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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MaskSpec | MaskSpec-main/scv2/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,
... | 4,376 | 38.432432 | 114 | py |
MaskSpec | MaskSpec-main/scv2/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... | 8,068 | 34.390351 | 158 | py |
MaskSpec | MaskSpec-main/scv2/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... | 18,310 | 44.324257 | 163 | py |
MaskSpec | MaskSpec-main/utils/pos_embed.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.
# --------------------------------------------------------
# Position embedding utils
# -----------------------------------... | 4,069 | 41.395833 | 107 | py |
MaskSpec | MaskSpec-main/utils/misc.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
#... | 11,644 | 32.851744 | 128 | py |
MaskSpec | MaskSpec-main/dcase18/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... | 2,397 | 33.753623 | 93 | py |
MaskSpec | MaskSpec-main/dcase18/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,
... | 4,376 | 38.432432 | 114 | py |
MaskSpec | MaskSpec-main/dcase18/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... | 8,101 | 34.535088 | 158 | py |
MaskSpec | MaskSpec-main/dcase18/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... | 18,349 | 44.420792 | 163 | py |
MaskSpec | MaskSpec-main/esc50/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 esc50.dataset import decode_mp3, pad_or_truncate
from models.models_mae import AugmentMelSTFT
def get_mean_std(n_mel=128):
print('Start...')
hdf5_file1 = './esc50... | 4,655 | 35.093023 | 91 | py |
MaskSpec | MaskSpec-main/esc50/engine_run.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
from timm.utils import accuracy
import ... | 4,464 | 38.166667 | 114 | py |
MaskSpec | MaskSpec-main/esc50/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 ... | 7,960 | 34.070485 | 162 | py |
MaskSpec | MaskSpec-main/esc50/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... | 17,124 | 43.829843 | 163 | py |
SMUG | SMUG-main/test.py | import torch
import torch.nn as nn
import numpy as np
from models.didn import DIDN
from models import networks
from util.metrics import PSNR
import pytorch_msssim
import global_network_dataset
import matplotlib.pyplot as plt
from options.test_options import TestOptions
opt = TestOptions().parse()
opt.batchSize = 1
d... | 9,874 | 41.200855 | 195 | py |
SMUG | SMUG-main/global_network_dataset.py | import numpy as np
from typing import Dict, Optional, Sequence, Tuple, Union
import math
import sigpy as sp
import sigpy.mri as mr
import sigpy.plot as pl
import scipy.io as sio
# from ismrmrdtools import show, transform
# import ReadWrapper
from torch.utils.data.dataset import Dataset
from torch.nn import init
import... | 5,437 | 44.316667 | 193 | py |
SMUG | SMUG-main/pretrain_denoiser.py | import torch
import torch.nn as nn
import numpy as np
from models.didn import DIDN
from util.metrics import PSNR
import pytorch_msssim
import global_network_dataset
from util.util import init_weights
from tqdm import tqdm
import os
from options.train_options import TrainOptions
opt = TrainOptions().parse()
device = t... | 4,349 | 36.179487 | 136 | py |
SMUG | SMUG-main/test_acceleration.py | import torch
import torch.nn as nn
import numpy as np
from models.didn import DIDN
from models import networks
from util.metrics import PSNR
import pytorch_msssim
import global_network_dataset2
import matplotlib.pyplot as plt
from options.test_options import TestOptions
opt = TestOptions().parse()
opt.batchSize = 1
... | 5,198 | 41.966942 | 195 | py |
SMUG | SMUG-main/train_SMUG.py | import torch
import torch.nn as nn
import numpy as np
from models.didn import DIDN
from models import networks
from util.metrics import PSNR
import pytorch_msssim
import global_network_dataset
from tqdm import tqdm
import os
from options.tune_options import TuneOptions
opt = TuneOptions().parse()
opt.smoothing = 'SMUG... | 8,873 | 41.056872 | 214 | py |
SMUG | SMUG-main/train_vanilla_MoDL.py | import torch
import torch.nn as nn
import numpy as np
from models.didn import DIDN
from models import networks
from util.metrics import PSNR
import pytorch_msssim
import global_network_dataset
from util.util import init_weights
from tqdm import tqdm
import os
from options.train_options import TrainOptions
opt = TrainO... | 5,389 | 37.776978 | 136 | py |
SMUG | SMUG-main/train_SMUGv0.py | import torch
import torch.nn as nn
import numpy as np
from models.didn import DIDN
from models import networks
from util.metrics import PSNR
import pytorch_msssim
import global_network_dataset
from tqdm import tqdm
import os
from options.tune_options import TuneOptions
opt = TuneOptions().parse()
opt.smoothing = 'SMUG... | 6,569 | 39.807453 | 195 | py |
SMUG | SMUG-main/global_network_dataset2.py | import numpy as np
from typing import Dict, Optional, Sequence, Tuple, Union
import math
import sigpy as sp
import sigpy.mri as mr
import sigpy.plot as pl
import scipy.io as sio
# from ismrmrdtools import show, transform
# import ReadWrapper
from torch.utils.data.dataset import Dataset
from torch.nn import init
import... | 6,322 | 43.528169 | 193 | py |
SMUG | SMUG-main/train_RSE2E.py | import torch
import torch.nn as nn
import numpy as np
from models.didn import DIDN
from models import networks
from util.metrics import PSNR
import pytorch_msssim
import global_network_dataset
from tqdm import tqdm
import os
from options.tune_options import TuneOptions
opt = TuneOptions().parse()
opt.smoothing = 'RSE2... | 7,025 | 40.821429 | 214 | py |
SMUG | SMUG-main/test_unrolling_step.py | import torch
import torch.nn as nn
import numpy as np
from models.didn import DIDN
from models import networks
from util.metrics import PSNR
import pytorch_msssim
import global_network_dataset
import matplotlib.pyplot as plt
from options.test_options import TestOptions
opt = TestOptions().parse()
opt.batchSize = 1
d... | 8,194 | 42.823529 | 195 | py |
SMUG | SMUG-main/options/base_options.py | import argparse
import os
from util import util
import torch
import models
import data
class BaseOptions():
def __init__(self):
self.initialized = False
def initialize(self, parser):
parser.add_argument('--dataroot', required=True, help='path to images (should have subfolders trainA, trainB, ... | 3,320 | 34.709677 | 136 | py |
SMUG | SMUG-main/models/networks.py | import torch
import torch.nn as nn
from torch.nn import init
import functools
import torch.nn.functional as F
from torch.optim import lr_scheduler
import numpy as np
#from util.simulation import simu_Affine2D, simu_noise2D
from util.metrics import roll_2
from util.util import ifft2, fft2, fftshift, ifftshift
import tim... | 28,938 | 39.081717 | 120 | py |
SMUG | SMUG-main/models/didn.py | """
Copyright (c) 2019 Imperial College London.
This source code is licensed under the MIT license found in the
LICENSE file in the root directory of this source tree.
"""
import torch
import torch.nn as nn
import numpy as np
import torch.nn.functional as F
class _Residual_Block(nn.Module):
def __init__(self, n... | 9,406 | 34.632576 | 144 | py |
SMUG | SMUG-main/util/image_pool.py | import random
import torch
class ImagePool():
def __init__(self, pool_size):
self.pool_size = pool_size
if self.pool_size > 0:
self.num_imgs = 0
self.images = []
def query(self, images):
if self.pool_size == 0:
return images
return_images = ... | 1,072 | 31.515152 | 93 | py |
SMUG | SMUG-main/util/util.py | from __future__ import print_function
import torch
from torch.nn import init
import numpy as np
from PIL import Image
import os
from util.fft_compatibility import fft_new, ifft_new
from scipy.interpolate import griddata
# Much of the code is from Sigma-net project (https://github.com/khammernik/sigmanet)
# and fastMRI... | 17,275 | 33.621242 | 146 | py |
SMUG | SMUG-main/util/metrics.py | import torch
import torch.nn.functional as F
from torch.autograd import Variable
import numpy as np
from math import exp
import math
from . import util
from util.util import absolute
from skimage.metrics import structural_similarity as ss
from scipy import ndimage
def gaussian(window_size, sigma,device):
gauss = torch... | 2,783 | 28.617021 | 121 | py |
SMUG | SMUG-main/util/fft_compatibility.py | import torch
from torch import Tensor
from packaging import version
# This code is from Matthew Muckley's TorchKBNufft: https://github.com/mmuckley/torchkbnufft
if version.parse(torch.__version__) >= version.parse("1.7.0"):
import torch.fft # type: ignore
def fft_old(image: Tensor, ndim: int, normalized: bool ... | 1,184 | 27.214286 | 92 | py |
SMUG | SMUG-main/util/hfen.py | """
Copyright (c) 2019 Imperial College London.
This source code is licensed under the MIT license found in the
LICENSE file in the root directory of this source tree.
"""
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... | 2,770 | 33.6375 | 103 | py |
SMUG | SMUG-main/data/.py | import torch.utils.data as data
from PIL import Image
import torchvision.transforms as transforms
class BaseDataset(data.Dataset):
def __init__(self):
super(BaseDataset, self).__init__()
def name(self):
return 'BaseDataset'
@staticmethod
def modify_commandline_options(parser, is_trai... | 3,421 | 31.903846 | 91 | py |
SMUG | SMUG-main/data/base_dataset.py | import torch.utils.data as data
from PIL import Image
import torchvision.transforms as transforms
class BaseDataset(data.Dataset):
def __init__(self):
super(BaseDataset, self).__init__()
def name(self):
return 'BaseDataset'
@staticmethod
def modify_commandline_options(parser, is_trai... | 3,421 | 31.903846 | 91 | py |
SMUG | SMUG-main/data/image_folder.py | ###############################################################################
# Code from
# https://github.com/pytorch/vision/blob/master/torchvision/datasets/folder.py
# Modified the original code so that it also loads images from the current
# directory as well as the subdirectories
################################... | 2,027 | 29.268657 | 100 | py |
SMUG | SMUG-main/data/__init__.py | import importlib
import torch.utils.data
from data.base_data_loader import BaseDataLoader
from data.base_dataset import BaseDataset
from torch.utils.data.sampler import Sampler
import random
def find_dataset_using_name(dataset_name):
# Given the option --dataset_mode [datasetname],
# the file "data/datasetname... | 4,720 | 34.765152 | 156 | py |
word-class-embeddings | word-class-embeddings-master/src/main.py | import argparse
from scipy.sparse import csr_matrix
from sklearn.model_selection import train_test_split
import scipy
from embedding.supervised import get_supervised_embeddings, STWFUNCTIONS
from model.classification import NeuralClassifier
from util.early_stop import EarlyStopping
from util.common import *
from data.d... | 19,837 | 48.719298 | 153 | py |
word-class-embeddings | word-class-embeddings-master/src/fasttext.py | import argparse
from sklearn.model_selection import train_test_split
from data.dataset import *
from embedding.pretrained import GloVe
from embedding.supervised import get_supervised_embeddings
from util.csv_log import CSVLog
from util.file import create_if_not_exist
from util.metrics import *
from time import time
fro... | 9,819 | 44.674419 | 167 | py |
word-class-embeddings | word-class-embeddings-master/src/main_bert.py | import argparse
from scipy.sparse import csr_matrix
from sklearn.model_selection import train_test_split
import scipy
from embedding.supervised import get_supervised_embeddings
from main import init_logfile, init_optimizer, init_loss
from model.classification import NeuralClassifier, Token2BertEmbeddings, Token2WCEmbed... | 14,970 | 46.526984 | 138 | py |
word-class-embeddings | word-class-embeddings-master/src/embedding/pretrained.py | from abc import ABC, abstractmethod
import torch, torchtext
import gensim
import os
import numpy as np
AVAILABLE_PRETRAINED = ['glove', 'word2vec', 'fasttext']
class PretrainedEmbeddings(ABC):
def __init__(self):
super().__init__()
@abstractmethod
def vocabulary(self): pass
@abstractmethod... | 2,960 | 30.168421 | 102 | py |
word-class-embeddings | word-class-embeddings-master/src/util/common.py | import warnings
warnings.filterwarnings("ignore", category=DeprecationWarning)
import numpy as np
from tqdm import tqdm
import torch
from scipy.sparse import vstack, issparse
from joblib import Parallel, delayed
import multiprocessing
import itertools
def index(data, vocab, known_words, analyzer, unk_index, out_of_vo... | 5,632 | 37.582192 | 122 | py |
word-class-embeddings | word-class-embeddings-master/src/util/early_stop.py | #adapted from https://github.com/Bjarten/early-stopping-pytorch/blob/master/pytorchtools.py
import torch
from time import time
from util.file import create_if_not_exist
class EarlyStopping:
def __init__(self, model, patience=20, verbose=True, checkpoint='./checkpoint.pt'):
# set patience to 0 or -1 to av... | 1,853 | 32.709091 | 115 | py |
word-class-embeddings | word-class-embeddings-master/src/model/classification.py | from model.layers import *
from transformers import BertModel, BertTokenizer
import logging
logging.basicConfig(level=logging.INFO)
class NeuralClassifier(nn.Module):
ALLOWED_NETS = {'cnn', 'lstm', 'attn'}
def __init__(self,
net_type,
output_size,
hidden_siz... | 5,988 | 37.63871 | 121 | py |
word-class-embeddings | word-class-embeddings-master/src/model/CustomRepresentationLearning.py | import numpy as np
import torch
from simpletransformers.language_representation import RepresentationModel
from simpletransformers.language_representation.representation_model import batch_iterable, mean_across_all_tokens, \
concat_all_tokens
from tqdm import tqdm
class CustomRepresentationModel(RepresentationMo... | 2,729 | 49.555556 | 197 | py |
word-class-embeddings | word-class-embeddings-master/src/model/layers.py | import torch
import torch.nn as nn
from torch.nn import functional as F
from torch.autograd import Variable
class EmbeddingCustom(nn.Module):
def __init__(self,
vocab_size,
learnable_length,
pretrained=None,
drop_embedding_range=None,
... | 7,656 | 39.513228 | 126 | py |
word-class-embeddings | word-class-embeddings-master/src/model/embedding_predictor.py | import torch
import torch.nn as nn
from torch.nn import functional as F
import numpy as np
from util.early_stop import EarlyStopping
class EmbeddingPredictor(nn.Module):
def __init__(self, input_size, output_size, hiddensize=64):
super(EmbeddingPredictor, self).__init__()
self.lin1 = nn.Linear(in... | 3,019 | 35.385542 | 165 | py |
pyaer | pyaer-master/docs/autogen.py | # -*- coding: utf-8 -*-
'''
General documentation architecture:
'''
from __future__ import print_function
from __future__ import unicode_literals
import re
import inspect
import os
import shutil
import pyaer
from pyaer import device
from pyaer import dvs128
from pyaer import davis
from pyaer import edvs
from pyaer im... | 19,880 | 33.159794 | 85 | py |
MonoDEVSNet | MonoDEVSNet-main/monodevsnet_trainer.py | # Author: Akhil Gurram
# Build on top of the monodepth2
# (Automatically pulled from git repo, monodepth2 source code is not included in this repository)
# This is the training script of the MonoDEVSNet framework.
# MonoDEVSNet: Monocular Depth Estimation through Virtual-world Supervision and Real-world SfM Self-Superv... | 41,235 | 46.726852 | 127 | py |
MonoDEVSNet | MonoDEVSNet-main/evaluation.py | # Author: Akhil Gurram
# Build on top of the monodepth2
# (Automatically pulled from git repo, monodepth2 source code is not included in this repository)
# This is the training script of the MonoDEVSNet framework.
# MonoDEVSNet: Monocular Depth Estimation through Virtual-world Supervision and Real-world SfM Self-Superv... | 20,597 | 47.810427 | 120 | py |
MonoDEVSNet | MonoDEVSNet-main/networks/hrnet_module.py | # From the Authors of https://ieeexplore.ieee.org/document/9052469, https://github.com/HRNet/HRNet-Image-Classification
import os
import torch
import torch.nn as nn
import torch.nn.functional as F
relu_inplace = True
BatchNorm2d = BatchNorm2d_class = nn.BatchNorm2d
BN_MOMENTUM = 0.1
ALIGN_CORNERS = True
class Mod... | 24,803 | 41.913495 | 127 | py |
MonoDEVSNet | MonoDEVSNet-main/networks/domain_classifier.py | # Author: Akhil Gurram
# Build on top of the monodepth2
# (Automatically pulled from git repo, monodepth2 source code is not included in this repository)
# This is the training script of the MonoDEVSNet framework.
# MonoDEVSNet: Monocular Depth Estimation through Virtual-world Supervision and Real-world SfM Self-Superv... | 2,921 | 36.948052 | 111 | py |
MonoDEVSNet | MonoDEVSNet-main/networks/misc_layers.py | # Author: Akhil Gurram
# Build on top of the monodepth2
# (Automatically pulled from git repo, monodepth2 source code is not included in this repository)
# This is the training script of the MonoDEVSNet framework.
# MonoDEVSNet: Monocular Depth Estimation through Virtual-world Supervision and Real-world SfM Self-Superv... | 2,545 | 33.876712 | 111 | py |
MonoDEVSNet | MonoDEVSNet-main/networks/densenet_encoder.py | import re
from collections import OrderedDict
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.checkpoint as cp
try:
from torch.hub import load_state_dict_from_url
except ImportError:
from torch.utils.model_zoo import load_url as load_state_dict_from_url... | 18,436 | 41.286697 | 116 | py |
MonoDEVSNet | MonoDEVSNet-main/networks/encoders.py | # Author: Akhil Gurram
# Build on top of the monodepth2
# (Automatically pulled from git repo, monodepth2 source code is not included in this repository)
# This is the training script of the MonoDEVSNet framework.
# MonoDEVSNet: Monocular Depth Estimation through Virtual-world Supervision and Real-world SfM Self-Superv... | 6,432 | 43.986014 | 111 | py |
MonoDEVSNet | MonoDEVSNet-main/datasets_EXT/vk_dataset.py | # Author: Akhil Gurram
# Build on top of the monodepth2
# (Automatically pulled from git repo, monodepth2 source code is not included in this repository)
# This is the training script of the MonoDEVSNet framework.
# MonoDEVSNet: Monocular Depth Estimation through Virtual-world Supervision and Real-world SfM Self-Superv... | 18,191 | 40.065463 | 119 | py |
MonoDEVSNet | MonoDEVSNet-main/utils/load_eval_output.py | # Author: Akhil Gurram
# Build on top of the monodepth2
# (Automatically pulled from git repo, monodepth2 source code is not included in this repository)
# This is the training script of the MonoDEVSNet framework.
# MonoDEVSNet: Monocular Depth Estimation through Virtual-world Supervision and Real-world SfM Self-Superv... | 2,363 | 36.52381 | 111 | py |
qasper-led-baseline | qasper-led-baseline-main/scripts/sample_qasper_answers.py | import argparse
import sys
import os
import json
from tqdm import tqdm
import torch
from allennlp.models.archival import load_archive
from transformers import AutoTokenizer
sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
from qasper_baselines import model, dataset_reader
def main():
... | 3,957 | 45.023256 | 121 | py |
qasper-led-baseline | qasper-led-baseline-main/tests/qasper_baseline_test.py | from allennlp.common.testing import ModelTestCase
from allennlp.common.util import ensure_list
from allennlp.data import Vocabulary
import numpy
from numpy.testing import assert_almost_equal
import torch
import qasper_baselines.model # pylint: disable=unused-import
import qasper_baselines.dataset_reader # pylint: d... | 687 | 30.272727 | 88 | py |
qasper-led-baseline | qasper-led-baseline-main/qasper_baselines/model.py | from typing import Any, Dict, List
from overrides import overrides
from transformers import AutoConfig, AutoModelForSeq2SeqLM, AutoTokenizer
from transformers.models.led.modeling_led import shift_tokens_right
import torch
from allennlp.nn import util
from allennlp.data import TextFieldTensors, Vocabulary
from allennl... | 8,468 | 44.047872 | 112 | py |
qasper-led-baseline | qasper-led-baseline-main/qasper_baselines/dataset_reader.py | import json
import logging
import random
from enum import Enum
from collections import defaultdict
from typing import Any, Dict, List, Optional, Iterable, Tuple
from overrides import overrides
import spacy
import torch
from allennlp.common.util import JsonDict
from allennlp.data.fields import (
MetadataField,
... | 18,507 | 43.277512 | 117 | py |
lbsgd-rl | lbsgd-rl-main/lbsgd_rl/la_mbda.py | import numpy as np
import tensorflow as tf
from tensorflow.keras.mixed_precision import experimental as prec
from tqdm import tqdm
import models
import utils
from replay_buffer import ReplayBuffer
from swag_world_model import SwagWorldModel
class LAMBDA(tf.Module):
def __init__(self, config, logger, observation_s... | 9,440 | 40.047826 | 96 | py |
lbsgd-rl | lbsgd-rl-main/lbsgd_rl/utils.py | from collections import defaultdict
import matplotlib.pyplot as plt
import numpy as np
import tensorflow as tf
from tensorboardX import SummaryWriter
from tensorflow.keras.mixed_precision import experimental as prec
from tqdm import tqdm
def compute_lambda_values(next_values, rewards, terminals, discount, lambda_):
... | 8,666 | 32.723735 | 81 | py |
lbsgd-rl | lbsgd-rl-main/lbsgd_rl/models.py | import numpy as np
import tensorflow as tf
import tensorflow_probability as tfp
from tensorflow.keras.mixed_precision import experimental as prec
from tensorflow_probability import distributions as tfd
import building_blocks as blocks
import utils
class WorldModel(tf.Module):
def __init__(self,
obs... | 15,451 | 40.426273 | 80 | py |
lbsgd-rl | lbsgd-rl-main/lbsgd_rl/replay_buffer.py | import numpy as np
import tensorflow as tf
from tensorflow.keras.mixed_precision import experimental as prec
from tensorflow_probability import stats as tfps
from tf_agents.replay_buffers import episodic_replay_buffer
import utils
class EpisodeBuffer(object):
def __init__(self, safety):
self._current_episode ... | 5,399 | 35.241611 | 80 | py |
lbsgd-rl | lbsgd-rl-main/lbsgd_rl/train.py | import argparse
import os.path
import pathlib
import random
import numpy as np
import tensorflow as tf
from tensorflow.keras.mixed_precision import experimental as prec
import env_wrappers as env_wrappers
import utils as utils
from la_mbda import LAMBDA
from lbsgd.lbsgd_la_mbda import LogBarrierSafeAgent
def define... | 10,874 | 35.864407 | 80 | py |
lbsgd-rl | lbsgd-rl-main/lbsgd_rl/building_blocks.py | import numpy as np
import tensorflow as tf
from tensorflow_probability import distributions as tfd
def decoder(type_, shape, layers, units):
if type_ == 'rgb_image':
return ConvDecoder(shape, dist='normal')
elif type_ == 'binary_image':
return ConvDecoder(shape, dist='bernoulli')
elif type_ == 'dense':... | 3,705 | 32.690909 | 79 | py |
lbsgd-rl | lbsgd-rl-main/lbsgd_rl/lbsgd/lbsgd_actor.py | import tensorflow as tf
from models import Actor
class LogBarrierAdaptiveStepSize(tf.Module):
def __init__(self, m_0, m_1, base_lr, eta_0, eta_rate):
super().__init__()
self._m_0 = tf.constant(m_0, tf.float32)
self._m_1 = tf.constant(m_1, tf.float32)
self._lr = tf.Variable(base_lr, False)
self... | 2,868 | 32.752941 | 79 | py |
MolCode | MolCode-main/main.py | from config import conf
from runner import Runner
import os
import torch
out_path = 'zaixizhang/exp_gen/'
root_path = 'zaixizhang/data/drugs_processed/'
if not os.path.isdir(out_path):
os.mkdir(out_path)
runner = Runner(conf, root_path = root_path, out_path=out_path)
print('Start Training!')
runner.train(root_pat... | 360 | 23.066667 | 63 | py |
MolCode | MolCode-main/conf_gen.py | import pickle
from config import conf
from runner import Runner
import torch
import numpy as np
from rdkit import Chem
import argparse
import os
import pickle
import torch
import networkx as nx
from rdkit.Chem.rdchem import BondType
from collections import defaultdict
import copy
from networkx.algorithms import tree
... | 4,481 | 41.283019 | 175 | py |
MolCode | MolCode-main/collect_bond_length.py | import pickle
from config import conf
from runner import Runner
import torch
#from utils import check_validity
import numpy as np
from rdkit import Chem
from rdkit.Chem.rdchem import BondType
mols = Chem.SDMolSupplier('./qm9/gdb9.sdf', removeHs=False, sanitize=False)
split_path = 'qm9/split.npz'
bond_to_type = {BondTy... | 1,292 | 39.40625 | 97 | py |
MolCode | MolCode-main/eval_covmat.py | import os
import argparse
import pickle
import torch
import numpy as np
import multiprocessing as mp
from functools import partial
from torch_geometric.data import Data
from tqdm.auto import tqdm
from utils.covmat import get_rmsd_confusion_matrix
out_path = '/zaixizhang/exp_gen/'
data_list = pickle.load(open('/zaixiz... | 1,066 | 28.638889 | 99 | py |
MolCode | MolCode-main/dataset.py | import numpy as np
import torch
from torch.utils.data import Dataset
import os
import networkx as nx
from networkx.algorithms import tree
from math import pi
from rdkit import Chem
from rdkit.Chem.rdchem import BondType
import pickle
from rdkit import Chem
def collate_mols(mol_dicts):
data_batch = {}
for key... | 15,546 | 51.346801 | 186 | py |
MolCode | MolCode-main/runner.py | import torch
from torch.utils.data import DataLoader, dataset
import os
import numpy as np
from model import SphGen
from dataset import QM9Gen, collate_mols
import torch.optim as optim
from torch_scatter import scatter
import torch.nn as nn
class Runner():
def __init__(self, conf, root_path, atomic_num_to_type={1:... | 11,472 | 52.61215 | 240 | py |
MolCode | MolCode-main/eval_prop.py | import pickle
import torch
from utils import check_validity, mol_stats, stats_bond_dist
import numpy as np
from rdkit import Chem
import os
from pyscf import gto, dft
from pyscf.prop.polarizability.rhf import dipole
from scipy.constants import physical_constants
EH2EV = physical_constants['Hartree energy in eV'][0]
d... | 4,043 | 31.095238 | 101 | py |
MolCode | MolCode-main/collect_angle_dist.py | import pickle
from config import conf
from runner import Runner
import torch
#from utils import check_validity
import numpy as np
from rdkit import Chem
from rdkit.Chem.rdchem import BondType
import random
from tqdm import tqdm
mols = Chem.SDMolSupplier('./qm9/gdb9.sdf', removeHs=False, sanitize=False)
split_path = 'q... | 2,298 | 43.211538 | 107 | py |
MolCode | MolCode-main/main_gen.py | import pickle
from config import conf
from runner import Runner
import torch
from utils import check_validity, compute_bonds_mmd
import numpy as np
from rdkit import Chem
import argparse
import os
def generate(node_temp, dist_temp, angle_temp, torsion_temp, bond_temp):
runner = Runner(conf)
min_atoms = 4
m... | 2,479 | 46.692308 | 218 | py |
MolCode | MolCode-main/utils/eval_bond_mmd.py | import torch
import numpy as np
def stats_bond_dist(mol_dict, valid_list, con_mat_list):
bond_dist = {}
id = 0
for n_atoms in mol_dict:
numbers, positions = mol_dict[n_atoms]['_atomic_numbers'], mol_dict[n_atoms]['_positions']
for pos, num in zip(positions, numbers):
if not val... | 3,712 | 38.924731 | 139 | py |
MolCode | MolCode-main/utils/covmat.py | import torch
import numpy as np
import pandas as pd
import multiprocessing as mp
from torch_geometric.data import Data
from functools import partial
from tqdm.auto import tqdm
from rdkit import Chem
from rdkit.Chem.rdForceFieldHelpers import MMFFOptimizeMolecule
from copy import deepcopy
from rdkit.Chem import rdMolAli... | 6,729 | 36.18232 | 117 | py |
MolCode | MolCode-main/model/features.py | # Based on the code from: https://github.com/klicperajo/dimenet,
# https://github.com/rusty1s/pytorch_geometric/blob/master/torch_geometric/nn/models/dimenet_utils.py
import numpy as np
from scipy.optimize import brentq
from scipy import special as sp
import torch
from math import sqrt, pi as PI
try:
import sympy... | 9,394 | 34.05597 | 148 | py |
MolCode | MolCode-main/model/spherenet.py | import os
import numpy as np
import torch
from torch import nn
from torch.nn import Linear, Embedding
from torch_geometric.nn.acts import swish
from torch_geometric.nn.inits import glorot_orthogonal
from torch_geometric.nn import radius_graph
from torch_scatter import scatter
import torch.nn.functional as F
from math i... | 11,280 | 36.603333 | 160 | py |
MolCode | MolCode-main/model/sphgen.py | import torch
import torch.nn as nn
import torch.nn.functional as F
from .spherenet import SphereNet
from .rgcn import RGCN
from .net_utils import *
from .geometric_computing import *
from .att import MH_ATT
from rdkit import Chem
class SphGen(nn.Module):
def __init__(self, cutoff, num_node_types, num_edge_types,... | 36,919 | 62.875433 | 227 | py |
MolCode | MolCode-main/model/rgcn.py | import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
class RelationGraphConvolution(nn.Module):
"""
Relation GCN layer.
"""
def __init__(self, in_features, out_features, edge_dim=3, aggregate='sum', dropout=0., use_relu=True, bias=False):
'''
:param i... | 4,689 | 32.985507 | 118 | py |
MolCode | MolCode-main/model/att.py | import torch
import torch.nn as nn
from torch_geometric.utils import softmax
from torch_scatter import scatter
from torch.nn import Embedding
class MH_ATT(nn.Module):
def __init__(self, n_att_heads=4, q_dim=128, k_dim=128, v_dim=128, out_dim=128):
super(MH_ATT, self).__init__()
self.n_att_heads =... | 2,212 | 50.465116 | 128 | py |
MolCode | MolCode-main/model/egnn.py | import math
import numpy as np
import torch
import torch.nn as nn
class GCL(nn.Module):
def __init__(self, input_nf, output_nf, hidden_nf, normalization_factor, aggregation_method, activation,
edges_in_d=0, nodes_att_dim=0, attention=False, normalization=None):
super(GCL, self).__init__()... | 13,578 | 41.567398 | 124 | py |
MolCode | MolCode-main/model/net_utils.py | import numpy as np
import torch
import torch.nn as nn
class ST_Net_Exp(nn.Module):
def __init__(self, input_dim, output_dim, hid_dim=64, num_layers=2, bias=True):
super(ST_Net_Exp, self).__init__()
self.num_layers = num_layers # unused
self.input_dim = input_dim
self.hid_dim = hid... | 2,781 | 28.595745 | 83 | py |
MolCode | MolCode-main/model/geometric_computing.py | # Based on the code from: https://github.com/klicperajo/dimenet,
# https://github.com/rusty1s/pytorch_geometric/blob/master/torch_geometric/nn/models/dimenet.py
import torch
from torch_scatter import scatter
from torch_sparse import SparseTensor
from math import sqrt, pi as PI
from torch_geometric.nn import knn_graph
... | 4,647 | 36.788618 | 137 | py |
mRASP | mRASP-master/user_dir/tasks/translation_w_langtok.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 itertools
import json
import logging
import os
import torch
from argparse import Namespace
import numpy as np
from fairseq import metr... | 19,849 | 40.614256 | 106 | py |
mRASP | mRASP-master/train/scripts/average_checkpoints_from_file.py | #!/usr/bin/env python3
# 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 argparse
import collections
import torch
import os
import re
def average_checkpoints(inputs):
"""Loads che... | 3,910 | 30.540323 | 102 | py |
mRASP | mRASP-master/train/scripts/concat_merge_vocab.py | import argparse
import itertools
import torch
import typing
import os
def get_args():
parser = argparse.ArgumentParser()
parser.add_argument("--checkpoint", type=str, required=True)
parser.add_argument("--now-vocab", type=str, required=True)
parser.add_argument("--to-append-vocab", type=str, required=... | 2,862 | 30.811111 | 111 | py |
isbi2017-part1 | isbi2017-part1-master/segment.py | import os
import numpy as np
from keras.optimizers import Adam, SGD
from keras.callbacks import ModelCheckpoint
from keras import backend as K
from keras.preprocessing.image import ImageDataGenerator
import matplotlib.pyplot as plt
import pickle as pkl
import ISIC_dataset as ISIC
from metrics import dice_loss, jacc_los... | 16,149 | 47.353293 | 146 | py |
isbi2017-part1 | isbi2017-part1-master/models.py | import numpy as np
from keras.models import Model
from keras.layers import merge, Flatten, Dense, Input, Dropout, Activation, Reshape
from keras.layers import Convolution2D, MaxPooling2D, ZeroPadding2D, UpSampling2D
from keras.layers import BatchNormalization
from keras.layers.noise import GaussianNoise
import h5py
np.... | 11,667 | 46.62449 | 112 | py |
isbi2017-part1 | isbi2017-part1-master/metrics.py | import numpy as np
from keras import backend as K
from sklearn.metrics import jaccard_similarity_score
smooth_default = 1.
def dice_coef(y_true, y_pred, smooth = smooth_default, per_batch = True):
if not per_batch:
y_true_f = K.flatten(y_true)
y_pred_f = K.flatten(y_pred)
intersection = K.... | 2,196 | 39.685185 | 127 | py |
SOPR-T | SOPR-T-main/src/MLP_ranknet.py | import numpy as np
import torch
import torch.nn as nn
class RankTransformer(nn.Module):
def __init__(self, num_enc_opt, num_features_rank, num_features_enc, num_seg, using_cls):
super(RankTransformer, self).__init__()
self.num_enc_opt = num_enc_opt
self.num_features_rank = num_features_rank... | 4,693 | 36.854839 | 156 | py |
SOPR-T | SOPR-T-main/src/train_policy.py | import argparse
import numpy as np
import os
import torch
import offline_agent
import online_agent
from utils.constants import env_list
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument("--env", default="HalfCheetah-v2") # OpenAI gym environment name
parser.add_argument("-... | 2,484 | 44.181818 | 123 | py |
SOPR-T | SOPR-T-main/src/test_ranknet.py | import numpy as np
import torch
import argparse
import time
from utils.constants import env_list
from utils.load_cluster import interact_with_data
import os
import offline_agent
import online_agent
from utils.metric import metric
def setup_seed(seed):
torch.manual_seed(seed)
torch.cuda.manual_seed_all(seed)
... | 9,116 | 51.699422 | 222 | py |
SOPR-T | SOPR-T-main/src/offline_agent.py | import numpy as np
import d3rlpy
import os
from d3rlpy.dataset import MDPDataset
from d3rlpy.metrics.scorer import td_error_scorer
from d3rlpy.metrics.scorer import average_value_estimation_scorer
from d3rlpy.metrics.scorer import dynamics_observation_prediction_error_scorer
from d3rlpy.metrics.scorer import dynamics_r... | 4,321 | 43.102041 | 132 | py |
SOPR-T | SOPR-T-main/src/RankTransformerNet.py | import numpy as np
import torch
import torch.nn as nn
class RankTransformer(nn.Module):
def __init__(self, num_enc_opt, num_features_enc, num_seg, using_cls):
super(RankTransformer, self).__init__()
self.num_enc_opt = num_enc_opt
self.num_seg = num_seg
self.usingCLS = using_cls
... | 3,465 | 38.83908 | 147 | py |
SOPR-T | SOPR-T-main/src/train_ranknet.py | import os
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.optim import Adam
from tensorboardX import SummaryWriter
import argparse
import time
import tqdm
import re
import json
import online_agent
from utils.constants import env_list
from utils.load_cluster import inte... | 12,848 | 42.853242 | 238 | py |
SOPR-T | SOPR-T-main/src/online_agent.py | import numpy as np
import d3rlpy
import os
from d3rlpy.dataset import MDPDataset
from d3rlpy.metrics.scorer import td_error_scorer
from d3rlpy.metrics.scorer import average_value_estimation_scorer
from d3rlpy.metrics.scorer import evaluate_on_environment
import gym
import torch
# wrapper of d3rlpy.algo
class OnlineAg... | 2,551 | 36.529412 | 112 | py |
SOPR-T | SOPR-T-main/src/utils/load_cluster.py | # get actions for states of each cluster and concate
import torch
import numpy as np
def interact_with_data(s_cluster, pol_list, device=torch.device('cuda'), noise_std=0):
if type(s_cluster) is list:
NUM_seg = len(s_cluster)
else:
NUM_seg = s_cluster.shape[0]
num_pol = len(pol_list)
NU... | 2,077 | 34.827586 | 119 | py |
SOPR-T | SOPR-T-main/d3rlpy-master/setup.py | import os
from setuptools import setup, Extension
os.environ['CFLAGS'] = '-std=c++11'
# get __version__ variable
here = os.path.abspath(os.path.dirname(__file__))
exec(open(os.path.join(here, 'd3rlpy', '_version.py')).read())
if __name__ == "__main__":
from numpy import get_include
from Cython.Build import ... | 3,740 | 40.10989 | 85 | py |
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