repo stringlengths 2 99 | file stringlengths 13 225 | code stringlengths 0 18.3M | file_length int64 0 18.3M | avg_line_length float64 0 1.36M | max_line_length int64 0 4.26M | extension_type stringclasses 1
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UString | UString-master/src/Models.py | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import inspect
from torch.nn.parameter import Parameter
import torch
import torch.nn as nn
from src.utils import glorot, zeros, uniform, reset
from torch_geometric.utils import remove_self_loops, add_self_loops... | 20,930 | 41.03012 | 145 | py |
UString | UString-master/script/vis_dad_det.py | import os
import numpy as np
import cv2
def vis_det(data_path, video_path, phase='training'):
files_list = []
batch_id = 1
for filename in sorted(os.listdir(os.path.join(data_path, phase))):
filepath = os.path.join(data_path, phase, filename)
all_data = np.load(filepath)
features = ... | 1,915 | 41.577778 | 104 | py |
UString | UString-master/script/split_dad.py | import os
import numpy as np
def process(data_path, dest_path, phase):
files_list = []
batch_id = 1
for filename in sorted(os.listdir(os.path.join(data_path, phase))):
filepath = os.path.join(data_path, phase, filename)
all_data = np.load(filepath)
features = all_data['data'] # 10 ... | 1,870 | 37.183673 | 109 | py |
UString | UString-master/script/extract_res101_dad.py | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import os.path as osp
import numpy as np
import os, cv2
import argparse, sys
from tqdm import tqdm
import torch
import torch.nn as nn
from torchvision import models, transforms
from torch.autograd import Varia... | 7,692 | 38.654639 | 146 | py |
UString | UString-master/script/vis_crash_det.py | import os, cv2
import numpy as np
def get_video_frames(video_file, n_frames=50):
assert os.path.exists(video_file), video_file
# get the video data
cap = cv2.VideoCapture(video_file)
ret, frame = cap.read()
video_data = []
counter = 0
while (ret):
video_data.append(frame)
r... | 2,125 | 39.884615 | 111 | py |
SelfDeblur | SelfDeblur-master/selfdeblur_levin_reproduce.py |
# coding: utf-8
from __future__ import print_function
import matplotlib.pyplot as plt
import argparse
import os
import numpy as np
import cv2
import torch
import torch.optim
import glob
from skimage.io import imread
from skimage.io import imsave
import warnings
from tqdm import tqdm
from torch.optim.lr_scheduler imp... | 5,559 | 33.75 | 156 | py |
SelfDeblur | SelfDeblur-master/SSIM.py | import torch
import torch.nn.functional as F
from torch.autograd import Variable
import numpy as np
from math import exp
def gaussian(window_size, sigma):
gauss = torch.Tensor([exp(-(x - window_size // 2) ** 2 / float(2 * sigma ** 2)) for x in range(window_size)])
return gauss / gauss.sum()
def create_windo... | 2,620 | 33.038961 | 114 | py |
SelfDeblur | SelfDeblur-master/selfdeblur_lai_reproduce.py |
# coding: utf-8
from __future__ import print_function
import matplotlib.pyplot as plt
import argparse
import os
import numpy as np
import cv2
import torch
import torch.optim
import glob
from skimage.io import imread
from skimage.io import imsave
import warnings
from tqdm import tqdm
from torch.optim.lr_scheduler imp... | 5,294 | 33.835526 | 156 | py |
SelfDeblur | SelfDeblur-master/selfdeblur_lai.py |
from __future__ import print_function
import matplotlib.pyplot as plt
import argparse
import os
import numpy as np
from networks.skip import skip
from networks.fcn import *
import cv2
import torch
import torch.optim
import glob
from skimage.io import imread
from skimage.io import imsave
import warnings
from tqdm impo... | 5,242 | 33.045455 | 156 | py |
SelfDeblur | SelfDeblur-master/selfdeblur_nonblind.py |
from __future__ import print_function
import matplotlib.pyplot as plt
import argparse
import os
import numpy as np
from networks.skip import skip
from networks.fcn import *
import cv2
import torch
import torch.optim
import glob
from skimage.io import imread
from skimage.io import imsave
import warnings
from tqdm impor... | 4,721 | 32.489362 | 128 | py |
SelfDeblur | SelfDeblur-master/selfdeblur_ycbcr.py |
from __future__ import print_function
import matplotlib.pyplot as plt
import argparse
import os
import numpy as np
from networks.skip import skip
from networks.fcn import fcn
import cv2
import torch
import torch.optim
from torch.autograd import Variable
import glob
from skimage.io import imread
from skimage.io import ... | 5,714 | 34.06135 | 156 | py |
SelfDeblur | SelfDeblur-master/selfdeblur_levin.py |
from __future__ import print_function
import matplotlib.pyplot as plt
import argparse
import os
import numpy as np
from networks.skip import skip
from networks.fcn import fcn
import cv2
import torch
import torch.optim
import glob
from skimage.io import imread
from skimage.io import imsave
import warnings
from tqdm imp... | 5,395 | 32.515528 | 126 | py |
SelfDeblur | SelfDeblur-master/networks/fcn.py | import torch
import torch.nn as nn
from .common import *
def fcn(num_input_channels=200, num_output_channels=1, num_hidden=1000):
model = nn.Sequential()
model.add(nn.Linear(num_input_channels, num_hidden,bias=True))
model.add(nn.ReLU6())
#
model.add(nn.Linear(num_hidden, num_output_channels))
# m... | 398 | 13.25 | 72 | py |
SelfDeblur | SelfDeblur-master/networks/non_local_embedded_gaussian.py | import torch
from torch import nn
from torch.nn import functional as F
class _NonLocalBlockND(nn.Module):
def __init__(self, in_channels, inter_channels=None, dimension=3, sub_sample=True, bn_layer=True):
super(_NonLocalBlockND, self).__init__()
assert dimension in [1, 2, 3]
self.dimensi... | 4,916 | 36.25 | 102 | py |
SelfDeblur | SelfDeblur-master/networks/skip.py | import torch
import torch.nn as nn
from .common import *
#from .non_local_embedded_gaussian import NONLocalBlock2D
#from .non_local_concatenation import NONLocalBlock2D
#from .non_local_gaussian import NONLocalBlock2D
from .non_local_dot_product import NONLocalBlock2D
def skip(
num_input_channels=2, num_out... | 4,045 | 36.119266 | 144 | py |
SelfDeblur | SelfDeblur-master/networks/resnet.py | import torch
import torch.nn as nn
from numpy.random import normal
from numpy.linalg import svd
from math import sqrt
import torch.nn.init
from .common import *
class ResidualSequential(nn.Sequential):
def __init__(self, *args):
super(ResidualSequential, self).__init__(*args)
def forward(self, x):
... | 2,945 | 29.371134 | 195 | py |
SelfDeblur | SelfDeblur-master/networks/downsampler.py | import numpy as np
import torch
import torch.nn as nn
class Downsampler(nn.Module):
'''
http://www.realitypixels.com/turk/computergraphics/ResamplingFilters.pdf
'''
def __init__(self, n_planes, factor, kernel_type, phase=0, kernel_width=None, support=None, sigma=None, preserve_size=False):
... | 7,872 | 31.66805 | 129 | py |
SelfDeblur | SelfDeblur-master/networks/non_local_dot_product.py | import torch
from torch import nn
from torch.nn import functional as F
class _NonLocalBlockND(nn.Module):
def __init__(self, in_channels, inter_channels=None, dimension=3, sub_sample=True, bn_layer=True):
super(_NonLocalBlockND, self).__init__()
assert dimension in [1, 2, 3]
self.dimensi... | 4,926 | 35.496296 | 102 | py |
SelfDeblur | SelfDeblur-master/networks/non_local_concatenation.py | import torch
from torch import nn
from torch.nn import functional as F
class _NonLocalBlockND(nn.Module):
def __init__(self, in_channels, inter_channels=None, dimension=3, sub_sample=True, bn_layer=True):
super(_NonLocalBlockND, self).__init__()
assert dimension in [1, 2, 3]
self.dimensi... | 5,350 | 35.155405 | 102 | py |
SelfDeblur | SelfDeblur-master/networks/common.py | import torch
import torch.nn as nn
import numpy as np
from .downsampler import Downsampler
def add_module(self, module):
self.add_module(str(len(self) + 1), module)
torch.nn.Module.add = add_module
class Concat(nn.Module):
def __init__(self, dim, *args):
super(Concat, self).__init__()
sel... | 3,531 | 27.483871 | 128 | py |
SelfDeblur | SelfDeblur-master/networks/unet.py | import torch.nn as nn
import torch
import torch.nn as nn
import torch.nn.functional as F
from .common import *
class ListModule(nn.Module):
def __init__(self, *args):
super(ListModule, self).__init__()
idx = 0
for module in args:
self.add_module(str(idx), module)
id... | 7,408 | 36.045 | 164 | py |
SelfDeblur | SelfDeblur-master/networks/non_local_gaussian.py | import torch
from torch import nn
from torch.nn import functional as F
class _NonLocalBlockND(nn.Module):
def __init__(self, in_channels, inter_channels=None, dimension=3, sub_sample=True, bn_layer=True):
super(_NonLocalBlockND, self).__init__()
assert dimension in [1, 2, 3]
self.dimensi... | 4,674 | 33.124088 | 102 | py |
SelfDeblur | SelfDeblur-master/models/skipfc.py | import torch
import torch.nn as nn
from .common import *
def skipfc(num_input_channels=2, num_output_channels=3,
num_channels_down=[16, 32, 64, 128, 128], num_channels_up=[16, 32, 64, 128, 128], num_channels_skip=[4, 4, 4, 4, 4],
filter_size_down=3, filter_size_up=1, filter_skip_size=1,
... | 5,145 | 34.489655 | 128 | py |
SelfDeblur | SelfDeblur-master/models/non_local_embedded_gaussian.py | import torch
from torch import nn
from torch.nn import functional as F
class _NonLocalBlockND(nn.Module):
def __init__(self, in_channels, inter_channels=None, dimension=3, sub_sample=True, bn_layer=True):
super(_NonLocalBlockND, self).__init__()
assert dimension in [1, 2, 3]
self.dimensi... | 4,916 | 36.25 | 102 | py |
SelfDeblur | SelfDeblur-master/models/skip.py | import torch
import torch.nn as nn
from .common import *
from .non_local_dot_product import NONLocalBlock2D
def skip(
num_input_channels=2, num_output_channels=3,
num_channels_down=[16, 32, 64, 128, 128], num_channels_up=[16, 32, 64, 128, 128], num_channels_skip=[4, 4, 4, 4, 4],
filter_si... | 3,885 | 35.317757 | 144 | py |
SelfDeblur | SelfDeblur-master/models/resnet.py | import torch
import torch.nn as nn
from numpy.random import normal
from numpy.linalg import svd
from math import sqrt
import torch.nn.init
from .common import *
class ResidualSequential(nn.Sequential):
def __init__(self, *args):
super(ResidualSequential, self).__init__(*args)
def forward(self, x):
... | 2,945 | 29.371134 | 195 | py |
SelfDeblur | SelfDeblur-master/models/downsampler.py | import numpy as np
import torch
import torch.nn as nn
class Downsampler(nn.Module):
'''
http://www.realitypixels.com/turk/computergraphics/ResamplingFilters.pdf
'''
def __init__(self, n_planes, factor, kernel_type, phase=0, kernel_width=None, support=None, sigma=None, preserve_size=False):
... | 7,872 | 31.66805 | 129 | py |
SelfDeblur | SelfDeblur-master/models/non_local_dot_product.py | import torch
from torch import nn
from torch.nn import functional as F
class _NonLocalBlockND(nn.Module):
def __init__(self, in_channels, inter_channels=None, dimension=3, sub_sample=True, bn_layer=True):
super(_NonLocalBlockND, self).__init__()
assert dimension in [1, 2, 3]
self.dimensi... | 4,926 | 35.496296 | 102 | py |
SelfDeblur | SelfDeblur-master/models/texture_nets.py | import torch
import torch.nn as nn
from .common import *
normalization = nn.BatchNorm2d
def conv(in_f, out_f, kernel_size, stride=1, bias=True, pad='zero'):
if pad == 'zero':
return nn.Conv2d(in_f, out_f, kernel_size, stride, padding=(kernel_size - 1) / 2, bias=bias)
elif pad == 'reflection':
... | 2,315 | 27.95 | 146 | py |
SelfDeblur | SelfDeblur-master/models/non_local_concatenation.py | import torch
from torch import nn
from torch.nn import functional as F
class _NonLocalBlockND(nn.Module):
def __init__(self, in_channels, inter_channels=None, dimension=3, sub_sample=True, bn_layer=True):
super(_NonLocalBlockND, self).__init__()
assert dimension in [1, 2, 3]
self.dimensi... | 5,350 | 35.155405 | 102 | py |
SelfDeblur | SelfDeblur-master/models/common.py | import torch
import torch.nn as nn
import numpy as np
from .downsampler import Downsampler
def add_module(self, module):
self.add_module(str(len(self) + 1), module)
torch.nn.Module.add = add_module
class Concat(nn.Module):
def __init__(self, dim, *args):
super(Concat, self).__init__()
sel... | 3,531 | 27.483871 | 128 | py |
SelfDeblur | SelfDeblur-master/models/unet.py | import torch.nn as nn
import torch
import torch.nn as nn
import torch.nn.functional as F
from .common import *
class ListModule(nn.Module):
def __init__(self, *args):
super(ListModule, self).__init__()
idx = 0
for module in args:
self.add_module(str(idx), module)
id... | 7,408 | 36.045 | 164 | py |
SelfDeblur | SelfDeblur-master/models/__init__.py | from .skip import skip
from .texture_nets import get_texture_nets
from .resnet import ResNet
from .unet import UNet
import torch.nn as nn
def get_net(input_depth, NET_TYPE, pad, upsample_mode, n_channels=3, act_fun='LeakyReLU', skip_n33d=128, skip_n33u=128, skip_n11=4, num_scales=5, downsample_mode='stride'):
if ... | 1,639 | 50.25 | 172 | py |
SelfDeblur | SelfDeblur-master/models/non_local_gaussian.py | import torch
from torch import nn
from torch.nn import functional as F
class _NonLocalBlockND(nn.Module):
def __init__(self, in_channels, inter_channels=None, dimension=3, sub_sample=True, bn_layer=True):
super(_NonLocalBlockND, self).__init__()
assert dimension in [1, 2, 3]
self.dimensi... | 4,674 | 33.124088 | 102 | py |
SelfDeblur | SelfDeblur-master/utils/common_utils.py | import torch
import torch.nn as nn
import torchvision
import sys
import cv2
import numpy as np
from PIL import Image
import PIL
import numpy as np
import matplotlib.pyplot as plt
import random
def crop_image(img, d=32):
'''Make dimensions divisible by `d`'''
imgsize = img.shape
new_size = (imgsize[0] - ... | 8,824 | 28.915254 | 138 | py |
e3_diffusion_for_molecules | e3_diffusion_for_molecules-main/eval_analyze.py | # Rdkit import should be first, do not move it
try:
from rdkit import Chem
except ModuleNotFoundError:
pass
import utils
import argparse
from qm9 import dataset
from qm9.models import get_model
import os
from equivariant_diffusion.utils import assert_mean_zero_with_mask, remove_mean_with_mask,\
assert_corre... | 7,795 | 38.175879 | 120 | py |
e3_diffusion_for_molecules | e3_diffusion_for_molecules-main/analyse_geom.py | from rdkit import Chem
import os
import numpy as np
import torch
from torch.utils.data import BatchSampler, DataLoader, Dataset, SequentialSampler
import argparse
import collections
import pickle
import os
import json
from tqdm import tqdm
from IPython.display import display
from matplotlib import pyplot as plt
import ... | 10,412 | 37.283088 | 125 | py |
e3_diffusion_for_molecules | e3_diffusion_for_molecules-main/setup.py | from setuptools import setup, find_packages
setup(
name='EN_diffusion',
version='1.0.0',
url=None,
author='Author Name',
author_email='[email protected]',
description='Description of my package',
packages=find_packages(),
install_requires=['numpy >= 1.11.1', 'matplotlib >= 1.5.1']
) | 315 | 25.333333 | 63 | py |
e3_diffusion_for_molecules | e3_diffusion_for_molecules-main/utils.py | import numpy as np
import getpass
import os
import torch
# Folders
def create_folders(args):
try:
os.makedirs('outputs')
except OSError:
pass
try:
os.makedirs('outputs/' + args.exp_name)
except OSError:
pass
# Model checkpoints
def save_model(model, path):
torch.s... | 4,012 | 25.058442 | 74 | py |
e3_diffusion_for_molecules | e3_diffusion_for_molecules-main/build_geom_dataset.py | import msgpack
import os
import numpy as np
import torch
from torch.utils.data import BatchSampler, DataLoader, Dataset, SequentialSampler
import argparse
from qm9.data import collate as qm9_collate
def extract_conformers(args):
drugs_file = os.path.join(args.data_dir, args.data_file)
save_file = f"geom_drugs... | 9,281 | 36.885714 | 101 | py |
e3_diffusion_for_molecules | e3_diffusion_for_molecules-main/main_geom_drugs.py | # Rdkit import should be first, do not move it
try:
from rdkit import Chem
except ModuleNotFoundError:
pass
import build_geom_dataset
from configs.datasets_config import geom_with_h
import copy
import utils
import argparse
import wandb
from os.path import join
from qm9.models import get_optim, get_model
from eq... | 12,752 | 44.384342 | 138 | py |
e3_diffusion_for_molecules | e3_diffusion_for_molecules-main/eval_conditional_qm9.py | import argparse
from os.path import join
import torch
import pickle
from qm9.models import get_model
from configs.datasets_config import get_dataset_info
from qm9 import dataset
from qm9.utils import compute_mean_mad
from qm9.sampling import sample
from qm9.property_prediction.main_qm9_prop import test
from qm9.propert... | 10,394 | 43.613734 | 125 | py |
e3_diffusion_for_molecules | e3_diffusion_for_molecules-main/eval_sample.py | # Rdkit import should be first, do not move it
try:
from rdkit import Chem
except ModuleNotFoundError:
pass
import utils
import argparse
from configs.datasets_config import qm9_with_h, qm9_without_h
from qm9 import dataset
from qm9.models import get_model
from equivariant_diffusion.utils import assert_correct... | 5,606 | 32.981818 | 90 | py |
e3_diffusion_for_molecules | e3_diffusion_for_molecules-main/main_qm9.py | # Rdkit import should be first, do not move it
try:
from rdkit import Chem
except ModuleNotFoundError:
pass
import copy
import utils
import argparse
import wandb
from configs.datasets_config import get_dataset_info
from os.path import join
from qm9 import dataset
from qm9.models import get_optim, get_model
from... | 13,079 | 44.103448 | 118 | py |
e3_diffusion_for_molecules | e3_diffusion_for_molecules-main/train_test.py | import wandb
from equivariant_diffusion.utils import assert_mean_zero_with_mask, remove_mean_with_mask,\
assert_correctly_masked, sample_center_gravity_zero_gaussian_with_mask
import numpy as np
import qm9.visualizer as vis
from qm9.analyze import analyze_stability_for_molecules
from qm9.sampling import sample_chai... | 9,409 | 44.240385 | 117 | py |
e3_diffusion_for_molecules | e3_diffusion_for_molecules-main/configs/datasets_config.py |
qm9_with_h = {
'name': 'qm9',
'atom_encoder': {'H': 0, 'C': 1, 'N': 2, 'O': 3, 'F': 4},
'atom_decoder': ['H', 'C', 'N', 'O', 'F'],
'n_nodes': {22: 3393, 17: 13025, 23: 4848, 21: 9970, 19: 13832, 20: 9482, 16: 10644, 13: 3060,
15: 7796, 25: 1506, 18: 13364, 12: 1689, 11: 807, 24: 539, 1... | 10,128 | 64.348387 | 671 | py |
e3_diffusion_for_molecules | e3_diffusion_for_molecules-main/equivariant_diffusion/distributions.py | import torch
from equivariant_diffusion.utils import \
center_gravity_zero_gaussian_log_likelihood_with_mask, \
standard_gaussian_log_likelihood_with_mask, \
center_gravity_zero_gaussian_log_likelihood, \
sample_center_gravity_zero_gaussian_with_mask, \
sample_center_gravity_zero_gaussian, \
sam... | 1,865 | 31.172414 | 80 | py |
e3_diffusion_for_molecules | e3_diffusion_for_molecules-main/equivariant_diffusion/utils.py | import torch
import numpy as np
class EMA():
def __init__(self, beta):
super().__init__()
self.beta = beta
def update_model_average(self, ma_model, current_model):
for current_params, ma_params in zip(current_model.parameters(), ma_model.parameters()):
old_weight, up_weigh... | 4,243 | 29.099291 | 96 | py |
e3_diffusion_for_molecules | e3_diffusion_for_molecules-main/equivariant_diffusion/__init__.py | 0 | 0 | 0 | py | |
e3_diffusion_for_molecules | e3_diffusion_for_molecules-main/equivariant_diffusion/en_diffusion.py | from equivariant_diffusion import utils
import numpy as np
import math
import torch
from egnn import models
from torch.nn import functional as F
from equivariant_diffusion import utils as diffusion_utils
# Defining some useful util functions.
def expm1(x: torch.Tensor) -> torch.Tensor:
return torch.expm1(x)
def... | 48,360 | 38.510621 | 178 | py |
e3_diffusion_for_molecules | e3_diffusion_for_molecules-main/qm9/losses.py | import torch
def sum_except_batch(x):
return x.view(x.size(0), -1).sum(dim=-1)
def assert_correctly_masked(variable, node_mask):
assert (variable * (1 - node_mask)).abs().sum().item() < 1e-8
def compute_loss_and_nll(args, generative_model, nodes_dist, x, h, node_mask, edge_mask, context):
bs, n_nodes,... | 1,067 | 25.04878 | 98 | py |
e3_diffusion_for_molecules | e3_diffusion_for_molecules-main/qm9/rdkit_functions.py | from rdkit import Chem
import numpy as np
from qm9.bond_analyze import get_bond_order, geom_predictor
from . import dataset
import torch
from configs.datasets_config import get_dataset_info
import pickle
import os
def compute_qm9_smiles(dataset_name, remove_h):
'''
:param dataset_name: qm9 or qm9_second_half... | 7,154 | 35.136364 | 134 | py |
e3_diffusion_for_molecules | e3_diffusion_for_molecules-main/qm9/utils.py | import torch
def compute_mean_mad(dataloaders, properties, dataset_name):
if dataset_name == 'qm9':
return compute_mean_mad_from_dataloader(dataloaders['train'], properties)
elif dataset_name == 'qm9_second_half' or dataset_name == 'qm9_second_half':
return compute_mean_mad_from_dataloader(dat... | 3,400 | 36.373626 | 92 | py |
e3_diffusion_for_molecules | e3_diffusion_for_molecules-main/qm9/dataset.py | from torch.utils.data import DataLoader
from qm9.data.args import init_argparse
from qm9.data.collate import PreprocessQM9
from qm9.data.utils import initialize_datasets
import os
def retrieve_dataloaders(cfg):
if 'qm9' in cfg.dataset:
batch_size = cfg.batch_size
num_workers = cfg.num_workers
... | 3,840 | 46.419753 | 127 | py |
e3_diffusion_for_molecules | e3_diffusion_for_molecules-main/qm9/sampling.py | import numpy as np
import torch
import torch.nn.functional as F
from equivariant_diffusion.utils import assert_mean_zero_with_mask, remove_mean_with_mask,\
assert_correctly_masked
from qm9.analyze import check_stability
def rotate_chain(z):
assert z.size(0) == 1
z_h = z[:, :, 3:]
n_steps = 30
th... | 6,002 | 34.105263 | 160 | py |
e3_diffusion_for_molecules | e3_diffusion_for_molecules-main/qm9/visualizer.py | import torch
import numpy as np
import os
import glob
import random
import matplotlib
import imageio
matplotlib.use('Agg')
import matplotlib.pyplot as plt
from qm9 import bond_analyze
##############
### Files ####
###########-->
def save_xyz_file(path, one_hot, charges, positions, dataset_info, id_from=0, name='mol... | 15,855 | 34.002208 | 154 | py |
e3_diffusion_for_molecules | e3_diffusion_for_molecules-main/qm9/bond_analyze.py | # Bond lengths from:
# http://www.wiredchemist.com/chemistry/data/bond_energies_lengths.html
# And:
# http://chemistry-reference.com/tables/Bond%20Lengths%20and%20Enthalpies.pdf
bonds1 = {'H': {'H': 74, 'C': 109, 'N': 101, 'O': 96, 'F': 92,
'B': 119, 'Si': 148, 'P': 144, 'As': 152, 'S': 134,
... | 9,764 | 30.5 | 79 | py |
e3_diffusion_for_molecules | e3_diffusion_for_molecules-main/qm9/models.py | import torch
from torch.distributions.categorical import Categorical
import numpy as np
from egnn.models import EGNN_dynamics_QM9
from equivariant_diffusion.en_diffusion import EnVariationalDiffusion
def get_model(args, device, dataset_info, dataloader_train):
histogram = dataset_info['n_nodes']
in_node_nf ... | 6,334 | 34 | 109 | py |
e3_diffusion_for_molecules | e3_diffusion_for_molecules-main/qm9/__init__.py | 0 | 0 | 0 | py | |
e3_diffusion_for_molecules | e3_diffusion_for_molecules-main/qm9/analyze.py | try:
from rdkit import Chem
from qm9.rdkit_functions import BasicMolecularMetrics
use_rdkit = True
except ModuleNotFoundError:
use_rdkit = False
import qm9.dataset as dataset
import torch
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
import numpy as np
import scipy.stats as sp_... | 13,305 | 32.686076 | 594 | py |
e3_diffusion_for_molecules | e3_diffusion_for_molecules-main/qm9/property_prediction/main_qm9_prop.py | import sys, os
sys.path.append(os.path.abspath(os.path.join('../../')))
from qm9.property_prediction.models_property import EGNN, Naive, NumNodes
import torch
from torch import nn, optim
import argparse
from qm9.property_prediction import prop_utils
import json
from qm9 import dataset, utils
import pickle
loss_l1 = nn... | 10,043 | 44.654545 | 197 | py |
e3_diffusion_for_molecules | e3_diffusion_for_molecules-main/qm9/property_prediction/__init__.py | 0 | 0 | 0 | py | |
e3_diffusion_for_molecules | e3_diffusion_for_molecules-main/qm9/property_prediction/models_property.py | from .models.gcl import E_GCL, unsorted_segment_sum
import torch
from torch import nn
class E_GCL_mask(E_GCL):
"""Graph Neural Net with global state and fixed number of nodes per graph.
Args:
hidden_dim: Number of hidden units.
num_nodes: Maximum number of nodes (for self-attentive pooling... | 6,706 | 40.91875 | 233 | py |
e3_diffusion_for_molecules | e3_diffusion_for_molecules-main/qm9/property_prediction/prop_utils.py | import os
import matplotlib
matplotlib.use('Agg')
import torch
import matplotlib.pyplot as plt
def create_folders(args):
try:
os.makedirs(args.outf)
except OSError:
pass
try:
os.makedirs(args.outf + '/' + args.exp_name)
except OSError:
pass
try:
os.makedirs... | 3,051 | 28.346154 | 128 | py |
e3_diffusion_for_molecules | e3_diffusion_for_molecules-main/qm9/property_prediction/models/gcl.py | from torch import nn
import torch
class MLP(nn.Module):
""" a simple 4-layer MLP """
def __init__(self, nin, nout, nh):
super().__init__()
self.net = nn.Sequential(
nn.Linear(nin, nh),
nn.LeakyReLU(0.2),
nn.Linear(nh, nh),
nn.LeakyReLU(0.2),
... | 12,996 | 36.02849 | 230 | py |
e3_diffusion_for_molecules | e3_diffusion_for_molecules-main/qm9/property_prediction/models/__init__.py | from .gcl import GCL
| 21 | 10 | 20 | py |
e3_diffusion_for_molecules | e3_diffusion_for_molecules-main/qm9/data/args.py | import argparse
from math import inf
#### Argument parser ####
def setup_shared_args(parser):
"""
Sets up the argparse object for the qm9 dataset
Parameters
----------
parser : :class:`argparse.ArgumentParser`
Argument Parser with arguments.
Parameters
----------
p... | 14,008 | 46.488136 | 137 | py |
e3_diffusion_for_molecules | e3_diffusion_for_molecules-main/qm9/data/utils.py | import torch
import numpy as np
import logging
import os
from torch.utils.data import DataLoader
from qm9.data.dataset_class import ProcessedDataset
from qm9.data.prepare import prepare_dataset
def initialize_datasets(args, datadir, dataset, subset=None, splits=None,
force_download=False, su... | 7,481 | 39.443243 | 136 | py |
e3_diffusion_for_molecules | e3_diffusion_for_molecules-main/qm9/data/collate.py | import torch
def batch_stack(props):
"""
Stack a list of torch.tensors so they are padded to the size of the
largest tensor along each axis.
Parameters
----------
props : list of Pytorch Tensors
Pytorch tensors to stack
Returns
-------
props : Pytorch tensor
Stack... | 2,718 | 25.144231 | 103 | py |
e3_diffusion_for_molecules | e3_diffusion_for_molecules-main/qm9/data/__init__.py | from qm9.data.utils import initialize_datasets
from qm9.data.collate import PreprocessQM9
from qm9.data.dataset_class import ProcessedDataset | 141 | 46.333333 | 51 | py |
e3_diffusion_for_molecules | e3_diffusion_for_molecules-main/qm9/data/dataset_class.py | import torch
from torch.utils.data import Dataset
import os
from itertools import islice
from math import inf
import logging
class ProcessedDataset(Dataset):
"""
Data structure for a pre-processed cormorant dataset. Extends PyTorch Dataset.
Parameters
----------
data : dict
Dictionary o... | 3,510 | 36.351064 | 175 | py |
e3_diffusion_for_molecules | e3_diffusion_for_molecules-main/qm9/data/prepare/md17.py | from os.path import join as join
import urllib.request
import numpy as np
import torch
import logging, os, urllib
from qm9.data.prepare.utils import download_data, is_int, cleanup_file
md17_base_url = 'http://quantum-machine.org/gdml/data/npz/'
md17_subsets = {'benzene': 'benzene_old_dft',
'uracil':... | 3,992 | 34.972973 | 156 | py |
e3_diffusion_for_molecules | e3_diffusion_for_molecules-main/qm9/data/prepare/qm9.py | import numpy as np
import torch
import logging
import os
import urllib
from os.path import join as join
import urllib.request
from qm9.data.prepare.process import process_xyz_files, process_xyz_gdb9
from qm9.data.prepare.utils import download_data, is_int, cleanup_file
def download_dataset_qm9(datadir, dataname, s... | 8,060 | 34.355263 | 114 | py |
e3_diffusion_for_molecules | e3_diffusion_for_molecules-main/qm9/data/prepare/download.py | import logging
import os
from qm9.data.prepare.md17 import download_dataset_md17
from qm9.data.prepare.qm9 import download_dataset_qm9
def prepare_dataset(datadir, dataset, subset=None, splits=None, cleanup=True, force_download=False):
"""
Download and process dataset.
Parameters
----------
data... | 2,990 | 35.925926 | 137 | py |
e3_diffusion_for_molecules | e3_diffusion_for_molecules-main/qm9/data/prepare/utils.py | import os, logging
from urllib.request import urlopen
def download_data(url, outfile='', binary=False):
"""
Downloads data from a URL and returns raw data.
Parameters
----------
url : str
URL to get the data from
outfile : str, optional
Where to save the data.
binary : boo... | 1,480 | 23.278689 | 80 | py |
e3_diffusion_for_molecules | e3_diffusion_for_molecules-main/qm9/data/prepare/__init__.py | from qm9.data.prepare.download import *
from qm9.data.prepare.process import *
from qm9.data.prepare.qm9 import *
from qm9.data.prepare.md17 import *
| 150 | 29.2 | 39 | py |
e3_diffusion_for_molecules | e3_diffusion_for_molecules-main/qm9/data/prepare/process.py | import logging
import os
import torch
import tarfile
from torch.nn.utils.rnn import pad_sequence
charge_dict = {'H': 1, 'C': 6, 'N': 7, 'O': 8, 'F': 9}
def split_dataset(data, split_idxs):
"""
Splits a dataset according to the indices given.
Parameters
----------
data : dict
Dictionary t... | 6,929 | 33.137931 | 138 | py |
e3_diffusion_for_molecules | e3_diffusion_for_molecules-main/generated_samples/gschnet/__init__.py | 0 | 0 | 0 | py | |
e3_diffusion_for_molecules | e3_diffusion_for_molecules-main/generated_samples/gschnet/analyze_gschnet.py | # Rdkit import should be first, do not move it
try:
from rdkit import Chem
except ModuleNotFoundError:
pass
import pickle
import torch.nn.functional as F
from qm9.analyze import analyze_stability_for_molecules
import numpy as np
import torch
def flatten_sample_dictionary(samples):
results = {'one_hot': [... | 1,647 | 29.518519 | 96 | py |
e3_diffusion_for_molecules | e3_diffusion_for_molecules-main/egnn/egnn_new.py | from torch import nn
import torch
import math
class GCL(nn.Module):
def __init__(self, input_nf, output_nf, hidden_nf, normalization_factor, aggregation_method,
edges_in_d=0, nodes_att_dim=0, act_fn=nn.SiLU(), attention=False):
super(GCL, self).__init__()
input_edge = input_nf * 2
... | 12,294 | 43.709091 | 131 | py |
e3_diffusion_for_molecules | e3_diffusion_for_molecules-main/egnn/egnn.py | import torch
from torch import Tensor
from torch import nn
import torch.nn.functional as F
class E_GCL(nn.Module):
"""Graph Neural Net with global state and fixed number of nodes per graph.
Args:
hidden_dim: Number of hidden units.
num_nodes: Maximum number of nodes (for self-attentive poo... | 14,826 | 41.976812 | 264 | py |
e3_diffusion_for_molecules | e3_diffusion_for_molecules-main/egnn/models.py | import torch
import torch.nn as nn
from egnn.egnn_new import EGNN, GNN
from equivariant_diffusion.utils import remove_mean, remove_mean_with_mask
import numpy as np
class EGNN_dynamics_QM9(nn.Module):
def __init__(self, in_node_nf, context_node_nf,
n_dims, hidden_nf=64, device='cpu',
... | 5,555 | 40.155556 | 107 | py |
cpuinfo | cpuinfo-main/configure.py | #!/usr/bin/env python
import confu
parser = confu.standard_parser("cpuinfo configuration script")
parser.add_argument("--log", dest="log_level",
choices=("none", "fatal", "error", "warning", "info", "debug"), default="error")
parser.add_argument("--mock", dest="mock", action="store_true")
def main(args):
op... | 4,374 | 40.666667 | 127 | py |
cpuinfo | cpuinfo-main/deps/clog/configure.py | #!/usr/bin/env python
import confu
parser = confu.standard_parser("clog configuration script")
def main(args):
options = parser.parse_args(args)
build = confu.Build.from_options(options)
build.export_cpath("include", ["clog.h"])
with build.options(source_dir="src", extra_include_dirs="src"):
... | 670 | 23.851852 | 67 | py |
cpuinfo | cpuinfo-main/scripts/arm-linux-filesystem-dump.py | #!/usr/bin/env python
import os
import sys
import argparse
import shutil
parser = argparse.ArgumentParser(description='Android system files extractor')
parser.add_argument("-p", "--prefix", metavar="NAME", required=True,
help="Prefix for stored files, e.g. galaxy-s7-us")
SYSTEM_FILES = [
"/... | 3,213 | 28.759259 | 100 | py |
cpuinfo | cpuinfo-main/scripts/parse-x86-cpuid-dump.py | #!/usr/bin/env python
from __future__ import print_function
import argparse
import sys
import re
parser = argparse.ArgumentParser(description='x86 CPUID dump parser')
parser.add_argument("input", metavar="INPUT", nargs=1,
help="Path to CPUID dump log")
def main(args):
options = parser.pars... | 1,504 | 30.354167 | 106 | py |
cpuinfo | cpuinfo-main/scripts/android-device-dump.py | #!/usr/bin/env python
import os
import sys
import string
import argparse
import subprocess
import tempfile
root_dir = os.path.abspath(os.path.dirname(__file__))
parser = argparse.ArgumentParser(description='Android system files extractor')
parser.add_argument("-p", "--prefix", metavar="NAME", required=True,
... | 15,626 | 37.970075 | 117 | py |
infinispan | infinispan-main/documentation/src/main/asciidoc/topics/python/monitor_site_status.py | #!/usr/bin/python3
import time
import requests
from requests.auth import HTTPDigestAuth
class InfinispanConnection:
def __init__(self, server: str = 'http://localhost:11222', cache_manager: str = 'default',
auth: tuple = ('admin', 'change_me')) -> None:
super().__init__()
self.__... | 3,065 | 28.76699 | 119 | py |
infinispan | infinispan-main/documentation/src/main/asciidoc/topics/code_examples/rest_client.py | import urllib.request
# Setup basic auth
base_uri = 'http://localhost:11222/rest/v2/caches/default'
auth_handler = urllib.request.HTTPBasicAuthHandler()
auth_handler.add_password(user='user', passwd='pass', realm='ApplicationRealm', uri=base_uri)
opener = urllib.request.build_opener(auth_handler)
urllib.request.instal... | 731 | 29.5 | 93 | py |
infinispan | infinispan-main/bin/diff_test_lists.py | #!/usr/bin/python
"""
Merge the results of the find_unstable_tests.py, find_unstable_tests_jira.py, and find_unstable_tests_teamcity.py
"""
import argparse
import csv
import os
from pprint import pprint
def parse_tsv(annotations_file, testNameReplacement, verbose):
tests = dict()
with open(annotations_file, '... | 3,301 | 36.101124 | 136 | py |
infinispan | infinispan-main/bin/greplog.py | #!/usr/bin/python
from __future__ import print_function
import argparse
import fileinput
import re
import sys
def handleMessage(message, filter):
if filter.search(message):
print(message, end='')
def main():
parser = argparse.ArgumentParser("Filter logs")
parser.add_argument('pattern', nargs=1,
... | 1,040 | 22.133333 | 76 | py |
infinispan | infinispan-main/bin/find_disabled_tests.py | #!/usr/bin/python
import re
import time
import sys
from utils import *
def main():
start_time = time.clock()
disabled_test_files = []
test_annotation_matcher = re.compile('^\s*@Test')
disabled_matcher = re.compile('enabled\s*=\s*false')
for test_file in GlobDirectoryWalker(get_search_path(sys.argv[0]),... | 972 | 23.948718 | 83 | py |
infinispan | infinispan-main/bin/clean_logs.py | #!/usr/bin/python
from __future__ import with_statement
import re
import subprocess
import os
import sys
VIEW_TO_USE = '3'
INPUT_FILE = "infinispan.log"
OUTPUT_FILE = "infinispan0.log"
addresses = {}
new_addresses = {}
def find(filename, expr):
with open(filename) as f:
for l in f:
if expr.match(l):
... | 3,285 | 27.08547 | 200 | py |
infinispan | infinispan-main/bin/find_unstable_tests_jira.py | #!/usr/bin/python
"""
Search JIRA using the restkit library (yum install python-restkit).
JIRA REST API documentation: https://docs.atlassian.com/jira/REST/5.0-m5
"""
import json
import re
from restkit import Resource, BasicAuth, request
from pprint import pprint
import argparse
from getpass import getpass
impor... | 2,435 | 29.45 | 244 | py |
infinispan | infinispan-main/bin/utils.py | import os
import fnmatch
import re
import subprocess
import sys
import readline
import shutil
import random
settings_file = '%s/.infinispan_dev_settings' % os.getenv('HOME')
upstream_url = '[email protected]:infinispan/infinispan.git'
### Known config keys
local_mvn_repo_dir_key = "local_mvn_repo_dir"
maven_pom_xml_namesp... | 12,812 | 29.65311 | 155 | py |
infinispan | infinispan-main/bin/find_unstable_tests_teamcity.py | #!/usr/bin/python
"""
Search JIRA using the restkit library (yum install python-restkit).
Teamcity REST API documentation: http://confluence.jetbrains.com/display/TCD8/REST+API
"""
import json
import re
from restkit import Resource, BasicAuth, request
from pprint import pprint
import argparse
import datetime
fro... | 4,221 | 33.892562 | 132 | py |
infinispan | infinispan-main/bin/find_broken_links.py | #!/usr/bin/python3
import re
import os
from urllib.request import Request, urlopen
"""
Finds broken links in documentation. Takes ~13 minutes. Run from root infinispan directory.
"""
rootDir = 'documentation/target/generated-docs/'
def isBad(url):
req = Request(url, headers={'User-Agent': 'Mozilla/5.0 Chrome... | 1,237 | 27.136364 | 95 | py |
infinispan | infinispan-main/bin/report_thread_leaks.py | #!/usr/bin/python3
import fileinput
import re
import sys
# Usage:
# * Add a breakpoint in Thread.start()
# * Action: new RuntimeException(String.format("Thread %s started thread %s", Thread.currentThread().getName(), name)).printStackTrace()
# * Condition: name.startsWith("<thread name prefix reported as thread leak>... | 2,713 | 28.5 | 136 | py |
infinispan | infinispan-main/bin/list_command_ids.py | #!/usr/bin/python
import re
import sys
from utils import *
command_file_name = re.compile('([a-zA-Z0-9/]*Command.java)')
def trim_name(nm):
res = command_file_name.search(nm)
if res:
return res.group(1)
else:
return nm
def get_next(ids_used):
# Cannot assume a command ID greater than the size of 1 b... | 1,762 | 23.830986 | 164 | py |
infinispan | infinispan-main/bin/find_unstable_tests.py | #!/usr/bin/python
import re
import time
import sys
import csv
import argparse
import os.path
import fnmatch
def main(args):
base_dir = args.dir
annotated_test_files = []
disabled_test_matcher = re.compile('\s*@Test.*groups\s*=\s*("unstable|Array\("unstable"\))|@Category\(UnstableTest\.class\).*')
filename... | 1,302 | 26.145833 | 129 | py |
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