... | 3,313 | 33.520833 | 78 | py |
Traffic-Benchmark | Traffic-Benchmark-master/methods/AGCRN/model/AGCRN.py | import torch
import torch.nn as nn
from model.AGCRNCell import AGCRNCell
class AVWDCRNN(nn.Module):
def __init__(self, node_num, dim_in, dim_out, cheb_k, embed_dim, num_layers=1):
super(AVWDCRNN, self).__init__()
assert num_layers >= 1, 'At least one DCRNN layer in the Encoder.'
self.node_n... | 3,454 | 44.460526 | 113 | py |
Traffic-Benchmark | Traffic-Benchmark-master/methods/AGCRN/model/Run_PEMS-BAY.py | import os
import sys
file_dir = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
print(file_dir)
sys.path.append(file_dir)
import torch
import numpy as np
import torch.nn as nn
import argparse
import configparser
from datetime import datetime
from model.AGCRN import AGCRN as Network
from model.BasicTrainer... | 6,953 | 32.921951 | 79 | py |
Traffic-Benchmark | Traffic-Benchmark-master/methods/AGCRN/model/AGCRN_debug.py | import torch
import torch.nn as nn
from model.AGCRNCell import AGCRNCell
class AVWDCRNN(nn.Module):
def __init__(self, node_num, dim_in, dim_out, cheb_k, embed_dim, num_layers=1):
super(AVWDCRNN, self).__init__()
assert num_layers >= 1, 'At least one DCRNN layer in the Encoder.'
self.node_n... | 4,677 | 41.917431 | 113 | py |
Traffic-Benchmark | Traffic-Benchmark-master/methods/AGCRN/model/AGCN.py | import torch
import torch.nn.functional as F
import torch.nn as nn
class AVWGCN(nn.Module):
def __init__(self, dim_in, dim_out, cheb_k, embed_dim):
super(AVWGCN, self).__init__()
self.cheb_k = cheb_k
self.weights_pool = nn.Parameter(torch.FloatTensor(embed_dim, cheb_k, dim_in, dim_out))
... | 1,453 | 54.923077 | 112 | py |
Traffic-Benchmark | Traffic-Benchmark-master/methods/AGCRN/model/AGCRNCell.py | import torch
import torch.nn as nn
from model.AGCN import AVWGCN
class AGCRNCell(nn.Module):
def __init__(self, node_num, dim_in, dim_out, cheb_k, embed_dim):
super(AGCRNCell, self).__init__()
self.node_num = node_num
self.hidden_dim = dim_out
self.gate = AVWGCN(dim_in+self.hidden_d... | 1,065 | 40 | 80 | py |
Traffic-Benchmark | Traffic-Benchmark-master/methods/AGCRN/model/Run_METR-LA.py | import os
import sys
file_dir = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
print(file_dir)
sys.path.append(file_dir)
import torch
import numpy as np
import torch.nn as nn
import argparse
import configparser
from datetime import datetime
from model.AGCRN import AGCRN as Network
from model.BasicTraine... | 6,953 | 32.757282 | 79 | py |
Traffic-Benchmark | Traffic-Benchmark-master/methods/AGCRN/model/BasicTrainer.py | import torch
import math
import os
import time
import copy
import numpy as np
from lib.logger import get_logger
from lib.metrics import All_Metrics
class Trainer(object):
def __init__(self, model, loss, optimizer, train_loader, val_loader, test_loader,
scaler, args, lr_scheduler=None):
sup... | 9,286 | 42.600939 | 148 | py |
Traffic-Benchmark | Traffic-Benchmark-master/methods/AGCRN/model/Run_BJ.py | import os
import sys
file_dir = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
print(file_dir)
sys.path.append(file_dir)
import torch
import numpy as np
import torch.nn as nn
import argparse
import configparser
from datetime import datetime
from model.AGCRN import AGCRN as Network
from model.BasicTrainer... | 6,947 | 32.892683 | 79 | py |
Traffic-Benchmark | Traffic-Benchmark-master/methods/AGCRN/lib/TrainInits.py | import torch
import random
import numpy as np
def init_seed(seed):
'''
Disable cudnn to maximize reproducibility
'''
torch.cuda.cudnn_enabled = False
torch.backends.cudnn.deterministic = True
random.seed(seed)
np.random.seed(seed)
torch.manual_seed(seed)
torch.cuda.manual_seed(seed)... | 1,818 | 33.980769 | 120 | py |
Traffic-Benchmark | Traffic-Benchmark-master/methods/AGCRN/lib/dataloader.py | import torch
import numpy as np
import torch.utils.data
from lib.add_window import Add_Window_Horizon
from lib.load_dataset import load_st_dataset
from lib.normalization import NScaler, MinMax01Scaler, MinMax11Scaler, StandardScaler, ColumnMinMaxScaler
def normalize_dataset(data, normalizer, column_wise=False):
if... | 9,437 | 45.492611 | 208 | py |
Traffic-Benchmark | Traffic-Benchmark-master/methods/AGCRN/lib/metrics.py | '''
Always evaluate the model with MAE, RMSE, MAPE, RRSE, PNBI, and oPNBI.
Why add mask to MAE and RMSE?
Filter the 0 that may be caused by error (such as loop sensor)
Why add mask to MAPE and MARE?
Ignore very small values (e.g., 0.5/0.5=100%)
'''
import numpy as np
import torch
def MAE_torch(pred, true, mask... | 7,947 | 34.641256 | 103 | py |
Traffic-Benchmark | Traffic-Benchmark-master/methods/AGCRN/lib/normalization.py | import numpy as np
import torch
class NScaler(object):
def transform(self, data):
return data
def inverse_transform(self, data):
return data
class StandardScaler:
"""
Standard the input
"""
def __init__(self, mean, std):
self.mean = mean
self.std = std
de... | 4,047 | 30.138462 | 93 | py |
Traffic-Benchmark | Traffic-Benchmark-master/methods/DGCRN_BJ/layer.py | from __future__ import division
import torch
import torch.nn as nn
from torch.nn import init
import numbers
import torch.nn.functional as F
from collections import OrderedDict
class gconv_RNN(nn.Module):
def __init__(self):
super(gconv_RNN, self).__init__()
def forward(self, x, A):
x = torch... | 2,008 | 26.902778 | 79 | py |
Traffic-Benchmark | Traffic-Benchmark-master/methods/DGCRN_BJ/net.py | import torch.utils.data as utils
import torch.nn.functional as F
import torch
import torch.nn as nn
from torch.autograd import Variable
from torch.nn.parameter import Parameter
import numpy as np
import pandas as pd
import math
import time
from layer import *
import random
import sys
from collections import OrderedDict... | 9,248 | 33.901887 | 79 | py |
Traffic-Benchmark | Traffic-Benchmark-master/methods/DGCRN_BJ/util.py | import pickle
import numpy as np
import os
import scipy.sparse as sp
import torch
from scipy.sparse import linalg
from torch.autograd import Variable
def normal_std(x):
return x.std() * np.sqrt((len(x) - 1.) / (len(x)))
class DataLoaderS(object):
def __init__(self,
file_name,
... | 12,198 | 31.530667 | 112 | py |
Traffic-Benchmark | Traffic-Benchmark-master/methods/DGCRN_BJ/train.py | import torch
import numpy as np
import argparse
import time
from util import *
from trainer import Trainer
from net import DGCRN
import setproctitle
import os
import random
setproctitle.setproctitle("DGCRN@lifuxian")
def str_to_bool(value):
if isinstance(value, bool):
return value
if value.lower() i... | 15,842 | 34.76298 | 186 | py |
Traffic-Benchmark | Traffic-Benchmark-master/methods/DGCRN_BJ/trainer.py | import torch.optim as optim
import math
from net import *
import util
class Trainer():
def __init__(self,
model,
lrate,
wdecay,
clip,
step_size,
seq_out_len,
scaler,
device,
... | 3,313 | 33.520833 | 78 | py |
Traffic-Benchmark | Traffic-Benchmark-master/methods/ASTGCN/train_MSTGCN_r.py | #!/usr/bin/env python
# coding: utf-8
import torch
import torch.nn as nn
import torch.optim as optim
import numpy as np
import os
from time import time
import shutil
import argparse
import configparser
from model.MSTGCN_r import make_model
from lib.utils import load_graphdata_channel1, get_adjacency_matrix, evaluate_on... | 6,970 | 31.423256 | 150 | py |
Traffic-Benchmark | Traffic-Benchmark-master/methods/ASTGCN/train_ASTGCN_r.py | #!/usr/bin/env python
# coding: utf-8
import torch
import torch.nn as nn
import torch.optim as optim
import numpy as np
import os
from time import time
import shutil
import argparse
import configparser
from model.ASTGCN_r import make_model
from lib.utils import load_graphdata_channel1, get_adjacency_matrix, compute_val... | 6,972 | 30.840183 | 150 | py |
Traffic-Benchmark | Traffic-Benchmark-master/methods/ASTGCN/model/ASTGCN_r.py | # -*- coding:utf-8 -*-
import torch
import torch.nn as nn
import torch.nn.functional as F
from lib.utils import scaled_Laplacian, cheb_polynomial
class Spatial_Attention_layer(nn.Module):
'''
compute spatial attention scores
'''
def __init__(self, DEVICE, in_channels, num_of_vertices, num_of_timesteps... | 10,548 | 36.275618 | 194 | py |
Traffic-Benchmark | Traffic-Benchmark-master/methods/ASTGCN/model/MSTGCN_r.py | # -*- coding:utf-8 -*-
import torch
import torch.nn as nn
import torch.nn.functional as F
from lib.utils import scaled_Laplacian, cheb_polynomial
class cheb_conv(nn.Module):
'''
K-order chebyshev graph convolution
'''
def __init__(self, K, cheb_polynomials, in_channels, out_channels):
'''
... | 5,014 | 32.885135 | 154 | py |
Traffic-Benchmark | Traffic-Benchmark-master/methods/ASTGCN/lib/utils.py | import os
import numpy as np
import torch
import torch.utils.data
from sklearn.metrics import mean_absolute_error
from sklearn.metrics import mean_squared_error
from .metrics import masked_mape_np
from scipy.sparse.linalg import eigs
import pickle
def load_pickle(pickle_file):
try:
with open(pickle_file,... | 18,213 | 34.996047 | 208 | py |
Traffic-Benchmark | Traffic-Benchmark-master/methods/STSGCN/main.py | # -*- coding:utf-8 -*-
import setproctitle
setproctitle.setproctitle("STSGCN@lifuxian")
import time
import json
import argparse
import numpy as np
import mxnet as mx
from utils import (construct_model, generate_data,
masked_mae_np, masked_mape_np, masked_mse_np)
parser = argparse.ArgumentParser()
... | 6,547 | 32.238579 | 830 | py |
Traffic-Benchmark | Traffic-Benchmark-master/methods/STSGCN/utils.py |
import os
import numpy as np
import mxnet as mx
import pickle
def load_pickle(pickle_file):
try:
with open(pickle_file, 'rb') as f:
pickle_data = pickle.load(f)
except UnicodeDecodeError as e:
with open(pickle_file, 'rb') as f:
pickle_data = pickle.load(f, encoding='l... | 7,732 | 27.747212 | 78 | py |
Traffic-Benchmark | Traffic-Benchmark-master/methods/STSGCN/load_params.py | # -*- coding:utf-8 -*-
import mxnet as mx
sym, arg_params, aux_params = mx.model.load_checkpoint('STSGCN', 200)
print(type(arg_params), type(aux_params))
| 157 | 18.75 | 69 | py |
Traffic-Benchmark | Traffic-Benchmark-master/methods/STSGCN/models/stsgcn.py | # -*- coding:utf-8 -*-
import mxnet as mx
def position_embedding(data,
input_length, num_of_vertices, embedding_size,
temporal=True, spatial=True,
init=mx.init.Xavier(magnitude=0.0003), prefix=""):
'''
Parameters
----------
data: mx... | 12,140 | 23.137177 | 76 | py |
Traffic-Benchmark | Traffic-Benchmark-master/methods/STSGCN/test/test_stsgcn.py | # -*- coding:utf-8 -*-
import sys
import mxnet as mx
sys.path.append('.')
num_of_vertices = 358
batch_size = 16
filter_ = [3, 3, 3]
filter_list = [[3, 3, 3], [6, 6, 6], [9, 9, 9]]
predict_length = 12
data = mx.sym.var('data')
adj = mx.sym.var('adj')
label = mx.sym.var('label')
def test_position_embedding():
fro... | 4,947 | 34.342857 | 75 | py |
FL-MRCM | FL-MRCM-main/main_fl_mr.py | #!/usr/bin/env python
# -*- coding: utf-8 -*-
# Python version: 3.6
import copy
import numpy as np
import torch
import os
from utils.options import args_parser
from models.recon_Update import LocalUpdate
from models.Fed import FedAvg
from models.test import evaluator_normal as evaluator
from data.mri_data import Slice... | 5,441 | 37.595745 | 148 | py |
FL-MRCM | FL-MRCM-main/main_test.py | #!/usr/bin/env python
# -*- coding: utf-8 -*-
# Python version: 3.6
import torch
import os
from utils.options import args_parser
from models.test import test_save_result, test_save_vector
from data.mri_data import SliceData, DataTransform
from data.subsample import create_mask_for_mask_type
from models.unet_model impo... | 2,799 | 32.73494 | 127 | py |
FL-MRCM | FL-MRCM-main/main_fl_mrcm.py | #!/usr/bin/env python
# -*- coding: utf-8 -*-
# Python version: 3.6
# import matplotlib
# matplotlib.use('Agg')
# import matplotlib.pyplot as plt
import copy
import numpy as np
import torch
import os
from utils.options import args_parser
from models.recon_Update import LocalUpdate_ad_da
from models.Fed import FedAvg
f... | 8,221 | 43.443243 | 167 | py |
FL-MRCM | FL-MRCM-main/models/test.py | #!/usr/bin/env python
# -*- coding: utf-8 -*-
# @python: 3.6
import torch
import torch.nn.functional as F
from torch.utils.data import DataLoader
from collections import defaultdict
import numpy as np
from utils import evaluate
import h5py
from tqdm import tqdm
def test_save_result(net_g, datatest, args):
net_g.... | 9,725 | 44.877358 | 91 | py |
FL-MRCM | FL-MRCM-main/models/unet_model.py | """
Copyright (c) Facebook, Inc. and its affiliates.
This source code is licensed under the MIT license found in the
LICENSE file in the root directory of this source tree.
"""
import torch
from torch import nn
from torch.nn import functional as F
class ConvBlock(nn.Module):
"""
A Convolutional Block that co... | 10,036 | 35.234657 | 98 | py |
FL-MRCM | FL-MRCM-main/models/Fed.py | #!/usr/bin/env python
# -*- coding: utf-8 -*-
# Python version: 3.6
import copy
import torch
from torch import nn
def FedAvg(w):
w_avg = copy.deepcopy(w[0])
for k in w_avg.keys():
for i in range(1, len(w)):
w_avg[k] += w[i][k]
w_avg[k] = torch.div(w_avg[k], len(w))
return w_av... | 322 | 18 | 46 | py |
FL-MRCM | FL-MRCM-main/models/recon_Update.py | #!/usr/bin/env python
# -*- coding: utf-8 -*-
# Python version: 3.6
import torch
from torch import nn
from torch.utils.data import DataLoader, Dataset
import time
import numpy as np
from torch.autograd import Variable
from torch.nn import functional as F
class LocalUpdate(object):
def __init__(self, args, device... | 8,565 | 46.588889 | 131 | py |
FL-MRCM | FL-MRCM-main/utils/preprocess_datasets_brats.py |
import os
import h5py
import pathlib
from data import transforms
import numpy as np
import torch
import nibabel as nib
from tqdm import tqdm
def mkdir(folder):
if not os.path.exists(folder):
os.makedirs(folder)
def main():
root_dir ='path to /MICCAI_BraTS2020_ValidationData'
root_out_dir = 'path... | 2,678 | 35.202703 | 97 | py |
FL-MRCM | FL-MRCM-main/utils/sampling.py | #!/usr/bin/env python
# -*- coding: utf-8 -*-
# Python version: 3.6
import numpy as np
from torchvision import datasets, transforms
from collections import OrderedDict
def mnist_iid(dataset, num_users):
"""
Sample I.I.D. client data from MNIST dataset
:param dataset:
:param num_users:
:return: dict... | 3,579 | 31.844037 | 106 | py |
FL-MRCM | FL-MRCM-main/data/volume_sampler.py | import torch
import numpy as np
from torch.utils.data import Sampler
import torch.distributed as dist
class VolumeSampler(Sampler):
"""
Based on pytorch DistributedSampler, the difference is that all instances from the same
volume need to go to the same node. Dataset example is a list of tuples (f... | 1,738 | 30.618182 | 105 | py |
FL-MRCM | FL-MRCM-main/data/mri_data.py | """
Copyright (c) Facebook, Inc. and its affiliates.
This source code is licensed under the MIT license found in the
LICENSE file in the root directory of this source tree.
"""
import pathlib
import random
import numpy as np
import h5py
from torch.utils.data import Dataset
from data import transforms
import torch
... | 6,487 | 40.063291 | 116 | py |
FL-MRCM | FL-MRCM-main/data/subsample.py | """
Copyright (c) Facebook, Inc. and its affiliates.
This source code is licensed under the MIT license found in the
LICENSE file in the root directory of this source tree.
"""
import numpy as np
import torch
def create_mask_for_mask_type(mask_type_str, center_fractions, accelerations):
if mask_type_str == 'ran... | 7,422 | 42.409357 | 112 | py |
FL-MRCM | FL-MRCM-main/data/transforms.py | """
Copyright (c) Facebook, Inc. and its affiliates.
This source code is licensed under the MIT license found in the
LICENSE file in the root directory of this source tree.
"""
import numpy as np
import torch
def to_tensor(data):
"""
Convert numpy array to PyTorch tensor. For complex arrays, the real and im... | 11,406 | 28.705729 | 115 | py |
HabitatDyn | HabitatDyn-main/metric_cal.py | import argparse
import logging
import os
import numpy as np
import torch
import torch.utils.data.dataloader as dataloader
from PIL import Image
from torch.utils.data import Dataset
from torch.utils.data.dataset import Dataset
from tqdm import tqdm
from utils.common import safe_mkdir
from utils.meter import AverageVal... | 9,689 | 36.55814 | 101 | py |
HabitatDyn | HabitatDyn-main/dist_cal.py | import json
import logging
import os
import cv2
import argparse
import matplotlib.pyplot as plt
import numpy as np
import scipy.spatial.distance as sci_dis
import torch
import yaml
from sklearn.cluster import DBSCAN
from sklearn.neighbors import LocalOutlierFactor
from tqdm import tqdm
import utils.distance_estimatio... | 22,013 | 47.170678 | 183 | py |
HabitatDyn | HabitatDyn-main/utils/common.py | import pathlib
import numpy as np
import math
import numbers
import torch
from torch import nn
from torch.nn import functional as F
def safe_mkdir(path):
try:
pathlib.Path(path).mkdir(parents=True, exist_ok=True)
except:
pass
def intersect2d(A,B):
'''
calculate the intersection of two... | 3,632 | 32.330275 | 86 | py |
HabitatDyn | HabitatDyn-main/utils/metrics.py | import torch
import torch.nn.functional as F
import torch.nn as nn
def iou(pred_mask, gt_mask):
"""Calculates the IoU of two masks.
Args:
pred_mask: A torch.Tensor of shape (batch_size, height, width).
gt_mask: A torch.Tensor of shape (batch_size, height, width).
Returns:
A torch.Tensor of shape (... | 1,792 | 27.015625 | 87 | py |
HabitatDyn | HabitatDyn-main/utils/meter.py | import numpy as np
class Meter(object):
"""Meters provide a way to keep track of important statistics in an online manner.
This class is abstract, but provides a standard interface for all meters to follow.
"""
def reset(self):
"""Reset the meter to default settings."""
pass
def ... | 1,655 | 26.147541 | 87 | py |
FastJTNNpy3 | FastJTNNpy3-master/fast_bo/gen_latent.py | import sys
sys.path.append('../')
import torch
import torch.nn as nn
from optparse import OptionParser
from tqdm import tqdm
import rdkit
from rdkit.Chem import Descriptors
from rdkit.Chem import MolFromSmiles, MolToSmiles
from rdkit.Chem import rdmolops
import numpy as np
from fast_jtnn import *
from fast_jtnn impor... | 4,431 | 32.074627 | 78 | py |
FastJTNNpy3 | FastJTNNpy3-master/fast_bo/run_bo.py | import sys
sys.path.append('../')
import pickle
import gzip
import scipy.stats as sps
import numpy as np
import os
import rdkit
from rdkit.Chem import MolFromSmiles, MolToSmiles
from rdkit.Chem import Descriptors
import torch
import torch.nn as nn
from fast_jtnn import create_var, JTNNVAE, Vocab, sascorer
from fast_jtn... | 8,400 | 32.738956 | 79 | py |
FastJTNNpy3 | FastJTNNpy3-master/fast_jtnn/jtnn_enc.py | import torch
import torch.nn as nn
import torch.nn.functional as F
from collections import deque
from .mol_tree import Vocab, MolTree
from .nnutils import create_var, index_select_ND
class JTNNEncoder(nn.Module):
def __init__(self, hidden_size, depth, embedding):
super(JTNNEncoder, self).__init__()
... | 4,473 | 32.893939 | 80 | py |
FastJTNNpy3 | FastJTNNpy3-master/fast_jtnn/datautils.py | import torch
from torch.utils.data import Dataset, DataLoader
from .mol_tree import MolTree
import numpy as np
from .jtnn_enc import JTNNEncoder
from .mpn import MPN
from .jtmpn import JTMPN
import pickle as pickle
import os, random
class PairTreeFolder(object):
def __init__(self, data_folder, vocab, batch_size, ... | 4,697 | 32.798561 | 131 | py |
FastJTNNpy3 | FastJTNNpy3-master/fast_jtnn/nnutils.py | import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.autograd import Variable
def create_var(tensor, requires_grad=None):
if requires_grad is None:
return Variable(tensor).cuda()
else:
return Variable(tensor, requires_grad=requires_grad).cuda()
def index_select_ND(sour... | 2,042 | 29.492537 | 67 | py |
FastJTNNpy3 | FastJTNNpy3-master/fast_jtnn/mpn.py | import torch
import torch.nn as nn
import rdkit.Chem as Chem
import torch.nn.functional as F
from .nnutils import *
from .chemutils import get_mol
ELEM_LIST = ['C', 'N', 'O', 'S', 'F', 'Si', 'P', 'Cl', 'Br', 'Mg', 'Na', 'Ca', 'Fe', 'Al', 'I', 'B', 'K', 'Se', 'Zn', 'H', 'Cu', 'Mn', 'unknown']
ATOM_FDIM = len(ELEM_LIST... | 4,469 | 34.47619 | 171 | py |
FastJTNNpy3 | FastJTNNpy3-master/fast_jtnn/jtnn_vae.py | import torch
import torch.nn as nn
import torch.nn.functional as F
from .mol_tree import Vocab, MolTree
from .nnutils import create_var, flatten_tensor, avg_pool
from .jtnn_enc import JTNNEncoder
from .jtnn_dec import JTNNDecoder
from .mpn import MPN
from .jtmpn import JTMPN
from .datautils import tensorize
from .chem... | 10,015 | 43.318584 | 172 | py |
FastJTNNpy3 | FastJTNNpy3-master/fast_jtnn/jtprop_vae.py | import torch
import torch.nn as nn
from .mol_tree import Vocab, MolTree
from .nnutils import create_var
from .jtnn_enc import JTNNEncoder
from .jtnn_dec import JTNNDecoder
from .mpn import MPN, mol2graph
from .jtmpn import JTMPN
from .chemutils import enum_assemble, set_atommap, copy_edit_mol, attach_mols, atom_equal,... | 14,781 | 40.757062 | 144 | py |
FastJTNNpy3 | FastJTNNpy3-master/fast_jtnn/jtmpn.py | import torch
import torch.nn as nn
import torch.nn.functional as F
from .nnutils import create_var, index_select_ND
from .chemutils import get_mol
import rdkit.Chem as Chem
ELEM_LIST = ['C', 'N', 'O', 'S', 'F', 'Si', 'P', 'Cl', 'Br', 'Mg', 'Na', 'Ca', 'Fe', 'Al', 'I', 'B', 'K', 'Se', 'Zn', 'H', 'Cu', 'Mn', 'unknown']
... | 5,387 | 37.76259 | 184 | py |
FastJTNNpy3 | FastJTNNpy3-master/fast_jtnn/jtnn_dec.py | import torch
import torch.nn as nn
import torch.nn.functional as F
from .mol_tree import Vocab, MolTree, MolTreeNode
from .nnutils import create_var, GRU
from .chemutils import enum_assemble, set_atommap
import copy
MAX_NB = 15
MAX_DECODE_LEN = 100
class JTNNDecoder(nn.Module):
def __init__(self, vocab, hidden_s... | 13,820 | 38.945087 | 440 | py |
FastJTNNpy3 | FastJTNNpy3-master/fast_molopt/pretrain.py | import torch
import torch.nn as nn
import torch.optim as optim
import torch.optim.lr_scheduler as lr_scheduler
from torch.utils.data import DataLoader
from torch.autograd import Variable
import math, random, sys
from optparse import OptionParser
from collections import deque
from jtnn import *
import rdkit
lg = rdki... | 3,110 | 32.095745 | 149 | py |
FastJTNNpy3 | FastJTNNpy3-master/fast_molopt/vaetrain.py | import torch
import torch.nn as nn
import torch.optim as optim
import torch.optim.lr_scheduler as lr_scheduler
from torch.utils.data import DataLoader
from torch.autograd import Variable
import math, random, sys
from optparse import OptionParser
from collections import deque
from jtnn import *
import rdkit
lg = rdki... | 3,695 | 33.542056 | 149 | py |
FastJTNNpy3 | FastJTNNpy3-master/fast_molopt/optimize.py | import torch
import torch.nn as nn
from torch.autograd import Variable
import math, random, sys
from optparse import OptionParser
from collections import deque
import rdkit
import rdkit.Chem as Chem
from rdkit.Chem import Descriptors
import sascorer
from jtnn import *
lg = rdkit.RDLogger.logger()
lg.setLevel(rdkit... | 1,845 | 29.766667 | 85 | py |
FastJTNNpy3 | FastJTNNpy3-master/Old/molvae/pretrain.py | import torch
import torch.nn as nn
import torch.optim as optim
import torch.optim.lr_scheduler as lr_scheduler
from torch.utils.data import DataLoader
from torch.autograd import Variable
import math, random, sys
from optparse import OptionParser
from collections import deque
from jtnn import Vocab, JTNNVAE, MoleculeD... | 2,973 | 31.326087 | 128 | py |
FastJTNNpy3 | FastJTNNpy3-master/Old/molvae/sample.py | import torch
import torch.nn as nn
import torch.optim as optim
import torch.optim.lr_scheduler as lr_scheduler
from torch.autograd import Variable
import math, random, sys
from optparse import OptionParser
from collections import deque
import rdkit
import rdkit.Chem as Chem
from rdkit.Chem import Draw
from jtnn impor... | 1,368 | 28.76087 | 77 | py |
FastJTNNpy3 | FastJTNNpy3-master/Old/molvae/vaetrain.py | import torch
import torch.nn as nn
import torch.optim as optim
import torch.optim.lr_scheduler as lr_scheduler
from torch.utils.data import DataLoader
from torch.autograd import Variable
import math, random, sys
from optparse import OptionParser
from collections import deque
from jtnn import *
import rdkit
lg = rdki... | 3,544 | 32.443396 | 127 | py |
FastJTNNpy3 | FastJTNNpy3-master/Old/molvae/reconstruct.py | import torch
import torch.nn as nn
from torch.autograd import Variable
import math, random, sys
from optparse import OptionParser
from collections import deque
import rdkit
import rdkit.Chem as Chem
from jtnn import *
lg = rdkit.RDLogger.logger()
lg.setLevel(rdkit.RDLogger.CRITICAL)
parser = OptionParser()
parser... | 1,586 | 24.190476 | 68 | py |
FastJTNNpy3 | FastJTNNpy3-master/Old/molvae/draw_nei.py | import torch
import torch.nn as nn
from torch.autograd import Variable
import math, random, sys
from optparse import OptionParser
import rdkit
import rdkit.Chem as Chem
from rdkit.Chem import Draw
import numpy as np
from jtnn import *
lg = rdkit.RDLogger.logger()
lg.setLevel(rdkit.RDLogger.CRITICAL)
parser = Opti... | 1,797 | 27.539683 | 86 | py |
FastJTNNpy3 | FastJTNNpy3-master/Old/molvae/jtnn/jtnn_enc.py | import torch
import torch.nn as nn
from collections import deque
from mol_tree import Vocab, MolTree
from nnutils import create_var, GRU
MAX_NB = 8
class JTNNEncoder(nn.Module):
def __init__(self, vocab, hidden_size, embedding=None):
super(JTNNEncoder, self).__init__()
self.hidden_size = hidden_s... | 3,664 | 30.324786 | 84 | py |
FastJTNNpy3 | FastJTNNpy3-master/Old/molvae/jtnn/datautils.py | from torch.utils.data import Dataset
from mol_tree import MolTree
import numpy as np
class MoleculeDataset(Dataset):
def __init__(self, data_file):
with open(data_file) as f:
self.data = [line.strip("\r\n ").split()[0] for line in f]
def __len__(self):
return len(self.data)
... | 985 | 24.947368 | 70 | py |
FastJTNNpy3 | FastJTNNpy3-master/Old/molvae/jtnn/nnutils.py | import torch
import torch.nn as nn
from torch.autograd import Variable
def create_var(tensor, requires_grad=None):
if requires_grad is None:
return Variable(tensor)
else:
return Variable(tensor, requires_grad=requires_grad)
def index_select_ND(source, dim, index):
index_size = index.size()... | 968 | 25.916667 | 60 | py |
FastJTNNpy3 | FastJTNNpy3-master/Old/molvae/jtnn/mpn.py | import torch
import torch.nn as nn
import rdkit.Chem as Chem
import torch.nn.functional as F
from nnutils import *
from chemutils import get_mol
ELEM_LIST = ['C', 'N', 'O', 'S', 'F', 'Si', 'P', 'Cl', 'Br', 'Mg', 'Na', 'Ca', 'Fe', 'Al', 'I', 'B', 'K', 'Se', 'Zn', 'H', 'Cu', 'Mn', 'unknown']
ATOM_FDIM = len(ELEM_LIST) ... | 4,279 | 33.24 | 171 | py |
FastJTNNpy3 | FastJTNNpy3-master/Old/molvae/jtnn/jtnn_vae.py | import torch
import torch.nn as nn
from mol_tree import Vocab, MolTree
from nnutils import create_var
from jtnn_enc import JTNNEncoder
from jtnn_dec import JTNNDecoder
from mpn import MPN, mol2graph
from jtmpn import JTMPN
from chemutils import enum_assemble, set_atommap, copy_edit_mol, attach_mols, atom_equal, decode... | 13,071 | 40.897436 | 144 | py |
FastJTNNpy3 | FastJTNNpy3-master/Old/molvae/jtnn/jtprop_vae.py | import torch
import torch.nn as nn
from mol_tree import Vocab, MolTree
from nnutils import create_var
from jtnn_enc import JTNNEncoder
from jtnn_dec import JTNNDecoder
from mpn import MPN, mol2graph
from jtmpn import JTMPN
from chemutils import enum_assemble, set_atommap, copy_edit_mol, attach_mols, atom_equal, decode... | 14,765 | 40.711864 | 144 | py |
FastJTNNpy3 | FastJTNNpy3-master/Old/molvae/jtnn/jtmpn.py | import torch
import torch.nn as nn
from nnutils import create_var, index_select_ND
from chemutils import get_mol
#from mpn import atom_features, bond_features, ATOM_FDIM, BOND_FDIM
import rdkit.Chem as Chem
ELEM_LIST = ['C', 'N', 'O', 'S', 'F', 'Si', 'P', 'Cl', 'Br', 'Mg', 'Na', 'Ca', 'Fe', 'Al', 'I', 'B', 'K', 'Se', ... | 5,326 | 37.323741 | 184 | py |
FastJTNNpy3 | FastJTNNpy3-master/Old/molvae/jtnn/jtnn_dec.py | import torch
import torch.nn as nn
from mol_tree import Vocab, MolTree, MolTreeNode
from nnutils import create_var, GRU
from chemutils import enum_assemble
import copy
MAX_NB = 8
MAX_DECODE_LEN = 100
class JTNNDecoder(nn.Module):
def __init__(self, vocab, hidden_size, latent_size, embedding=None):
super(... | 12,422 | 37.580745 | 118 | py |
FastJTNNpy3 | FastJTNNpy3-master/Old/molopt/pretrain.py | import torch
import torch.nn as nn
import torch.optim as optim
import torch.optim.lr_scheduler as lr_scheduler
from torch.utils.data import DataLoader
from torch.autograd import Variable
import math, random, sys
from optparse import OptionParser
from collections import deque
from jtnn import *
import rdkit
lg = rdki... | 3,110 | 32.095745 | 149 | py |
FastJTNNpy3 | FastJTNNpy3-master/Old/molopt/vaetrain.py | import torch
import torch.nn as nn
import torch.optim as optim
import torch.optim.lr_scheduler as lr_scheduler
from torch.utils.data import DataLoader
from torch.autograd import Variable
import math, random, sys
from optparse import OptionParser
from collections import deque
from jtnn import *
import rdkit
lg = rdki... | 3,695 | 33.542056 | 149 | py |
FastJTNNpy3 | FastJTNNpy3-master/Old/molopt/optimize.py | import torch
import torch.nn as nn
from torch.autograd import Variable
import math, random, sys
from optparse import OptionParser
from collections import deque
import rdkit
import rdkit.Chem as Chem
from rdkit.Chem import Descriptors
import sascorer
from jtnn import *
lg = rdkit.RDLogger.logger()
lg.setLevel(rdkit... | 1,845 | 29.766667 | 85 | py |
FastJTNNpy3 | FastJTNNpy3-master/Old/bo/gen_latent.py | import torch
import torch.nn as nn
from torch.autograd import Variable
from optparse import OptionParser
import rdkit
from rdkit.Chem import Descriptors
from rdkit.Chem import MolFromSmiles, MolToSmiles
from rdkit.Chem import rdmolops
import sascorer
import numpy as np
from jtnn import *
lg = rdkit.RDLogger.logger... | 2,922 | 31.120879 | 104 | py |
FastJTNNpy3 | FastJTNNpy3-master/Old/bo/run_bo.py | import pickle
import gzip
from sparse_gp import SparseGP
import scipy.stats as sps
import numpy as np
import os.path
import rdkit
from rdkit.Chem import MolFromSmiles, MolToSmiles
from rdkit.Chem import Descriptors
import torch
import torch.nn as nn
from jtnn import create_var, JTNNVAE, Vocab
from optparse import Op... | 5,700 | 35.082278 | 151 | py |
FastJTNNpy3 | FastJTNNpy3-master/fast_molvae/sample.py | import sys
sys.path.append('../')
import torch
import torch.nn as nn
import math, random, sys
import argparse
from fast_jtnn import *
import rdkit
def load_model(vocab, model_path, hidden_size=450, latent_size=56, depthT=20, depthG=3):
vocab = [x.strip("\r\n ") for x in open(vocab)]
vocab = Vocab(vocab)
... | 1,836 | 33.660377 | 133 | py |
FastJTNNpy3 | FastJTNNpy3-master/fast_molvae/preprocess.py | import sys
sys.path.append('../')
import torch
import torch.nn as nn
from multiprocessing import Pool
import numpy as np
import os
from tqdm import tqdm
import math, random, sys
from optparse import OptionParser
import pickle
from fast_jtnn import *
import rdkit
def tensorize(smiles, assm=True):
mol_tree = MolTr... | 2,146 | 27.25 | 85 | py |
FastJTNNpy3 | FastJTNNpy3-master/fast_molvae/vae_train.py | import sys
sys.path.append('../')
import torch
import torch.nn as nn
import torch.optim as optim
import torch.optim.lr_scheduler as lr_scheduler
from torch.utils.data import DataLoader
from torch.autograd import Variable
import math, random, sys
import numpy as np
import argparse
from collections import deque
import p... | 5,667 | 33.351515 | 214 | py |
ctcdecode | ctcdecode-master/setup.py | #!/usr/bin/env python
import glob
import multiprocessing.pool
import os
import tarfile
import urllib.request
import warnings
from setuptools import distutils, find_packages, setup
from torch.utils.cpp_extension import BuildExtension, CppExtension, include_paths
def download_extract(url, dl_path):
if not os.path.... | 4,428 | 29.756944 | 117 | py |
ctcdecode | ctcdecode-master/tests/test_decode.py | """Test decoders."""
from __future__ import absolute_import, division, print_function
import os
import unittest
import ctcdecode
import torch
class TestDecoders(unittest.TestCase):
def setUp(self):
self.vocab_list = ["'", " ", "a", "b", "c", "d", "_"]
self.beam_size = 20
self.probs_seq1 ... | 9,962 | 45.125 | 113 | py |
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