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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dhypr | dhypr-main/code/D-HYPR/layers/hyp_layers.py | import math
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.nn.init as init
from torch.nn.modules.module import Module
from torch.nn.parameter import Parameter
import pdb
from layers.att_layers import DenseAtt, SpAttn
def get_dim_act_curv(args):
if not args.act:
act = lambd... | 5,990 | 33.431034 | 97 | py |
dhypr | dhypr-main/code/D-HYPR/layers/att_layers.py | import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
import pdb
class DenseAtt(nn.Module):
def __init__(self, in_features, dropout, act):
super(DenseAtt, self).__init__()
self.linear = nn.Linear(2 * in_features, 1, bias=True)
self.act = act
self.in_... | 3,977 | 31.606557 | 78 | py |
dhypr | dhypr-main/code/D-HYPR/layers/layers.py | import math
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.nn.modules.module import Module
from torch.nn.parameter import Parameter
import pdb
from layers.hyp_layers import HypLinear, HypAct
class Linear(Module):
def __init__(self, in_features, out_features, dropout, act, use_bias)... | 1,883 | 27.119403 | 90 | py |
dhypr | dhypr-main/code/D-HYPR/optimizers/__init__.py | from torch.optim import Adam | 28 | 28 | 28 | py |
dhypr | dhypr-main/code/D-HYPR/utils/data_utils.py | import os
import pickle as pkl
import sys
import networkx as nx
import numpy as np
import scipy.sparse as sp
import torch
import pdb
import pickle
import random
def load_graph(filepath):
G = nx.read_edgelist(os.path.join(filepath, 'train_edges.txt'),
delimiter='\t', create_using=nx.DiGra... | 17,347 | 38.788991 | 132 | py |
dhypr | dhypr-main/code/D-HYPR/utils/train_utils.py | import os
import numpy as np
import torch
import torch.nn.functional as F
import torch.nn.modules.loss
import pdb
import pickle
def format_metrics(metrics, split):
return " ".join(
["{}_{}: {:.4f}".format(
split, metric_name,
metric_val) for metric_name, metric_val in ... | 3,475 | 31.185185 | 107 | py |
dhypr | dhypr-main/code/D-HYPR/utils/math_utils.py | import torch
def cosh(x, clamp=15):
return x.clamp(-clamp, clamp).cosh()
def sinh(x, clamp=15):
return x.clamp(-clamp, clamp).sinh()
def tanh(x, clamp=15):
return x.clamp(-clamp, clamp).tanh()
def arcosh(x):
return Arcosh.apply(x)
def arsinh(x):
return Arsinh.apply(x)
def artanh(x):
... | 1,515 | 21.294118 | 83 | py |
dhypr | dhypr-main/code/D-HYPR/manifolds/base.py | from torch.nn import Parameter
class Manifold(object):
def __init__(self):
super().__init__()
self.eps = 10e-8
def sqdist(self, p1, p2, c):
raise NotImplementedError
def egrad2rgrad(self, p, dp, c):
raise NotImplementedError
def proj(self, p, c):
raise NotImp... | 1,598 | 23.227273 | 67 | py |
dhypr | dhypr-main/code/D-HYPR/manifolds/poincare.py | import torch
from manifolds.base import Manifold
from utils.math_utils import artanh, tanh
class PoincareBall(Manifold):
def __init__(self, ):
super(PoincareBall, self).__init__()
self.name = 'PoincareBall'
self.min_norm = 1e-15
self.eps = {torch.float32: 4e-3, torch.float64: 1e-5... | 5,026 | 34.907143 | 90 | py |
denn | denn-master/denn/experiments.py | import torch
import torch.nn as nn
import argparse
import numpy as np
from denn.algos import train_L2, train_L2_2D, train_GAN, train_GAN_2D
from denn.models import MLP
from denn.config.config import get_config
from denn.utils import handle_overwrite
import denn.problems as pb
def get_problem(pkey, params):
""" he... | 2,886 | 30.380435 | 95 | py |
denn | denn-master/denn/traditional.py | import argparse
import numpy as np
import torch
from denn.config.config import get_config
from denn.rk4 import rk4
from denn.fd import fd
from denn.problems import NonlinearOscillator, CoupledOscillator, SIRModel
def exp_deriv(t, x):
"""
dxdt = -x
"""
rhs = -x
return rhs
def solve_exp(params):
... | 3,539 | 22.137255 | 95 | py |
denn | denn-master/denn/utils.py | import os
import torch
from torch import autograd
import numpy as np
import itertools
import matplotlib.pyplot as plt
from IPython.display import clear_output
import pandas as pd
# global plot params
plt.rc('axes', titlesize=15, labelsize=15)
plt.rc('legend', fontsize=15)
plt.rc('xtick', labelsize=13)
plt.rc('ytick', ... | 12,070 | 37.078864 | 128 | py |
denn | denn-master/denn/problems.py | import numpy as np
import torch
from scipy.integrate import odeint, solve_ivp
from denn.utils import diff
from denn.rans.numerical import solve_rans_scipy_solve_bvp
import os
_THIS_DIR = os.path.dirname(os.path.abspath(__file__))
class Problem():
""" parent class for all problems
"""
def __init__(self, n ... | 24,745 | 32.127175 | 120 | py |
denn | denn-master/denn/models.py | import torch
import torch.nn as nn
class ResidualBlock(nn.Module):
""" Most basic residual block
https://arxiv.org/pdf/1512.03385.pdf : Equation #1
"""
def __init__(self, n_units, activation, spectral_norm=False):
super(ResidualBlock, self).__init__()
norm = lambda x: nn.utils.spe... | 2,440 | 28.059524 | 106 | py |
denn | denn-master/denn/algos.py | import numpy as np
import torch
import torch.nn as nn
import os
from denn.utils import LambdaLR, plot_results, calc_gradient_penalty, handle_overwrite
from denn.config.config import write_config
try:
from ray.tune import track
except:
print("Ray not loaded.")
this_dir = os.path.dirname(os.path.abspath(__file... | 24,001 | 36.328149 | 145 | py |
denn | denn-master/denn/rans/diff_sampling.py | # testing sampling methods
import channel_flow as chan
import utils
torch.random.manual_seed(123)
sampling = ['grid', 'perturb', 'boundary', 'uniform']
HYPERS={'num_epochs': 100000}
for s in sampling:
print('Training with sampling : {}'.format(s))
HYPERS['sampling'] = s
pdenn = chan.Chanflow(**HYPERS)
... | 410 | 28.357143 | 62 | py |
denn | denn-master/denn/rans/cv_kappa.py | ## run cross-validation on kappa (really just run training at various kappas)
import denn.channel_flow as chan
import utils
import torch
torch.random.manual_seed(123)
kappas = [0.38, 0.39, 0.40, 0.41, 0.42]
HYPERS={'num_epochs': 100000, 'sampling': 'perturb'}
for k in kappas:
print('Training with kappa={}'.format(... | 473 | 30.6 | 77 | py |
denn | denn-master/denn/rans/rans_utils.py | import denn.rans.channel_flow as chan
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
import torch
# global plot params
plt.rc('axes', titlesize=15)
plt.rc('axes', labelsize=12)
plt.rc('legend', fontsize=12)
def calc_renot(u_bar, delta, nu):
""" calculates Re_not where Re stands for Reynold... | 5,479 | 37.865248 | 151 | py |
denn | denn-master/denn/rans/channel_flow.py | import numpy as np
import torch
from torch.autograd import grad
import tqdm
import copy
import time
import os
import denn.rans.rans_utils as utils
class Chanflow(torch.nn.Module):
""" Basic neural network to approximate the solution of the stationary channel flow PDE """
def __init__(self, **kwargs):
... | 9,561 | 41.123348 | 135 | py |
denn | denn-master/denn/rans/train_chanflow.py | import denn.channel_flow as chan
import numpy as np
import torch
import os
import argparse
import time
import rans_utils
torch.random.manual_seed(123)
HYPERS={
'num_epochs': 10000,
'sampling': 'perturb',
'k': 0.41,
'activation': 'swish'
}
if __name__ == '__main__':
parser = ... | 1,346 | 33.538462 | 114 | py |
DADER | DADER-main/utils.py | import os
import random
import numpy as np
import pandas as pd
import torch
import torch.backends.cudnn as cudnn
from torch.utils.data import DataLoader, TensorDataset, RandomSampler
import param
class InputFeatures(object):
"""A single set of features of data."""
def __init__(self, input_ids=None, input_mas... | 8,707 | 40.466667 | 127 | py |
DADER | DADER-main/modules/matcher.py | import torch
import sys
sys.path.append("..")
import param
import torch.nn as nn
class BertClassifier(nn.Module):
"""This is the matcher when Feature Extractor is LMs (Bert etc.)"""
def __init__(self, dropout=0.1):
super(BertClassifier, self).__init__()
self.dropout = nn.Dropout(p=dropout)
... | 852 | 31.807692 | 72 | py |
DADER | DADER-main/modules/extractor.py | import torch
import sys
sys.path.append("..")
import param
import torch.nn as nn
from transformers import BertModel
from torch.autograd import Function
from transformers import BartTokenizer, BartModel
class ReverseLayerF(Function):
@staticmethod
def forward(ctx, x, alpha):
ctx.alpha = alpha
re... | 3,540 | 32.40566 | 106 | py |
DADER | DADER-main/modules/alignment.py | import torch
import sys
sys.path.append("..")
import param
import torch.nn as nn
from transformers import BartTokenizer, BartModel
from torch.autograd import Function
import torch.nn.functional as F
class ReverseLayerF(Function):
@staticmethod
def forward(ctx, x, alpha):
ctx.alpha = alpha
retur... | 4,534 | 33.884615 | 131 | py |
DADER | DADER-main/metrics/mmd.py | #!/usr/bin/env python
# encoding: utf-8
import torch
def guassian_kernel(source, target, kernel_mul=2.0, kernel_num=5, fix_sigma=None):
n_samples = int(source.size()[0])+int(target.size()[0])
total = torch.cat([source, target], dim=0)
total0 = total.unsqueeze(0).expand(int(total.size(0)), int(total.size(0... | 1,901 | 42.227273 | 98 | py |
DADER | DADER-main/metrics/coral.py | #!/usr/bin/env python
# encoding: utf-8
import torch
def cal_coral_loss(source, target):
batch_size = int(source.size()[0])
dim = int(source.size()[1])
source_T = torch.transpose(source,0,1)
target_T = torch.transpose(target,0,1)
cov_s = (1/(batch_size-1))*torch.mm(source_T, source)
cov_t = (1... | 903 | 33.769231 | 96 | py |
DADER | DADER-main/train/adapt_invgan_kd.py | import torch
import torch.nn.functional as F
import torch.nn as nn
import torch.optim as optim
from train.evaluate import evaluate
import param
from utils import make_cuda,save_model,init_model
import csv
import os
import math
import datetime
def adapt(args, src_encoder, tgt_encoder, discriminator,
src_class... | 14,220 | 39.51567 | 156 | py |
DADER | DADER-main/train/pretrain.py | """Pretrain F and M with labeled Source data."""
import torch
import torch.nn.functional as F
import torch.nn as nn
import torch.optim as optim
import param
from utils import make_cuda,save_model,init_model
import csv
import os
import datetime
from train.evaluate import evaluate
def pretrain(args, encoder, classifier, ... | 13,699 | 39.175953 | 154 | py |
DADER | DADER-main/train/adapt_k_order.py | import sys
sys.path.append('../')
import torch
from utils import make_cuda
import torch.nn.functional as F
import torch.nn as nn
import param
import torch.optim as optim
from utils import save_model
import csv
import os
from metrics import coral
import numpy as np
import itertools
def train(args, encoder, classifier,... | 6,119 | 35.646707 | 132 | py |
DADER | DADER-main/train/evaluate.py | """Adaptation to train target encoder."""
import torch
import torch.nn.functional as F
import torch.nn as nn
import torch.optim as optim
import sys
sys.path.append("..")
import param
from utils import make_cuda,save_model,init_model
import csv
import os
import datetime
def evaluate(encoder, classifier, data_loader,arg... | 2,194 | 28.662162 | 106 | py |
DADER | DADER-main/train/adapt_invgan.py | import torch
import torch.nn.functional as F
import torch.nn as nn
import torch.optim as optim
import param
from utils import make_cuda,save_model,init_model
from train.evaluate import evaluate
import csv
import os
import math
import datetime
def adapt_adda_best(args, src_encoder, tgt_encoder, discriminator,
... | 4,155 | 36.781818 | 115 | py |
DADER | DADER-main/train/adapt_grl.py | import torch
from utils import make_cuda
import torch.nn as nn
import param
import torch.optim as optim
from utils import save_model
import numpy as np
import csv
import os
def train(args, encoder, classifier, dom_classifier, src_data_loader, tgt_data_train_loader, tgt_data_valid_loader):
"""Train encoder for targ... | 6,399 | 36.647059 | 144 | py |
DADER | DADER-main/train/adapt_mmd.py | """Adversarial adaptation to train target encoder."""
import sys
sys.path.append('../')
import torch
from utils import make_cuda
import torch.nn.functional as F
import torch.nn as nn
import param
import torch.optim as optim
from utils import save_model
import csv
import os
from metrics import mmd
import numpy as np
imp... | 6,194 | 35.875 | 132 | py |
DADER | DADER-main/train/adapt_ed.py | import torch
from utils import make_cuda
import torch.nn.functional as F
import torch.nn as nn
from torch.nn import CrossEntropyLoss
import torch.optim as optim
from transformers import BartTokenizer
import param
from utils import save_model,init_model
import csv
import os
def train(args, encoder, classifier, decoder... | 5,680 | 35.184713 | 142 | py |
DADER | DADER-main/main/main_ed.py | """Main script for Encoder-Decoder."""
import sys
sys.path.append("..")
from train.adapt_ed import train, evaluate
from modules.extractor import BartEncoder
from modules.matcher import BertClassifier
from modules.alignment import BartDecoder
from utils import CSV2Array, bart_convert_examples_to_features, get_data_loade... | 7,353 | 42.77381 | 139 | py |
DADER | DADER-main/main/main_invgan_kd.py | """Main script for InvGAN + Knowledge Distillation (KD)."""
import sys
sys.path.append("..")
import param
from train.pretrain import pretrain,pretrain_best
from train.adapt_invgan_kd import adapt,adapt_best
from train.evaluate import evaluate
from modules.extractor import BertEncoder
from modules.matcher import BertCla... | 8,812 | 42.845771 | 111 | py |
DADER | DADER-main/main/main_grl.py | """Main script for Gradient reversal layer."""
import sys
sys.path.append("../")
import param
from train.adapt_grl import train, evaluate
from modules.extractor import BertEncoder
from modules.matcher import BertClassifier
from modules.alignment import DomainClassifier
from utils import CSV2Array, convert_examples_to_f... | 5,975 | 39.378378 | 160 | py |
DADER | DADER-main/main/main_mmd.py | """Main script for Maximum Mean Discrepancy (MMD)."""
import sys
sys.path.append("../")
import param
from train.adapt_mmd import train
from modules.extractor import BertEncoder
from modules.matcher import BertClassifier
from utils import CSV2Array, convert_examples_to_features, get_data_loader, init_model
from sklearn.... | 5,770 | 40.221429 | 146 | py |
DADER | DADER-main/main/main_invgan.py | """Main script for Inverted Labels GAN (InvGAN)."""
import sys
sys.path.append("..")
import param
from train.pretrain import pretrain,pretrain_best
from train.adapt_invgan import adapt_adda_best
from train.evaluate import evaluate
from modules.extractor import BertEncoder
from modules.matcher import BertClassifier
from... | 8,805 | 43.03 | 115 | py |
DADER | DADER-main/main/main_noda.py | """Main script for NoDA."""
import sys
sys.path.append("..")
import param
from train.pretrain import pretrain,pretrain_best
from train.adapt_invgan_kd import adapt,adapt_best
from train.evaluate import evaluate
from modules.extractor import BertEncoder
from modules.matcher import BertClassifier
from modules.alignment i... | 7,830 | 42.505556 | 111 | py |
DADER | DADER-main/main/main_k_order.py | """Main script for K-order."""
import sys
sys.path.append("../")
import param
from train.adapt_k_order import train, evaluate
from modules.extractor import BertEncoder
from modules.matcher import BertClassifier
from utils import CSV2Array, convert_examples_to_features, get_data_loader, init_model, save_model
from sklea... | 5,743 | 38.888889 | 144 | py |
pre-training-via-denoising | pre-training-via-denoising-main/setup.py | import subprocess
from setuptools import setup, find_packages
try:
version = (
subprocess.check_output(["git", "describe", "--abbrev=0", "--tags"])
.strip()
.decode("utf-8")
)
except:
print("Failed to retrieve the current version, defaulting to 0")
version = "0"
with open("requ... | 502 | 20.869565 | 76 | py |
pre-training-via-denoising | pre-training-via-denoising-main/scripts/train.py | import numpy as np # sometimes needed to avoid mkl-service error
import sys
import os
import argparse
import logging
import pytorch_lightning as pl
from pytorch_lightning.callbacks import EarlyStopping
from pytorch_lightning.callbacks.model_checkpoint import ModelCheckpoint
from pytorch_lightning.loggers import CSVLog... | 13,184 | 62.389423 | 185 | py |
pre-training-via-denoising | pre-training-via-denoising-main/tests/test_datasets.py | import pytest
from pytest import mark, raises
from os.path import join
import numpy as np
from torchmdnet.datasets import Custom
@mark.parametrize("energy", [True, False])
@mark.parametrize("forces", [True, False])
@mark.parametrize("num_files", [1, 3])
def test_custom(energy, forces, num_files, tmpdir, num_samples=1... | 1,816 | 33.942308 | 87 | py |
pre-training-via-denoising | pre-training-via-denoising-main/tests/test_optimize.py | import pytest
from pytest import mark
import torch as pt
from torchmdnet.models.model import create_model
from torchmdnet.optimize import optimize
@mark.parametrize('device', ['cpu', 'cuda'])
@mark.parametrize('num_atoms', [10, 100])
def test_gn(device, num_atoms):
if not pt.cuda.is_available() and device == 'cud... | 1,646 | 29.5 | 109 | py |
pre-training-via-denoising | pre-training-via-denoising-main/tests/test_rbfs.py | from pytest import mark
import torch
from torchmdnet.models.utils import rbf_class_mapping
@mark.parametrize("name,rbf_class", list(rbf_class_mapping.items()))
def test_num_rbf(name, rbf_class, num_rbf=20):
rbf = rbf_class(num_rbf=num_rbf)
y = rbf(torch.linspace(0, 10, 100))
assert y.ndim == 2, "Failed to... | 1,112 | 34.903226 | 84 | py |
pre-training-via-denoising | pre-training-via-denoising-main/tests/utils.py | import yaml
from os.path import dirname, join
import torch
from torch_geometric.data import Dataset, Data
def load_example_args(model_name, remove_prior=False, **kwargs):
with open(join(dirname(dirname(__file__)), "examples", "ET-QM9.yaml"), "r") as f:
args = yaml.load(f, Loader=yaml.FullLoader)
args[... | 2,576 | 29.317647 | 85 | py |
pre-training-via-denoising | pre-training-via-denoising-main/tests/test_equivariance.py | import torch
from torchmdnet.models.model import create_model
from utils import load_example_args
def test_scalar_invariance():
torch.manual_seed(1234)
rotate = torch.tensor(
[
[0.9886788, -0.1102370, 0.1017945],
[0.1363630, 0.9431761, -0.3030248],
[-0.0626055, 0.31... | 1,393 | 28.041667 | 88 | py |
pre-training-via-denoising | pre-training-via-denoising-main/tests/test_cfconv.py | import pytest
from pytest import mark
import torch as pt
from torchmdnet.models.torchmd_gn import CFConv as RefCFConv
from torchmdnet.models.utils import Distance, GaussianSmearing, ShiftedSoftplus
from NNPOps.CFConv import CFConv
from NNPOps.CFConvNeighbors import CFConvNeighbors
@mark.parametrize('device', ['cpu', ... | 2,640 | 35.680556 | 127 | py |
pre-training-via-denoising | pre-training-via-denoising-main/tests/test_module.py | from pytest import mark
from glob import glob
from os.path import dirname, join
import pytorch_lightning as pl
from torchmdnet import models
from torchmdnet.models.model import load_model
from torchmdnet.priors import Atomref
from torchmdnet.module import LNNP
from torchmdnet.data import DataModule
from utils import l... | 1,185 | 26.581395 | 74 | py |
pre-training-via-denoising | pre-training-via-denoising-main/tests/test_model.py | import pytest
from pytest import mark
import pickle
from os.path import exists, dirname, join
import torch
import pytorch_lightning as pl
from torchmdnet import models
from torchmdnet.models.model import create_model
from torchmdnet.models import output_modules
from utils import load_example_args, create_example_batch... | 3,317 | 33.5625 | 97 | py |
pre-training-via-denoising | pre-training-via-denoising-main/tests/test_calculator.py | import torch
from torch.testing import assert_allclose
from pytest import mark
from glob import glob
from os.path import dirname, join
from torchmdnet.calculators import External
from torchmdnet.models.model import load_model
from utils import create_example_batch
def test_compare_forward():
checkpoint = join(di... | 1,398 | 33.121951 | 74 | py |
pre-training-via-denoising | pre-training-via-denoising-main/tests/test_datamodule.py | from pytest import mark
import torch
from torchmdnet.data import DataModule
from utils import load_example_args, DummyDataset
def test_datamodule_create(tmpdir):
args = load_example_args("graph-network")
args["train_size"] = 800
args["val_size"] = 100
args["test_size"] = 100
args["log_dir"] = tmpd... | 2,367 | 36 | 99 | py |
pre-training-via-denoising | pre-training-via-denoising-main/tests/test_utils.py | from os.path import join, exists
from pytest import mark, raises
import torch
from torchmdnet.utils import make_splits
def sum_lengths(*args):
return sum(map(len, args))
def test_make_splits_outputs():
result = make_splits(100, 0.7, 0.2, 0.1, 1234)
assert len(result) == 3
assert isinstance(result[0]... | 3,035 | 36.95 | 86 | py |
pre-training-via-denoising | pre-training-via-denoising-main/torchmdnet/priors.py | from abc import abstractmethod, ABCMeta
import torch
from torch import nn
from pytorch_lightning.utilities import rank_zero_warn
__all__ = ["Atomref"]
class BasePrior(nn.Module, metaclass=ABCMeta):
r"""Base class for prior models.
Derive this class to make custom prior models, which take some arguments and ... | 2,800 | 34.455696 | 100 | py |
pre-training-via-denoising | pre-training-via-denoising-main/torchmdnet/utils.py | import yaml
import argparse
import numpy as np
import torch
from os.path import dirname, join, exists
from pytorch_lightning.utilities import rank_zero_warn
def train_val_test_split(dset_len, train_size, val_size, test_size, seed, order=None):
assert (train_size is None) + (val_size is None) + (
test_size... | 5,127 | 31.455696 | 100 | py |
pre-training-via-denoising | pre-training-via-denoising-main/torchmdnet/module.py | import torch
from torch.optim import AdamW
from torch.optim.lr_scheduler import ReduceLROnPlateau, CosineAnnealingLR
from torch.nn.functional import mse_loss, l1_loss
from pytorch_lightning import LightningModule
from torchmdnet.models.model import create_model, load_model
class LNNP(LightningModule):
def __init... | 10,910 | 40.645038 | 131 | py |
pre-training-via-denoising | pre-training-via-denoising-main/torchmdnet/data.py | from os.path import join
from tqdm import tqdm
import torch
from torch.utils.data import Subset
from torch_geometric.data import DataLoader
from pytorch_lightning import LightningDataModule
from pytorch_lightning.utilities import rank_zero_warn
from torchmdnet import datasets
from torchmdnet.utils import make_splits, M... | 6,091 | 36.838509 | 170 | py |
pre-training-via-denoising | pre-training-via-denoising-main/torchmdnet/optimize.py | import torch as pt
from NNPOps.CFConv import CFConv
from NNPOps.CFConvNeighbors import CFConvNeighbors
from .models.model import TorchMD_Net
from .models.torchmd_gn import TorchMD_GN
class TorchMD_GN_optimized(pt.nn.Module):
def __init__(self, model):
if model.rbf_type != 'gauss':
raise Val... | 2,217 | 32.104478 | 87 | py |
pre-training-via-denoising | pre-training-via-denoising-main/torchmdnet/calculators.py | import torch
from torchmdnet.models.model import load_model
class External:
def __init__(self, netfile, embeddings, device="cpu"):
self.model = load_model(netfile, device=device, derivative=True)
self.device = device
self.n_atoms = embeddings.size(1)
self.embeddings = embeddings.re... | 748 | 36.45 | 87 | py |
pre-training-via-denoising | pre-training-via-denoising-main/torchmdnet/models/torchmd_gn.py | from torch import nn
from torch_geometric.nn import MessagePassing
from torchmdnet.models.utils import (
NeighborEmbedding,
CosineCutoff,
Distance,
rbf_class_mapping,
act_class_mapping,
)
class TorchMD_GN(nn.Module):
r"""The TorchMD Graph Network architecture.
Code adapted from https://git... | 9,046 | 34.065891 | 152 | py |
pre-training-via-denoising | pre-training-via-denoising-main/torchmdnet/models/utils.py | import math
import torch
from torch import nn
import torch.nn.functional as F
from torch_geometric.nn import MessagePassing
from torch_cluster import radius_graph
def visualize_basis(basis_type, num_rbf=50, cutoff_lower=0, cutoff_upper=5):
"""
Function for quickly visualizing a specific basis. This is useful ... | 10,640 | 34.352159 | 96 | py |
pre-training-via-denoising | pre-training-via-denoising-main/torchmdnet/models/model.py | import re
from typing import Optional, List, Tuple
import torch
from torch.autograd import grad
from torch import nn
from torch_scatter import scatter
from pytorch_lightning.utilities import rank_zero_warn
from torchmdnet.models import output_modules
from torchmdnet.models.wrappers import AtomFilter
from torchmdnet imp... | 9,663 | 34.270073 | 122 | py |
pre-training-via-denoising | pre-training-via-denoising-main/torchmdnet/models/wrappers.py | from abc import abstractmethod, ABCMeta
from torch import nn
class BaseWrapper(nn.Module, metaclass=ABCMeta):
r"""Base class for model wrappers.
Children of this class should implement the `forward` method,
which calls `self.model(z, pos, batch=batch)` at some point.
Wrappers that are applied before ... | 1,641 | 30.576923 | 99 | py |
pre-training-via-denoising | pre-training-via-denoising-main/torchmdnet/models/torchmd_et.py | from typing import Optional, Tuple
import torch
from torch import nn
from torch_geometric.nn import MessagePassing
from torch_scatter import scatter
from torchmdnet.models.utils import (
NeighborEmbedding,
CosineCutoff,
Distance,
rbf_class_mapping,
act_class_mapping,
)
from torch.nn.parameter import... | 16,319 | 36.090909 | 124 | py |
pre-training-via-denoising | pre-training-via-denoising-main/torchmdnet/models/torchmd_t.py | from torch import nn
from torch_geometric.nn import MessagePassing
from torchmdnet.models.utils import (
NeighborEmbedding,
CosineCutoff,
Distance,
rbf_class_mapping,
act_class_mapping,
)
class TorchMD_T(nn.Module):
r"""The TorchMD Transformer architecture.
Args:
hidden_channels (... | 10,080 | 36.199262 | 97 | py |
pre-training-via-denoising | pre-training-via-denoising-main/torchmdnet/models/output_modules.py | from abc import abstractmethod, ABCMeta
from typing import Optional
import ase
from torchmdnet.models.utils import act_class_mapping, GatedEquivariantBlock
from torch_scatter import scatter
import torch
from torch import nn
__all__ = ["Scalar", "DipoleMoment", "ElectronicSpatialExtent"]
class OutputModel(nn.Module,... | 5,641 | 32.784431 | 86 | py |
pre-training-via-denoising | pre-training-via-denoising-main/torchmdnet/datasets/custom.py | import glob
import numpy as np
import torch
from torch_geometric.data import Dataset, Data
class Custom(Dataset):
r"""Custom Dataset to manage loading coordinates, embedding indices,
energies and forces from NumPy files. :obj:`coordglob` and :obj:`embedglob`
are required parameters. Either :obj:`energyglo... | 4,416 | 40.669811 | 99 | py |
pre-training-via-denoising | pre-training-via-denoising-main/torchmdnet/datasets/md17.py | import torch
from torch_geometric.data import InMemoryDataset, download_url, Data
from pytorch_lightning.utilities import rank_zero_warn
import numpy as np
class MD17(InMemoryDataset):
"""Machine learning of accurate energy-conserving molecular force fields (Chmiela et al. 2017)
This class provides functional... | 3,768 | 35.95098 | 118 | py |
pre-training-via-denoising | pre-training-via-denoising-main/torchmdnet/datasets/pcqm4mv2.py | from typing import Optional, Callable, List
import os
from tqdm import tqdm
import glob
import ase
import numpy as np
import torch
from torch_geometric.data import (InMemoryDataset, download_url, extract_zip,
Data)
class PCQM4MV2_XYZ(InMemoryDataset):
r"""3D coordinates for mol... | 2,934 | 32.735632 | 111 | py |
pre-training-via-denoising | pre-training-via-denoising-main/torchmdnet/datasets/ani1.py | import os
from os.path import join
from tqdm import tqdm
from urllib import request
import torch
from torch_geometric.data import InMemoryDataset, extract_tar, Data
import h5py
class ANI1(InMemoryDataset):
raw_url = "https://ndownloader.figshare.com/files/9057631"
element_numbers = {"H": 1, "C": 6, "N": 7, ... | 2,840 | 33.228916 | 81 | py |
pre-training-via-denoising | pre-training-via-denoising-main/torchmdnet/datasets/qm9.py | import torch
from torch_geometric.transforms import Compose
from torch_geometric.datasets import QM9 as QM9_geometric
from torch_geometric.nn.models.schnet import qm9_target_dict
class QM9(QM9_geometric):
def __init__(self, root, transform=None, dataset_arg=None):
assert dataset_arg is not None, (
... | 1,463 | 30.826087 | 79 | py |
pre-training-via-denoising | pre-training-via-denoising-main/torchmdnet/datasets/hdf.py | import torch
from torch_geometric.data import Dataset, Data
import h5py
class HDF5(Dataset):
"""A custom dataset that loads data from a HDF5 file.
To use this, dataset_root should be the path to the HDF5 file, or alternatively
a semicolon separated list of multiple files. Each group in the file contains... | 2,254 | 36.583333 | 85 | py |
MaskSpec | MaskSpec-main/trainer/test.py | import argparse
import datetime
import json
import numpy as np
import os
import time
from pathlib import Path
import torch
import torch.backends.cudnn as cudnn
from torch.utils.tensorboard import SummaryWriter
import timm
from timm.models.layers import trunc_normal_
from timm.loss import BinaryCrossEntropy
import sys... | 16,646 | 43.273936 | 138 | py |
MaskSpec | MaskSpec-main/trainer/main_pretrain.py | import argparse
from ast import arg
import datetime
import json
import numpy as np
import os
import time
from pathlib import Path
import torch
import torch.backends.cudnn as cudnn
from torch.utils.tensorboard import SummaryWriter
import timm
import timm.optim.optim_factory as optim_factory
import sys
sys.path.append(... | 12,606 | 42.472414 | 138 | py |
MaskSpec | MaskSpec-main/trainer/engine_finetune.py | import math
import sys
from typing import Iterable
from sklearn import metrics
import torch
import os
sys.path.append(os.path.dirname(os.path.dirname(os.path.realpath(__file__))))
import utils.misc as misc
import utils.lr_sched as lr_sched
from timm.loss import BinaryCrossEntropy
import numpy as np
def train_one_epoch... | 4,821 | 38.203252 | 114 | py |
MaskSpec | MaskSpec-main/trainer/engine_pretrain.py | # Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
# --------------------------------------------------------
# References:
# DeiT: https://github.com/facebookresearch/deit
#... | 3,103 | 35.952381 | 108 | py |
MaskSpec | MaskSpec-main/trainer/main_finetune.py | import argparse
import datetime
import json
import numpy as np
import os
import time
from pathlib import Path
import torch
import torch.backends.cudnn as cudnn
from torch.utils.tensorboard import SummaryWriter
import timm
from timm.models.layers import trunc_normal_
from timm.loss import BinaryCrossEntropy
import sys... | 17,554 | 44.361757 | 138 | py |
MaskSpec | MaskSpec-main/openmic18/get_mean_std.py | import torch
import numpy as np
import h5py
import os
import sys
sys.path.append(os.path.dirname(os.path.dirname(os.path.realpath(__file__))))
from scv2.dataset import decode_mp3, pad_or_truncate
from models.models_mae import AugmentMelSTFT
def get_mean_std(n_mel=128, sample_number=10000):
print('Start...')
h... | 2,415 | 34.014493 | 95 | py |
MaskSpec | MaskSpec-main/openmic18/convert_to_mp3.py | import os
import tarfile
import multiprocessing
import glob
import h5py
import numpy as np
from torch.hub import download_url_to_file
# global constants
openmicurl = "https://zenodo.org/record/1432913/files/openmic-2018-v1.0.0.tgz?download=1"
download_target = "openmic-2018-v1.0.0.tgz"
extract_target = download_targe... | 6,626 | 37.306358 | 133 | py |
MaskSpec | MaskSpec-main/openmic18/engine_run.py | import math
import sys
from typing import Iterable
import torch
import os
sys.path.append(os.path.dirname(os.path.dirname(os.path.realpath(__file__))))
import utils.misc as misc
import utils.lr_sched as lr_sched
from timm.utils import accuracy
import numpy as np
from sklearn import metrics
from torch.nn import function... | 5,730 | 38.798611 | 114 | py |
MaskSpec | MaskSpec-main/openmic18/dataset.py | import io
import os
import random
import av
from torch.utils.data import Dataset as TorchDataset
import torch
import numpy as np
import sys
sys.path.append(os.path.dirname(os.path.dirname(os.path.realpath(__file__))))
from audioset.audiodatasets import PreprocessDataset
import h5py
import augly.audio as audaugs
LMODE... | 8,018 | 34.482301 | 158 | py |
MaskSpec | MaskSpec-main/openmic18/run.py | import argparse
import datetime
import json
import numpy as np
import os
import time
from pathlib import Path
import torch
import torch.backends.cudnn as cudnn
from torch.utils.tensorboard import SummaryWriter
import timm
from timm.models.layers import trunc_normal_
from timm.loss import LabelSmoothingCrossEntropy, S... | 16,147 | 43.607735 | 163 | py |
MaskSpec | MaskSpec-main/audioset/get_mean_std.py | import torch
import numpy as np
import h5py
import os
import sys
sys.path.append(os.path.dirname(os.path.dirname(os.path.realpath(__file__))))
from audioset.dataset import decode_mp3, pad_or_truncate
from models.models_mae import AugmentMelSTFT
def get_mean_std(n_mel=128, sample_number=10000):
print('Start...')
... | 2,373 | 33.911765 | 94 | py |
MaskSpec | MaskSpec-main/audioset/audiodatasets.py | import hashlib
import os
import time
import torch
from torch.utils.data import Dataset
from os.path import expanduser
import logging
def h6(w):
return hashlib.md5(w.encode('utf-8')).hexdigest()[:6]
class AudioPreprocessDataset(Dataset):
"""A bases preprocessing dataset representing a Dataset of files that ... | 7,509 | 32.377778 | 116 | py |
MaskSpec | MaskSpec-main/audioset/dataset.py | import io
import os
import random
import av
from torch.utils.data import Dataset as TorchDataset, ConcatDataset, DistributedSampler, WeightedRandomSampler, RandomSampler
import torch
import numpy as np
import sys
sys.path.append(os.path.dirname(os.path.dirname(os.path.realpath(__file__))))
from audioset.audiodatasets ... | 15,256 | 39.255937 | 220 | py |
MaskSpec | MaskSpec-main/audioset/prepare_scripts/create_h5pymp3_dataset.py | # %%
import h5py
import pandas as pd
import numpy as np
import csv
import os
# %%
base_dir = "/data/dean/whl/audioset_Kong/"
balanced_csv= base_dir+ "metadata/balanced_train_segments.csv"
eval_csv= base_dir+ "metadata/eval_segments.csv"
mp3_path = "/data/dean/whl/PaSST/audioset/prepare_scripts/mp3_audio/"
# %%
def... | 5,884 | 30.639785 | 133 | py |
MaskSpec | MaskSpec-main/dcase19/get_mean_std.py | import torch
import numpy as np
import h5py
import os
import sys
sys.path.append(os.path.dirname(os.path.dirname(os.path.realpath(__file__))))
from scv2.dataset import decode_mp3, pad_or_truncate
from models.models_mae import AugmentMelSTFT
def get_mean_std(n_mel=128, sample_number=10000, hdf5_file = './dcase19/data/... | 3,067 | 36.414634 | 134 | py |
MaskSpec | MaskSpec-main/dcase19/run_ensemble.py | import argparse
import datetime
import json
import numpy as np
import os
import time
from pathlib import Path
import torch
import torch.backends.cudnn as cudnn
from torch.utils.tensorboard import SummaryWriter
import timm
from timm.models.layers import trunc_normal_
from timm.loss import LabelSmoothingCrossEntropy, S... | 14,262 | 44.423567 | 147 | py |
MaskSpec | MaskSpec-main/dcase19/convert_to_mp3.py | import argparse
import multiprocessing
import os
from torch import float64
import wget
import numpy as np
import csv
import soundfile as sf
# prepare the data of the dcase19t1a dataset.
print('Now download and process dcase19t1a dataset, it will take a few moments...')
# download the dcase19t1a dataset
if os.path.ex... | 6,480 | 50.031496 | 266 | py |
MaskSpec | MaskSpec-main/dcase19/engine_run.py | import math
import sys
from typing import Iterable
import torch
import os
sys.path.append(os.path.dirname(os.path.dirname(os.path.realpath(__file__))))
import utils.misc as misc
import utils.lr_sched as lr_sched
from timm.utils import accuracy
def train_one_epoch(model: torch.nn.Module, criterion: torch.nn.Module,
... | 6,990 | 39.645349 | 120 | py |
MaskSpec | MaskSpec-main/dcase19/dataset.py | import io
import os
import random
import av
from torch.utils.data import Dataset as TorchDataset
import torch
import numpy as np
import sys
sys.path.append(os.path.dirname(os.path.dirname(os.path.realpath(__file__))))
from audioset.audiodatasets import PreprocessDataset
import h5py
import augly.audio as audaugs
LMODE... | 7,968 | 34.261062 | 158 | py |
MaskSpec | MaskSpec-main/dcase19/run.py | import argparse
import datetime
import json
import numpy as np
import os
import time
from pathlib import Path
import torch
import torch.backends.cudnn as cudnn
from torch.utils.tensorboard import SummaryWriter
import timm
from timm.models.layers import trunc_normal_
from timm.loss import LabelSmoothingCrossEntropy, S... | 16,741 | 43.884718 | 163 | py |
MaskSpec | MaskSpec-main/models/models_swin.py | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.checkpoint as checkpoint
from timm.models.layers import DropPath, to_2tuple, trunc_normal_
class Mlp(nn.Module):
def __init__(self, in_features, hidden_features=None, out_features=None, act_layer=nn.GELU, drop=0.):
super(... | 24,141 | 40.986087 | 119 | py |
MaskSpec | MaskSpec-main/models/models_simMIM.py | import torch
import torch.nn as nn
import torch.nn.functional as F
from timm.models.layers import trunc_normal_
import numpy as np
import os
import sys
sys.path.append(os.path.dirname(os.path.dirname(os.path.realpath(__file__))))
from models.models_swin import SwinTransformer
from models.models_mae import AugmentMelSTF... | 5,750 | 38.9375 | 121 | py |
MaskSpec | MaskSpec-main/models/models_mae.py | # Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
# --------------------------------------------------------
# References:
# timm: https://github.com/rwightman/pytorch-image... | 14,899 | 40.853933 | 163 | py |
MaskSpec | MaskSpec-main/models/models_vit.py | # Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
# --------------------------------------------------------
# References:
# timm: https://github.com/rwightman/pytorch-image... | 6,973 | 41.785276 | 139 | py |
MaskSpec | MaskSpec-main/models/models_swinTrans.py | import torch
import torch.nn as nn
import numpy as np
import os
import sys
sys.path.append(os.path.dirname(os.path.dirname(os.path.realpath(__file__))))
import models.models_swin
from models.models_mae import AugmentMelSTFT
class SwinTransformer(models.models_swin.SwinTransformer):
def __init__(self, n_mels=64, sr... | 3,434 | 40.385542 | 115 | py |
MaskSpec | MaskSpec-main/scv2/get_mean_std.py | import torch
import numpy as np
import h5py
import os
import sys
sys.path.append(os.path.dirname(os.path.dirname(os.path.realpath(__file__))))
from scv2.dataset import decode_mp3, pad_or_truncate
from models.models_mae import AugmentMelSTFT
def get_mean_std(n_mel=128, sample_number=10000):
print('Start...')
h... | 2,323 | 33.686567 | 90 | py |
MaskSpec | MaskSpec-main/scv2/convert_to_mp3.py | import argparse
import multiprocessing
import glob
import os
import wget
import zipfile
import numpy as np
# prepare the data of the speechcommands dataset.
print('Now download and process speechcommands dataset, it will take a few moments...')
# download the speechcommands dataset
if os.path.exists('./scv2/data/spee... | 4,355 | 39.71028 | 151 | py |
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