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
value |
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scAdapt | scAdapt-master/scAdapt/utils.py | # !/usr/bin/env python
import numpy as np
from sklearn.decomposition import PCA
import umap
import matplotlib.pyplot as plt
# The 954 most common RGB monitor colors
# https://xkcd.com/color/rgb/
# 9-class Set1, for plotting data with qualitative labels
color_dict = {0:'#e41a1c', 1:'#377eb8', 2:'#4daf4a', 3:'#984ea3', ... | 4,817 | 36.348837 | 126 | py |
scAdapt | scAdapt-master/scAdapt/networks.py | import torch
import torch.nn as nn
import torch.optim as optim
from torch.autograd.variable import *
import os
import numpy as np
import torch.nn.functional as F
from easydl import *
# seed_everything()
import torch
import numpy as np
seed=666
import torch.backends.cudnn as cudnn
cudnn.deterministic = True
cudnn.bench... | 3,765 | 27.748092 | 113 | py |
scAdapt | scAdapt-master/scAdapt/vat.py | import contextlib
import torch
import torch.nn as nn
import torch.nn.functional as F
@contextlib.contextmanager
def _disable_tracking_bn_stats(model):
def switch_attr(m):
if hasattr(m, 'track_running_stats'):
m.track_running_stats ^= True
model.apply(switch_attr)
yield
... | 1,854 | 27.984375 | 78 | py |
scAdapt | scAdapt-master/scAdapt/center_loss.py | import torch
import torch.nn as nn
# seed_everything()
import torch
import numpy as np
seed=666
import torch.backends.cudnn as cudnn
cudnn.deterministic = True
cudnn.benchmark = True
import random
random.seed(seed)
torch.manual_seed(seed)
torch.cuda.manual_seed_all(seed)
np.random.seed(seed)
class CenterLoss(nn.Modul... | 2,153 | 34.9 | 137 | py |
gpytorch_test | gpytorch_test-main/test_pyro_gpytorch.py |
"""
Latent Function Inference with Pyro + GPyTorch Test code.
Adapted from:
https://docs.gpytorch.ai/en/v1.5.1/examples/07_Pyro_Integration/Pyro_GPyTorch_Low_Level.html
"""
import sys
import math
import torch
import pyro
import gpytorch
import numpy as np
import argparse
from time import perf_counter
parser = ar... | 5,326 | 34.993243 | 136 | py |
Demon-in-the-Variant | Demon-in-the-Variant-master/pysrc/train_imagenet.py | from __future__ import print_function
from __future__ import division
from __future__ import print_function
import os
from absl import app
from absl import flags
from absl import logging
import tensorflow as tf
import numpy as np
import cv2
import random
import pickle
import copy
from config import Options
import t... | 16,607 | 33.672234 | 127 | py |
Demon-in-the-Variant | Demon-in-the-Variant-master/pysrc/train_megaface.py | from __future__ import print_function
from __future__ import division
from __future__ import print_function
import os
from absl import app
from absl import flags
from absl import logging
import tensorflow as tf
from tensorflow.keras import backend as K
import numpy as np
import cv2
import random
import pickle
from ... | 22,020 | 31.527326 | 141 | py |
Demon-in-the-Variant | Demon-in-the-Variant-master/pysrc/train_cifar10.py | from __future__ import print_function
import sys
sys.path.append('home/tdteach/workspace/models/')
import os
from absl import app
from absl import flags as absl_flags
from absl import logging
os.environ["TF_CPP_MIN_LOG_LEVEL"] = "2"
logging.set_verbosity(logging.ERROR)
import tensorflow as tf
from official.utils.flag... | 28,096 | 30.569663 | 148 | py |
Demon-in-the-Variant | Demon-in-the-Variant-master/pysrc/train_gtsrb.py | from __future__ import print_function
import sys
sys.path.append('home/tdteach/workspace/models/')
import os
from absl import app
from absl import flags as absl_flags
from absl import logging
os.environ["TF_CPP_MIN_LOG_LEVEL"] = "2"
logging.set_verbosity(logging.ERROR)
import tensorflow as tf
from official.utils.flag... | 29,713 | 30.443386 | 150 | py |
inlinetest | inlinetest-main/research/research/collector.py | from jsonargparse import CLI
import seutil as se
import requests
from research.macros import Macros
from tqdm import tqdm
import os
import re
class Collector:
def __init__(self):
pass
def bash_str_parse_helper(self, bash_str: str):
bash_str = bash_str.replace(".", r"\.")
# bash_str = ... | 6,054 | 40.758621 | 118 | py |
inlinetest | inlinetest-main/research/research/macros.py | from os.path import expanduser
import os
from pathlib import Path
class Macros:
this_dir: Path = Path(os.path.dirname(os.path.realpath(__file__)))
home_dir: Path = Path(expanduser("~"))
project_dir: Path = this_dir.parent.parent
log_file = project_dir / "experiments.log"
python_dir: Path = project... | 2,532 | 34.180556 | 70 | py |
inlinetest | inlinetest-main/data/examples/python/collection_4.py | from inline import Here
def benchmark(args):
if args.amp:
_logger.warning("Overriding precision to 'amp' since --amp flag set.")
args.precision = "amp"
_logger.info(
f"Benchmarking in {args.precision} precision. "
f'{"NHWC" if args.channels_last else "NCHW"} layout. '
f'... | 2,330 | 39.894737 | 117 | py |
oneAPI-samples | oneAPI-samples-master/AI-and-Analytics/version_check.py | #import importlib
from importlib import util
tensorflow_found = util.find_spec("tensorflow") is not None
pytorch_found = util.find_spec("torch") is not None
pytorch_ext_found = util.find_spec("intel_extension_for_pytorch") is not None
tensorflow_ext_found = util.find_spec("intel_extension_for_tensorflow") is not None
x... | 4,738 | 34.901515 | 109 | py |
oneAPI-samples | oneAPI-samples-master/AI-and-Analytics/End-to-end-Workloads/LanguageIdentification/Inference/inference_commonVoice.py | #!/usr/bin/env python
# encoding: utf-8
'''
==============================================================
Copyright © 2022 Intel Corporation
SPDX-License-Identifier: MIT
==============================================================
'''
import os
import random
import csv
from time import time
from collections impo... | 12,421 | 52.08547 | 173 | py |
oneAPI-samples | oneAPI-samples-master/AI-and-Analytics/End-to-end-Workloads/LanguageIdentification/Inference/quantize_model.py | #!/usr/bin/env python
# encoding: utf-8
'''
==============================================================
Copyright © 2022 Intel Corporation
SPDX-License-Identifier: MIT
==============================================================
'''
import sys
import os
import time
import torch
import numpy as np
from neural_c... | 3,723 | 31.666667 | 135 | py |
oneAPI-samples | oneAPI-samples-master/AI-and-Analytics/End-to-end-Workloads/LanguageIdentification/Inference/inference_custom.py | #!/usr/bin/env python
# encoding: utf-8
'''
==============================================================
Copyright © 2022 Intel Corporation
SPDX-License-Identifier: MIT
==============================================================
'''
import os
import numpy as np
import random
import csv
from time import time
fr... | 19,524 | 52.787879 | 173 | py |
oneAPI-samples | oneAPI-samples-master/AI-and-Analytics/Getting-Started-Samples/IntelAIKitContainer_GettingStarted/version_check.py | #import importlib
from importlib import util
tensorflow_found = util.find_spec("tensorflow") is not None
pytorch_found = util.find_spec("torch") is not None
pytorch_ext_found = util.find_spec("intel_pytorch_extension") is not None
tensorflow_ext_found = util.find_spec("intel_extension_for_tensorflow") is not None
xgboo... | 3,077 | 36.084337 | 106 | py |
oneAPI-samples | oneAPI-samples-master/AI-and-Analytics/Getting-Started-Samples/IntelPython_XGBoost_GettingStarted/IntelPython_XGBoost_GettingStarted.py | #!/usr/bin/env python
# coding: utf-8
# In[2]:
# =============================================================
# Copyright © 2020 Intel Corporation
#
# SPDX-License-Identifier: MIT
# =============================================================
# # XGBoost Getting Started Example on Linear Regression
# ## Importi... | 2,294 | 21.067308 | 190 | py |
oneAPI-samples | oneAPI-samples-master/AI-and-Analytics/Getting-Started-Samples/INC-Sample-for-Tensorflow/mnist_dataset.py |
#tf 2.x
import tensorflow as tf
from tensorflow.keras import utils
def read_data():
classes = 10
print("Loading data ...")
mnist = tf.keras.datasets.mnist.load_data()
# print("Converting data ...")
(x_train, label_train), (x_test, label_test) = mnist
x_train = x_train.astype('float32')
... | 782 | 28 | 86 | py |
oneAPI-samples | oneAPI-samples-master/AI-and-Analytics/Getting-Started-Samples/INC-Sample-for-Tensorflow/alexnet.py | import os
import numpy as np
import tensorflow as tf
from tensorflow.python.keras import utils
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense, Dropout, Flatten, Activation, Reshape
from tensorflow.keras.layers import Convolution2D, MaxPooling2D
from tensorflow.keras.layers im... | 3,375 | 29.690909 | 107 | py |
oneAPI-samples | oneAPI-samples-master/AI-and-Analytics/Getting-Started-Samples/INC-Quantization-Sample-for-PyTorch/dataset.py | from torch.utils.data import Dataset
from typing import List
from transformers import AutoTokenizer
import torch
class IMDBDataset(Dataset):
"""Dataset with strings to predict pos/neg
Args:
text (List[str]): list of strings
label (List[str]): list of corresponding labels (spam/ham)
data... | 1,201 | 28.317073 | 75 | py |
oneAPI-samples | oneAPI-samples-master/AI-and-Analytics/Getting-Started-Samples/Intel_Extension_For_PyTorch_GettingStarted/Intel_Extension_For_PyTorch_Hello_World.py | #!/usr/bin/env python
# encoding: utf-8
'''
==============================================================
Copyright © 2019 Intel Corporation
SPDX-License-Identifier: MIT
==============================================================
'''
import torch
import torch.nn as nn
from torch.utils.data import Dataset, Data... | 4,232 | 32.864 | 318 | py |
oneAPI-samples | oneAPI-samples-master/AI-and-Analytics/Features-and-Functionality/IntelPytorch_Interactive_Chat_Quantization/IntelPytorch_Interactive_Chat_Quantization.py | #!/usr/bin/env python
# coding: utf-8
# In[ ]:
# =============================================================
# Copyright © 2023 Intel Corporation
#
# SPDX-License-Identifier: MIT
# =============================================================
# # Interactive chat based on DialoGPT model using Intel® Extension f... | 8,874 | 33.266409 | 511 | py |
oneAPI-samples | oneAPI-samples-master/AI-and-Analytics/Features-and-Functionality/IntelPython_XGBoost_Performance/IntelPython_XGBoost_Performance.py | #!/usr/bin/env python
# coding: utf-8
# # XGBoost Performance Comparison
# In this example we will train a XGBoost model and predict the results to show off Intel's optimizations for XGBoost used for increased performance. Intel optimized XGBoost is shipped as a part of the Intel® oneAPI AI Analytics Toolkit.
#
# T... | 6,795 | 32.313725 | 426 | py |
oneAPI-samples | oneAPI-samples-master/AI-and-Analytics/Features-and-Functionality/IntelPython_XGBoost_daal4pyPrediction/ci_test.py | import os
def runJupyterNotebook(input_notebook_filename, output_notebook_filename, conda_env, fdpath='./'):
import nbformat
import os
from nbconvert.preprocessors import ExecutePreprocessor
from nbconvert.preprocessors import CellExecutionError
if os.path.isfile(input_notebook_filename) is False:... | 1,109 | 40.111111 | 98 | py |
oneAPI-samples | oneAPI-samples-master/AI-and-Analytics/Features-and-Functionality/IntelPyTorch_Extensions_Inference_Optimization/scripts/ci_test.py | def runJupyterNotebook(input_notebook_filename, output_notebook_filename, conda_env, fdpath='./'):
import nbformat
import os
from nbconvert.preprocessors import ExecutePreprocessor
from nbconvert.preprocessors import CellExecutionError
if os.path.isfile(input_notebook_filename) is False:
pri... | 1,041 | 46.363636 | 185 | py |
oneAPI-samples | oneAPI-samples-master/AI-and-Analytics/Features-and-Functionality/IntelPyTorch_Extensions_Inference_Optimization/scripts/resnet50.py | import torch
import torchvision.models as models
def inference(model, data):
with torch.no_grad():
# warm up
for _ in range(100):
model(data)
# measure
import time
start = time.time()
for _ in range(100):
output = model(data)
end = time.time()
print('Inference took {:.2f}... | 1,644 | 25.967213 | 92 | py |
oneAPI-samples | oneAPI-samples-master/AI-and-Analytics/Features-and-Functionality/IntelTensorFlow_AMX_BF16_Training/Intel_TensorFlow_AMX_BF16_Training.py | # details about this Python script
# - based on a Kaggle solution - https://www.kaggle.com/code/xhlulu/disaster-nlp-distilbert-in-tf/notebook for disaster tweet classification
# - uses the pretrained huggingface distilbert model and fine tunes it based on test dataset
# - uses keras mixed precision API to run the BF16... | 9,411 | 39.568966 | 144 | py |
oneAPI-samples | oneAPI-samples-master/AI-and-Analytics/Features-and-Functionality/IntelPyTorch_InferenceOptimizations_AMX_BF16_INT8/ci_test.py | import os
def runJupyterNotebook(input_notebook_filename, output_notebook_filename, conda_env, fdpath='./'):
import nbformat
import os
from nbconvert.preprocessors import ExecutePreprocessor
from nbconvert.preprocessors import CellExecutionError
if os.path.isfile(input_notebook_filename) is False:
... | 1,176 | 44.269231 | 98 | py |
oneAPI-samples | oneAPI-samples-master/AI-and-Analytics/Features-and-Functionality/IntelPyTorch_InferenceOptimizations_AMX_BF16_INT8/IntelPyTorch_InferenceOptimizations_AMX_BF16_INT8.py | #!/usr/bin/env python
# encoding: utf-8
'''
==============================================================
Copyright © 2023 Intel Corporation
SPDX-License-Identifier: MIT
==============================================================
'''
import os
from time import time
import matplotlib.pyplot as plt
import torch
... | 9,285 | 38.016807 | 141 | py |
oneAPI-samples | oneAPI-samples-master/AI-and-Analytics/Features-and-Functionality/IntelPytorch_Quantization/IntelPytorch_Quantization.py | #!/usr/bin/env python
# encoding: utf-8
'''
==============================================================
Copyright © 2022 Intel Corporation
SPDX-License-Identifier: MIT
==============================================================
'''
import torch
import torchvision
import tqdm
import os
from time import time
im... | 5,745 | 36.311688 | 113 | py |
oneAPI-samples | oneAPI-samples-master/AI-and-Analytics/Features-and-Functionality/IntelPyTorch_TrainingOptimizations_AMX_BF16/IntelPyTorch_TrainingOptimizations_AMX_BF16.py | #!/usr/bin/env python
# encoding: utf-8
'''
==============================================================
Copyright © 2022 Intel Corporation
SPDX-License-Identifier: MIT
==============================================================
'''
import os
from time import time
import matplotlib.pyplot as plt
import torch
... | 5,883 | 34.233533 | 129 | py |
oneAPI-samples | oneAPI-samples-master/AI-and-Analytics/Features-and-Functionality/IntelPyTorch_TrainingOptimizations_AMX_BF16/ci_test.py | import os
def runJupyterNotebook(input_notebook_filename, output_notebook_filename, conda_env, fdpath='./'):
import nbformat
import os
from nbconvert.preprocessors import ExecutePreprocessor
from nbconvert.preprocessors import CellExecutionError
if os.path.isfile(input_notebook_filename) is False:
... | 1,164 | 43.807692 | 98 | py |
oneAPI-samples | oneAPI-samples-master/AI-and-Analytics/Features-and-Functionality/IntelTensorFlow_AMX_BF16_Inference/Intel_TensorFlow_AMX_BF16_Inference.py | # details about this Python script
# - Enable auto-mixed precision with few code changes for faster inference.
# - Image Classification task using TensorFlow Hub's ResNet50v1.5 pretrained model.
# - Export the optimized model in the SavedModel format.
# Importing libraries
import os
import numpy as np
import time
imp... | 4,481 | 33.21374 | 145 | py |
PEBAL | PEBAL-main/code/main.py | import argparse
import torch.distributed as dist
import torch.optim
from config.config import config
from dataset.data_loader import Fishyscapes, Cityscapes
from dataset.data_loader import get_mix_loader
from engine.engine import Engine
from engine.evaluator import SlidingEval
from engine.lr_policy import WarmUpPolyLR
... | 7,077 | 44.961039 | 119 | py |
PEBAL | PEBAL-main/code/losses.py | import torch
import torch.nn.functional as F
from torchvision import transforms
def smooth(arr, lamda1):
new_array = arr
arr2 = torch.zeros_like(arr)
arr2[:, :-1, :] = arr[:, 1:, :]
arr2[:, -1, :] = arr[:, -1, :]
new_array2 = torch.zeros_like(new_array)
new_array2[:, :, :-1] = new_array[:, :,... | 3,970 | 36.462264 | 111 | py |
PEBAL | PEBAL-main/code/test.py | import argparse
import torch.optim
from valid import *
from utils.logger import *
from engine.engine import Engine
from config.config import config
from model.network import Network
from collections import OrderedDict
from engine.evaluator import SlidingEval
from dataset.data_loader import Fishyscapes, Cityscapes
from ... | 4,040 | 39.41 | 104 | py |
PEBAL | PEBAL-main/code/valid.py | import warnings
import numpy as np
import torch.optim
from tqdm import tqdm
from utils.metric import hist_info
from utils.pyt_utils import eval_ood_measure
warnings.filterwarnings('ignore', '.*imshow.*', )
def valid_anomaly(model, test_set, data_name=None, epoch=None, my_wandb=None, logger=None,
... | 3,223 | 35.636364 | 114 | py |
PEBAL | PEBAL-main/code/dataset/data_loader.py | import os
import random
from collections import namedtuple
from typing import Any, Callable, Optional, Tuple
import cv2
import numpy as np
import torch
from PIL import Image
from torch.utils import data
from torch.utils.data import Dataset
from config.config import config
from dataset.base_dataset import BaseDataset
... | 32,339 | 42.702703 | 120 | py |
PEBAL | PEBAL-main/code/dataset/base_dataset.py | #!/usr/bin/env python3
# encoding: utf-8
# @Time : 2017/12/16 下午8:41
# @Author : yuchangqian
# @Contact : changqian_yu@163.com
# @File : base_dataset.py
import os
import cv2
import numpy as np
import torch
import torch.utils.data as data
class BaseDataset(data.Dataset):
def __init__(self, setting, split_... | 4,912 | 30.292994 | 83 | py |
PEBAL | PEBAL-main/code/engine/engine.py | #!/usr/bin/env python3
# encoding: utf-8
# @Time : 2018/8/2 下午3:23
# @Author : yuchangqian
# @Contact : changqian_yu@163.com
# @File : engine.py
import argparse
import os
import shutil
import time
import torch
import torch.distributed as dist
from utils.pyt_utils import load_model, link_file, ensure_dir
from u... | 7,335 | 35.864322 | 114 | py |
PEBAL | PEBAL-main/code/engine/evaluator.py | import collections
import time
import cv2
import numpy
import torch
from utils.metric import compute_score
class SlidingEval(torch.nn.Module):
def __init__(self, config, device):
super(SlidingEval, self).__init__()
self.config = config
self.device = device
# slide the window to eval... | 7,252 | 37.375661 | 106 | py |
PEBAL | PEBAL-main/code/engine/trainer.py | import torch
from tqdm import tqdm
class Trainer:
"""
loss_1 -> gambler loss; loss_2 -> energy loss
lr_scheduler -> cosine;
"""
def __init__(self, engine, loss1, loss2, tensorboard, lr_scheduler=None, ckpt_dir=None):
self.engine = engine
self.loss1 = loss1
self.loss2 = los... | 3,068 | 34.686047 | 115 | py |
PEBAL | PEBAL-main/code/utils/img_utils.py | import collections
import numbers
import random
import cv2
import numpy as np
import torch
import torchvision.transforms as trans
class Compose(object):
"""Wraps together multiple image augmentations.
Should also be used with only one augmentation, as it ensures, that input
images are of type 'PIL.Image'... | 6,517 | 27.094828 | 99 | py |
PEBAL | PEBAL-main/code/utils/conv_2_5d.py | #!/usr/bin/env python
# -*- coding: utf-8 -*-
# @Time : 2019-03-04 20:52
# @Author : Jingbo Wang
# @E-mail : wangjingbo1219@foxmail.com & wangjingbo@megvii.com
# @File : conv_2.5d.py
# @Software: PyCharm
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.nn.parameter import Paramete... | 10,011 | 51.145833 | 122 | py |
PEBAL | PEBAL-main/code/utils/pyt_utils.py | # encoding: utf-8
import argparse
# from Code.furnace.engine.logger import get_logger
import logging
import os
import random
import time
from collections import Counter
from collections import OrderedDict
import sklearn.metrics as sk
import torch
from sklearn.metrics import roc_curve, precision_recall_curve, average_p... | 19,597 | 35.158672 | 111 | py |
PEBAL | PEBAL-main/code/utils/wandb_upload.py | import os
import PIL
import matplotlib.pyplot as plt
import numpy
import seaborn as sns
import torch
import torchvision
import wandb
from matplotlib.lines import Line2D
from sklearn.preprocessing import minmax_scale as scaler
# from utils.visualize import show_img
def get_class_colors(*args):
return [[128, 64,... | 11,738 | 48.741525 | 111 | py |
PEBAL | PEBAL-main/code/model/wide_resnet_base.py | """
# Code adapted from:
# https://github.com/mapillary/inplace_abn/
#
# BSD 3-Clause License
#
# Copyright (c) 2017, mapillary
# All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are met:
#
# * Redistribution... | 14,152 | 34.471178 | 87 | py |
PEBAL | PEBAL-main/code/model/resnet.py | import torch.nn as nn
from utils.pyt_utils import load_model
__all__ = ['ResNet', 'resnet18', 'resnet34', 'resnet50', 'resnet101',
'resnet152']
def conv3x3(in_planes, out_planes, stride=1):
"""3x3 convolution with padding"""
return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride,
... | 7,741 | 33.717489 | 80 | py |
PEBAL | PEBAL-main/code/model/wider_resnet.py | """
# Code adapted from:
# https://github.com/mapillary/inplace_abn/
#
# BSD 3-Clause License
#
# Copyright (c) 2017, mapillary
# All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are met:
#
# * Redistribution... | 14,164 | 34.590452 | 87 | py |
PEBAL | PEBAL-main/code/model/network.py | import torch.nn as nn
from model.wide_network import DeepWV3Plus
class Network(nn.Module):
def __init__(self, num_classes, wide=False):
super(Network, self).__init__()
# if wide:
self.branch1 = DeepWV3Plus(num_classes)
def forward(self, data, output_anomaly=False):
return sel... | 367 | 25.285714 | 64 | py |
PEBAL | PEBAL-main/code/model/mynn.py | """
Custom Norm wrappers to enable sync BN, regular BN and for weight initialization
"""
import torch
import torch.nn as nn
def Norm2d(in_channels):
"""
Custom Norm Function to allow flexible switching
"""
layer = torch.nn.BatchNorm2d
normalization_layer = layer(in_channels)
return normalizati... | 4,745 | 33.143885 | 109 | py |
PEBAL | PEBAL-main/code/model/wide_network.py | from torchvision import transforms
from model.mynn import *
from model.wide_resnet_base import WiderResNetA2
Norm2d = torch.nn.BatchNorm2d
class _AtrousSpatialPyramidPoolingModule(nn.Module):
"""
operations performed:
1x1 x depth
3x3 x depth dilation 6
3x3 x depth dilation 12
3x3 x d... | 5,190 | 31.647799 | 94 | py |
bdr | bdr-master/src/gen_scripts.py | from os import system
header = """#!/bin/bash
#note anything after #SBATCH is a command
#SBATCH --mail-user=flaxman@gmail.com
#Email you if job starts, completed or failed
#SBATCH --mail-type=NONE
#SBATCH --job-name=%s-stan
#SBATCH --partition=large
#Choose your partition depending on your requirements
#SBATCH --ntask... | 1,284 | 22.363636 | 106 | py |
gnn_solubility | gnn_solubility-main/gnn_regression.py | import time
import argparse
import numpy as np
import torch
import torch.nn as nn
from torch.utils.data import DataLoader
from libs.io_utils import get_dataset
from libs.io_utils import MyDataset
from libs.io_utils import gnn_collate_fn
from libs.models import MyModel
from libs.utils import str2bool
from libs.utils... | 7,467 | 28.171875 | 111 | py |
gnn_solubility | gnn_solubility-main/attention_visualization.py | import time
import argparse
from functools import partial
import numpy as np
import pandas as pd
import torch
import torch.nn as nn
from torch.utils.data import DataLoader
from libs.io_inference import get_dataset
from libs.io_inference import MyDataset
from libs.io_inference import my_collate_fn
from libs.models i... | 5,828 | 29.84127 | 71 | py |
gnn_solubility | gnn_solubility-main/gnn_classification.py | import time
import argparse
from functools import partial
import numpy as np
import torch
import torch.nn as nn
from torch.utils.data import DataLoader
from torch.optim.swa_utils import AveragedModel
from torch.optim.swa_utils import SWALR
from libs.io_utils import get_dataset
from libs.io_utils import SolubilityCl... | 8,390 | 28.135417 | 103 | py |
gnn_solubility | gnn_solubility-main/inference_solubility_r.py | import time
import argparse
from functools import partial
import numpy as np
import torch
import torch.nn as nn
from torch.utils.data import DataLoader
from libs.io_utils import get_dataset
from libs.io_utils import MyDataset
from libs.io_utils import gnn_collate_fn
from libs.models import MyModel
from libs.utils ... | 8,886 | 28.427152 | 113 | py |
gnn_solubility | gnn_solubility-main/gnn_regression_mcdo.py | import time
import argparse
from functools import partial
import numpy as np
import torch
import torch.nn as nn
from torch.utils.data import DataLoader
from libs.io_utils import get_dataset
from libs.io_utils import MyDataset
from libs.io_utils import gnn_collate_fn
from libs.models import MyModel
from libs.utils ... | 8,274 | 28.137324 | 111 | py |
gnn_solubility | gnn_solubility-main/libs/io_inference.py | import pandas as pd
import torch
import dgl
from rdkit import Chem
from tdc.single_pred import ADME
from tdc.single_pred import HTS
from tdc.single_pred import Tox
ATOM_VOCAB = [
'C', 'N', 'O', 'S', 'F',
'H', 'Si', 'P', 'Cl', 'Br',
'Li', 'Na', 'K', 'Mg', 'Ca',
'Fe', 'As', 'Al', 'I', 'B',
'V', 'Tl', 'Sb', 'Sn... | 3,294 | 20.535948 | 81 | py |
gnn_solubility | gnn_solubility-main/libs/utils.py | import argparse
import random
import math
import numpy as np
import torch
import torch.nn.functional as F
from sklearn.metrics import accuracy_score
from sklearn.metrics import roc_auc_score
from sklearn.metrics import precision_score
from sklearn.metrics import recall_score
from sklearn.metrics import mean_squared_... | 8,327 | 24.624615 | 149 | py |
gnn_solubility | gnn_solubility-main/libs/layers.py | import math
import torch
import torch.nn as nn
import torch.nn.functional as F
import dgl
import dgl.function as fn
from dgl.backend import pytorch as F_dgl
from dgl.nn.functional import edge_softmax
class MLP(nn.Module):
def __init__(
self,
input_dim,
hidden_dim,
output_dim,
bias=True,
act=F.relu,
... | 7,489 | 21.159763 | 95 | py |
gnn_solubility | gnn_solubility-main/libs/models.py | import torch
import torch.nn as nn
import torch.nn.functional as F
import dgl
from libs.layers import GraphConvolution
from libs.layers import GraphIsomorphism
from libs.layers import GraphIsomorphismEdge
from libs.layers import GraphAttention
from libs.layers import PMALayer
class MyModel(nn.Module):
def __init__... | 3,742 | 22.540881 | 102 | py |
gnn_solubility | gnn_solubility-main/libs/io_utils.py | import numpy as np
import torch
import dgl
from rdkit import Chem
from rdkit.Chem import AllChem
from rdkit.Chem import DataStructs
from tdc.single_pred import ADME
from tdc.single_pred import HTS
from tdc.single_pred import Tox
ATOM_VOCAB = [
'C', 'N', 'O', 'S', 'F',
'H', 'Si', 'P', 'Cl', 'Br',
'Li', 'Na', 'K'... | 5,585 | 20.992126 | 81 | py |
bayesmark | bayesmark-master/bayesmark/experiment.py | # Copyright (c) 2019 Uber Technologies, Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed... | 27,515 | 43.380645 | 120 | py |
GRAM | GRAM-main/examples/mlp_mnist.py | from typing import Dict
from typing import List
from typing import Any
from typing import Tuple
import torch
import torch.nn as nn
import pytorch_lightning as pl
from sklearn import metrics
import numpy as np
import omegaconf
import torch.nn.functional as F
import pickle
from tqdm import tqdm
import pandas as pd
imp... | 12,613 | 34.532394 | 220 | py |
GRAM | GRAM-main/examples/run_MNIST.py | import os
import time
import glob
from datetime import datetime
import pytorch_lightning as pl
import wandb
import pickle
import torch
import numpy as np
from pytorch_lightning.callbacks.early_stopping import EarlyStopping
from torchvision.datasets import MNIST
import warnings
warnings.filterwarnings('ignore')
from... | 5,013 | 30.142857 | 148 | py |
GRAM | GRAM-main/gram/knowledge_tracing/content_all_item_kt.py | from typing import Dict
from typing import List
from typing import Any
from typing import Tuple
import torch
import torch.nn as nn
import pytorch_lightning as pl
from sklearn import metrics
import numpy as np
import omegaconf
import torch.nn.functional as F
import pickle
import wandb
from gram.knowledge_tracing.compon... | 53,474 | 50.221264 | 217 | py |
GRAM | GRAM-main/gram/knowledge_tracing/AlternatingCKT.py | from typing import Dict
from typing import List
from typing import Any
from typing import Tuple
import torch
import torch.nn as nn
import pytorch_lightning as pl
from sklearn import metrics
import numpy as np
import omegaconf
import torch.nn.functional as F
import pickle
from tqdm import tqdm
from magneto.train.schedu... | 9,117 | 45.284264 | 154 | py |
GRAM | GRAM-main/gram/knowledge_tracing/content_saint_base.py | """
A simple implementation of SAINT. Does not implement training loop
"""
from typing import Dict
import omegaconf
import torch
import torch.nn as nn
from gram.knowledge_tracing.components.input_embedding import SaintInputEmbedding
class Generator(nn.Module):
"""
A submodule of SAINT that converts raw Tran... | 4,657 | 34.557252 | 84 | py |
GRAM | GRAM-main/gram/knowledge_tracing/content_dkt.py | from typing import Dict
from typing import List
from typing import Any
from typing import Tuple
import torch
import torch.nn as nn
import pytorch_lightning as pl
from sklearn import metrics
import numpy as np
import omegaconf
import torch.nn.functional as F
import pickle
from gram.knowledge_tracing.components.input_e... | 3,366 | 35.597826 | 121 | py |
GRAM | GRAM-main/gram/knowledge_tracing/content_saint_kt.py | from typing import Dict, Tuple, List, Any
import numpy as np
import omegaconf
import pytorch_lightning as pl
import torch
import torch_optimizer as advanced_optim
from magneto.train.schedulers import get_noam_scheduler
from sklearn.metrics import roc_auc_score
from torch.nn import functional as F
import pickle
from g... | 11,017 | 37.524476 | 130 | py |
GRAM | GRAM-main/gram/knowledge_tracing/components/sbert_regressor.py | from typing import Dict
from typing import List
from typing import Any
from typing import Tuple
import torch
import torch.nn as nn
import pytorch_lightning as pl
from sklearn import metrics
import numpy as np
import omegaconf
import torch.nn.functional as F
import pickle
from gram.knowledge_tracing.components.input_e... | 13,689 | 47.718861 | 172 | py |
GRAM | GRAM-main/gram/knowledge_tracing/components/eernn.py | import torch
import torch.nn as nn
class BidirectionalLSTM(nn.Module):
def __init__(self, vocab_size, embedding_dim, hidden_dim, n_layers=1, bidirectional=True, dropout=0.1):
super().__init__()
self.embedding = nn.Embedding(vocab_size, embedding_dim)
self.rnn = nn.LSTM(embedding_dim, hid... | 3,766 | 34.87619 | 125 | py |
GRAM | GRAM-main/gram/knowledge_tracing/components/SBERT.py | import logging
from typing import List, Iterable, Union, Optional
import numpy as np
from numpy import ndarray
import torch
from torch import nn, Tensor
from tqdm.autonotebook import trange
from sentence_transformers import SentenceTransformer
from sentence_transformers.util import (
batch_to_device,
)
logger = l... | 6,915 | 38.073446 | 185 | py |
GRAM | GRAM-main/gram/knowledge_tracing/components/input_embedding.py | from typing import Dict
import torch
import torch.nn as nn
import omegaconf
import pickle
from gram.knowledge_tracing.components.SBERT import SBERT, CKTAdditiveAttention
from gram.knowledge_tracing.components.eernn import BidirectionalLSTM, TransformerEncoder
from torchtext.legacy import data
from torchtext.vocab impo... | 11,372 | 39.617857 | 149 | py |
GRAM | GRAM-main/gram/scripts/run_pl_nrms.py | import numpy as np
import os
os.environ['CUDA_VISIBLE_DEVICES'] = '0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15'
from pathlib import Path
from repoc_content_kt.news_recommendation.LightningNRMSWithLM import LightningNRMSWithLM
from repoc_content_kt.news_recommendation.LightningAlternateNRMSWithLM import LightningAlternatingNR... | 12,690 | 43.68662 | 152 | py |
GRAM | GRAM-main/gram/scripts/run_script.py | import os
import time
import glob
from datetime import datetime
import pytorch_lightning as pl
import warnings
import pickle
warnings.filterwarnings('ignore')
from pytorch_lightning.callbacks.early_stopping import EarlyStopping
from pytorch_lightning.callbacks import ModelCheckpoint
from omegaconf.dictconfig import... | 13,904 | 45.196013 | 136 | py |
GRAM | GRAM-main/gram/scripts/sanity_check.py | import os
import time
import glob
from datetime import datetime
import pytorch_lightning as pl
import warnings
warnings.filterwarnings('ignore')
from pytorch_lightning.callbacks.early_stopping import EarlyStopping
from pytorch_lightning.callbacks import ModelCheckpoint
from omegaconf.dictconfig import DictConfig
fr... | 9,163 | 41.82243 | 136 | py |
GRAM | GRAM-main/gram/scripts/helpers/pipelines.py | import os
import time
import glob
from datetime import datetime
import pytorch_lightning as pl
import wandb
import pickle
import torch
import numpy as np
import warnings
warnings.filterwarnings('ignore')
from sklearn.linear_model import LinearRegression
from sklearn import linear_model
from sklearn.feature_selection... | 17,950 | 47.516216 | 203 | py |
GRAM | GRAM-main/gram/datasets/content_toeic_dataset.py | from gram.utils import utils
from typing import (
Any,
List,
)
import numpy as np
import omegaconf
import torch
from gram.utils.utils import standard_collate_fn
from blink.data.transforms import (
ConvertTypes,
AppendIntegerPosition,
ShiftRight,
)
from gram.utils.utils import StandardToeicInteracti... | 9,447 | 33.481752 | 142 | py |
GRAM | GRAM-main/gram/utils/schedulers.py | from torch.optim import Optimizer
from torch.optim.lr_scheduler import LambdaLR
def get_noam_scheduler(
optimizer: Optimizer,
warmup_steps: int,
only_warmup: bool = False,
interval: str = "step",
):
"""
Noam learning rate scheduler for pytorch-lightning.
To use this method appropriately, d... | 1,530 | 29.62 | 79 | py |
GRAM | GRAM-main/gram/utils/utils.py | from functools import partial
from typing import Any
from typing import Callable
from typing import Dict
from typing import Iterable
from typing import List
from typing import Optional
from typing import Type
from abc import ABC
from typing import Any, Dict, Iterable, Optional, Tuple, List
import h5py
import numpy as ... | 14,173 | 32.116822 | 100 | py |
GRAM | GRAM-main/gram/news_recommendation/LightningAlternateNRMSWithLM.py | import numpy as np
import torch
from torch import nn
import torch.nn.functional as F
import os
import math
from sklearn import metrics
from metrics import roc_auc_score, ndcg_score, mrr_score, ctr_score
import pytorch_lightning as pl
from model_bert import ModelBert
from LightningNRMS import LightningNRMS
from model_be... | 32,389 | 48.075758 | 281 | py |
GRAM | GRAM-main/gram/news_recommendation/LightningNRMS.py | import numpy as np
import torch
from torch import nn
import torch.nn.functional as F
import os
import math
from sklearn import metrics
from metrics import roc_auc_score, ndcg_score, mrr_score, ctr_score
import pytorch_lightning as pl
from repoc_content_kt.news_recommendation.model_bert import ModelBert
import pickle
fr... | 10,621 | 40.330739 | 216 | py |
GRAM | GRAM-main/gram/news_recommendation/LightningExpB.py | from typing import Dict
from typing import List
from typing import Any
from typing import Tuple
import torch
import torch.nn as nn
import pytorch_lightning as pl
from sklearn import metrics
import numpy as np
import omegaconf
import torch.nn.functional as F
import pickle
from gram.knowledge_tracing.components.input_e... | 6,066 | 36.91875 | 172 | py |
GRAM | GRAM-main/gram/news_recommendation/utils.py | import logging
import os
import sys
import torch
import numpy as np
import argparse
import re
import os
import time
import glob
from datetime import datetime
import pytorch_lightning as pl
import wandb
import pickle
import torch
import numpy as np
import warnings
warnings.filterwarnings('ignore')
from sentence_tran... | 9,367 | 33.568266 | 161 | py |
GRAM | GRAM-main/gram/news_recommendation/dataset.py | from torch.utils.data import IterableDataset, Dataset
import numpy as np
import random
from tqdm import tqdm
class DatasetTrain(IterableDataset):
def __init__(self, filename, news_index, news_combined, cfg):
super(DatasetTrain).__init__()
self.filename = filename
self.news_index = news_inde... | 6,549 | 40.455696 | 169 | py |
GRAM | GRAM-main/gram/news_recommendation/nrms.py | import torch
from torch import nn
import torch.nn.functional as F
import numpy as np
from repoc_content_kt.news_recommendation.model_bert import AdditiveAttention, MultiHeadAttention
class NewsEncoder(nn.Module):
def __init__(self, cfg, embedding_matrix):
super(NewsEncoder, self).__init__()
self.... | 4,476 | 55.670886 | 211 | py |
GRAM | GRAM-main/gram/news_recommendation/model_bert.py | import numpy as np
import torch
from torch import nn
import torch.nn.functional as F
import os
import math
class AdditiveAttention(nn.Module):
''' AttentionPooling used to weighted aggregate news vectors
Arg:
d_h: the last dimension of input
'''
def __init__(self, d_h, hidden_size=200):
... | 17,036 | 38.25576 | 149 | py |
GRAM | GRAM-main/gram/news_recommendation/LightningNRMSWithLM.py | import numpy as np
import torch
from torch import nn
import torch.nn.functional as F
import os
import math
from sklearn import metrics
from metrics import roc_auc_score, ndcg_score, mrr_score, ctr_score
import pytorch_lightning as pl
from model_bert import ModelBert
import pickle
from tqdm import tqdm
from repoc_conten... | 9,516 | 37.530364 | 145 | py |
GRAM | GRAM-main/gram/news_recommendation/LightningExpC.py | import numpy as np
import torch
from torch import nn
import torch.nn.functional as F
import os
import math
from sklearn import metrics
from metrics import roc_auc_score, ndcg_score, mrr_score, ctr_score
import pytorch_lightning as pl
from model_bert import ModelBert
from LightningNRMS import LightningNRMS
from model_be... | 3,724 | 39.934066 | 151 | py |
GRAM | GRAM-main/gram/news_recommendation/LightningAlternatingHalfNRMS.py | import numpy as np
import torch
from torch import nn
import torch.nn.functional as F
import os
import math
from sklearn import metrics
from metrics import roc_auc_score, ndcg_score, mrr_score, ctr_score
import pytorch_lightning as pl
from model_bert import ModelBert
from LightningNRMS import LightningNRMS
from model_be... | 22,954 | 44.098232 | 195 | py |
mopo | mopo-master/examples/development/main.py | import os
import copy
import glob
import pickle
import sys
import pdb
import tensorflow as tf
from ray import tune
from softlearning.environments.utils import get_environment_from_params
from softlearning.algorithms.utils import get_algorithm_from_variant
from softlearning.policies.utils import get_policy_from_varian... | 8,680 | 35.783898 | 80 | py |
mopo | mopo-master/examples/development/simulate_policy_with_state.py | import argparse
from distutils.util import strtobool
import json
import os
import pickle
import numpy as np
import tensorflow as tf
import pdb
from softlearning.environments.utils import get_environment_from_params
from softlearning.policies.utils import get_policy_from_variant
# from softlearning.samplers import rol... | 4,998 | 30.24375 | 88 | py |
mopo | mopo-master/examples/development/simulate_policy.py | import argparse
from distutils.util import strtobool
import json
import os
import pickle
import numpy as np
import tensorflow as tf
from softlearning.environments.utils import get_environment_from_params
from softlearning.policies.utils import get_policy_from_variant
from softlearning.samplers import rollouts
def p... | 2,833 | 31.953488 | 76 | py |
mopo | mopo-master/mopo/algorithms/mopo.py | ## adapted from https://github.com/rail-berkeley/softlearning/blob/master/softlearning/algorithms/sac.py
import os
import math
import pickle
from collections import OrderedDict
from numbers import Number
from itertools import count
import gtimer as gt
import pdb
import numpy as np
import tensorflow as tf
from tensorf... | 29,127 | 36.248082 | 143 | py |
mopo | mopo-master/mopo/utils/visualization.py | import io
import math
import numpy as np
import cv2
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
import pdb
def plot_trajectories(writer, label, epoch, env_traj, model_traj, means, stds):
state_dim = env_traj[0].size
model_states = [[obs[s] for obs in model_traj] for s in range(stat... | 4,294 | 30.580882 | 126 | py |
mopo | mopo-master/softlearning/value_functions/value_function.py | import tensorflow as tf
import numpy as np
from serializable import Serializable
class SumQFunction(Serializable):
def __init__(self,
observation_shape,
action_shape,
q_functions):
self._Serializable__initialize(locals())
self.q_functions = q_fu... | 1,833 | 30.62069 | 77 | py |
mopo | mopo-master/softlearning/preprocessors/convnet.py | import tensorflow as tf
from softlearning.models.feedforward import feedforward_model
from softlearning.utils.keras import PicklableKerasModel
def convnet_preprocessor(
input_shapes,
image_shape,
output_size,
conv_filters=(32, 32),
conv_kernel_sizes=((5, 5), (5, 5)),
p... | 2,328 | 28.858974 | 71 | py |
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