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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urnng | urnng-master/train_lm.py | #!/usr/bin/env python3
import sys
import os
import argparse
import json
import random
import shutil
import copy
import torch
from torch import cuda
import torch.nn as nn
from torch.autograd import Variable
from torch.nn.parameter import Parameter
import torch.nn.functional as F
import numpy as np
import time
import ... | 6,303 | 36.52381 | 133 | py |
urnng | urnng-master/models.py | import torch
from torch import nn
import torch.nn.functional as F
import numpy as np
from utils import *
from TreeCRF import ConstituencyTreeCRF
from torch.distributions import Bernoulli
class RNNLM(nn.Module):
def __init__(self, vocab=10000,
w_dim=650,
h_dim=650,
num_lay... | 16,848 | 39.995134 | 112 | py |
urnng | urnng-master/parse.py | #!/usr/bin/env python3
import sys
import os
import argparse
import json
import random
import shutil
import copy
import torch
from torch import cuda
import torch.nn as nn
import numpy as np
import time
from utils import *
import utils
import re
parser = argparse.ArgumentParser()
# Data path options
parser.add_argume... | 7,103 | 35.060914 | 137 | py |
urnng | urnng-master/train.py | #!/usr/bin/env python3
import sys
import os
import argparse
import json
import random
import shutil
import copy
import torch
from torch import cuda
import torch.nn as nn
from torch.autograd import Variable
from torch.nn.parameter import Parameter
import torch.nn.functional as F
import numpy as np
import time
import ... | 14,042 | 41.683891 | 121 | py |
dgcnn | dgcnn-master/pytorch/main.py | #!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
@Author: Yue Wang
@Contact: yuewangx@mit.edu
@File: main.py
@Time: 2018/10/13 10:39 PM
"""
from __future__ import print_function
import os
import argparse
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torch.optim.l... | 9,567 | 40.964912 | 119 | py |
dgcnn | dgcnn-master/pytorch/model.py | #!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
@Author: Yue Wang
@Contact: yuewangx@mit.edu
@File: model.py
@Time: 2018/10/13 6:35 PM
"""
import os
import sys
import copy
import math
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
def knn(x, k):
inner = -2*torch.matmul(x... | 5,434 | 34.292208 | 181 | py |
dgcnn | dgcnn-master/pytorch/data.py | #!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
@Author: Yue Wang
@Contact: yuewangx@mit.edu
@File: data.py
@Time: 2018/10/13 6:21 PM
"""
import os
import sys
import glob
import h5py
import numpy as np
from torch.utils.data import Dataset
def download():
BASE_DIR = os.path.dirname(os.path.abspath(__file__))
... | 2,630 | 28.897727 | 110 | py |
dgcnn | dgcnn-master/pytorch/util.py | #!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
@Author: Yue Wang
@Contact: yuewangx@mit.edu
@File: util
@Time: 4/5/19 3:47 PM
"""
import numpy as np
import torch
import torch.nn.functional as F
def cal_loss(pred, gold, smoothing=True):
''' Calculate cross entropy loss, apply label smoothing if needed. '''
... | 995 | 20.191489 | 75 | py |
MedCAT | MedCAT-master/setup.py | import setuptools
with open("./README.md", "r") as fh:
long_description = fh.read()
setuptools.setup(
name="medcat",
setup_requires=["setuptools_scm"],
use_scm_version={"local_scheme": "no-local-version", "fallback_version": "unknown"},
author="w-is-h",
author_email="w.kraljevic@gmail.com",
... | 2,996 | 48.95 | 114 | py |
MedCAT | MedCAT-master/examples/run_standalone_meta_annotations.py | from medcat.datasets.helpers import encode_examples
from datasets import load_dataset
from transformers.data.data_collator import DataCollatorWithPadding
from transformers import AutoTokenizer
from torch.utils.data import DataLoader
from medcat.meta_cat import MetaCAT
import torch
CACHE_DIR = './'
DATASETS_CLASS_PATH ... | 1,621 | 34.26087 | 146 | py |
MedCAT | MedCAT-master/medcat/cat.py | import os
import glob
import shutil
import pickle
import traceback
import json
import logging
import math
import time
import psutil
from time import sleep
from multiprocess import Process, Manager, cpu_count
from multiprocess.queues import Queue
from multiprocess.synchronize import Lock
from typing import Union, List, ... | 82,532 | 46.460035 | 172 | py |
MedCAT | MedCAT-master/medcat/meta_cat.py | import os
import json
import logging
import torch
import numpy
from multiprocessing import Lock
from torch import nn, Tensor
from spacy.tokens import Doc
from datetime import datetime
from typing import Iterable, Iterator, Optional, Dict, List, Tuple, cast, Union
from medcat.utils.hasher import Hasher
from medcat.confi... | 24,788 | 40.732323 | 152 | py |
MedCAT | MedCAT-master/medcat/datasets/data_collator.py | from typing import Any, Dict, List
import torch
class CollateAndPadNER(object):
def __init__(self, pad_id):
self.pad_id = pad_id
def __call__(self, features: List[Any]) -> Dict[str, torch.Tensor]:
batch = {}
max_len = max([len(f['input_ids']) for f in features])
batch['input_... | 723 | 35.2 | 87 | py |
MedCAT | MedCAT-master/medcat/utils/data_utils.py | import json
import torch
import copy
import numpy as np
from sklearn.metrics import cohen_kappa_score
from typing import Dict, List, Optional, Union, Tuple, Any, Set
from spacy.tokens.doc import Doc
from spacy.tokens.span import Span
from medcat.cdb import CDB
from collections import defaultdict
import random
import l... | 39,699 | 41.826321 | 180 | py |
MedCAT | MedCAT-master/medcat/utils/meta_cat/models.py | import torch
from collections import OrderedDict
from typing import Optional, Any, List
from torch import nn, Tensor
from torch.nn import CrossEntropyLoss
from transformers import BertPreTrainedModel, BertModel, BertConfig
from transformers.modeling_outputs import TokenClassifierOutput
from medcat.meta_cat import Confi... | 6,023 | 39.16 | 122 | py |
MedCAT | MedCAT-master/medcat/utils/meta_cat/ml_utils.py | import os
import random
import math
import torch
import numpy as np
import pandas as pd
import torch.optim as optim
from typing import List, Optional, Tuple, Any, Dict
from torch import nn
from scipy.special import softmax
from medcat.config_meta_cat import ConfigMetaCAT
from medcat.tokenizers.meta_cat_tokenizers impor... | 11,484 | 37.029801 | 148 | py |
MedCAT | MedCAT-master/medcat/preprocessing/iterators.py | import pandas
import re
from typing import List, Optional, Dict, Iterable, Any, Tuple
NUM = "NUMNUM"
FAST_SPLIT = re.compile("[^A-Za-z0-9]")
class EmbMimicCSV(object):
"""Iterate over MIMIC data in CSV format
csv_paths: paths to csv files containing the mimic data
"""
def __init__(self, csv_paths... | 5,797 | 35.696203 | 106 | py |
learning_best_average_power | learning_best_average_power-main/wald_train.py | import numpy as np
import torch
def generate_fixed_ref(xmpl, N, null, alt):
alts = [alt]
null = np.array([null])
theories = np.concatenate([null,alts])
Xnull = xmpl.sample_prob_model(null[0],N)
Xalt0 = xmpl.sample_prob_model(alts[0],N)
ynull = np.zeros(N)
yalt0 = np.ones(N)
... | 2,836 | 28.552083 | 95 | py |
learning_best_average_power | learning_best_average_power-main/wald_plots.py | import numpy as np
import matplotlib.pyplot as plt
import torch
from wald_train import *
from wald_groundtruth import *
import scipy.stats as sps
from sklearn.isotonic import IsotonicRegression
def plot_data_showcase(xmpl, poi = 3.0, scale = 1.0):
X,y,t = generate_data(xmpl, poi = poi, scale=scale, N = 1000)
p... | 11,250 | 34.269592 | 144 | py |
learning_best_average_power | learning_best_average_power-main/wald_train_pois.py | import wald_onoff as xmpl
from wald_train import *
from wald_plots import *
from wald_groundtruth import *
model,losses = train(xmpl, 50000)
torch.save(model,'wald_pois.ckpt')
| 177 | 21.25 | 34 | py |
learning_best_average_power | learning_best_average_power-main/wald_train_gauss.py | import wald_gaussian as xmpl
from wald_train import *
from wald_plots import *
from wald_groundtruth import *
model,losses = train(xmpl, 50000)
torch.save(model,'wald_gauss.ckpt')
| 181 | 21.75 | 35 | py |
LAGNN | LAGNN-main/main.py | from graph_transformer import GraphTransformerNet
from egat_model import EGAT
import argparse
from torch.optim.lr_scheduler import ReduceLROnPlateau
from dgl import seed
import time
import logging
from sklearn import metrics
from dgl.data.utils import load_graphs
import random
import torch.nn.functional as F
import dgl... | 13,473 | 40.204893 | 187 | py |
LAGNN | LAGNN-main/egat_model.py | import torch
import torch.nn as nn
import torch.nn.functional as F
class EGATConv(nn.Module):
def __init__(self,
in_node_feats,
in_edge_feats,
out_node_feats,
out_edge_feats,
num_heads):
super().__init__()
... | 3,393 | 35.891304 | 128 | py |
HRSTNet | HRSTNet-main/tools_validate.py | import os
import sys
import argparse
from seg.config.defaults import get_cfg
from seg.utils.comm import default_setup
from seg.engine.launch import launch
from seg.engine.trainer import Trainer
from seg.utils.comm import init_data_dir
from seg.engine.trainer import val_epoch_brats
def setup(args):
"""
Create ... | 4,269 | 42.131313 | 117 | py |
HRSTNet | HRSTNet-main/tools_flops.py | import os
import sys
import argparse
import torch
from seg.config.defaults import get_cfg
from seg.utils.comm import default_setup
from seg.engine.launch import launch
from seg.utils.comm import init_data_dir
from seg.models.builder import get_model
from mmcv.cnn import get_model_complexity_info
def setup(args):
... | 3,512 | 36.774194 | 93 | py |
HRSTNet | HRSTNet-main/tools_visualize.py | import os
import sys
import argparse
from seg.config.defaults import get_cfg
from seg.utils.comm import default_setup
from seg.engine.launch import launch
from seg.engine.trainer import Trainer
from seg.utils.comm import init_data_dir
def setup(args):
"""
Create configs and perform basic setups.
"""
c... | 4,054 | 40.377551 | 93 | py |
HRSTNet | HRSTNet-main/tools_train.py | import os
import sys
import argparse
from seg.config.defaults import get_cfg
from seg.utils.comm import default_setup
from seg.engine.launch import launch
from seg.engine.trainer import Trainer
from seg.utils.comm import init_data_dir
def setup(args):
"""
Create configs and perform basic setups.
"""
c... | 3,113 | 37.444444 | 95 | py |
HRSTNet | HRSTNet-main/tools_inference.py | import os
import sys
import argparse
from seg.config.defaults import get_cfg
from seg.utils.comm import default_setup
from seg.engine.launch import launch
from seg.engine.trainer import Trainer
from seg.utils.comm import init_data_dir
def setup(args):
"""
Create configs and perform basic setups.
"""
c... | 4,510 | 41.961905 | 118 | py |
HRSTNet | HRSTNet-main/seg/solver/edice_loss.py | import torch
import torch.nn as nn
class EDiceLoss(nn.Module):
"""Dice loss tailored to Brats need.
"""
def __init__(self, do_sigmoid=True):
super(EDiceLoss, self).__init__()
self.do_sigmoid = do_sigmoid
self.labels = ["ET", "TC", "WT"]
self.device = "cpu"
def binary_... | 4,018 | 32.773109 | 102 | py |
HRSTNet | HRSTNet-main/seg/solver/build.py | import torch
from seg.config.config import CfgNode
from seg.utils.optimizer_utils import LinearWarmupCosineAnnealingLR
from monai.losses import DiceCELoss
from monai.metrics import DiceMetric
from monai.utils.enums import MetricReduction
from seg.solver.edice_loss import EDiceLoss, EDiceLoss_Val
from monai.transforms i... | 5,595 | 44.868852 | 111 | py |
HRSTNet | HRSTNet-main/seg/models/vt_unet.py | import torch
import torch.nn as nn
import copy
from seg.utils.register import MODEL_REGISTRY
from seg.models.vt_unet_utils import SwinTransformerSys3D
class VTUNet(nn.Module):
def __init__(self, config, num_classes=3, zero_head=False, embed_dim=96, win_size=7):
super(VTUNet, self).__init__()
self.... | 4,499 | 49.561798 | 115 | py |
HRSTNet | HRSTNet-main/seg/models/unetr.py | # Copyright 2020 - 2021 MONAI Consortium
# 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 to in wri... | 9,501 | 37.469636 | 126 | py |
HRSTNet | HRSTNet-main/seg/models/swin_unet.py | import torch
from seg.utils.register import MODEL_REGISTRY
from typing import Sequence, Tuple, Union
from monai.networks.blocks import UnetOutBlock, UnetrBasicBlock, UnetrUpBlock
from monai.utils import ensure_tuple_rep
import numpy as np
from seg.models.swin.swin_transformer import SwinTransformer
import torch.nn as n... | 10,105 | 37.280303 | 119 | py |
HRSTNet | HRSTNet-main/seg/models/hrtrans_utils.py | import torch
import torch.nn as nn
from torch.nn import LayerNorm
from einops import rearrange
from typing import Sequence, Type, Union, Tuple
from seg.models.swin.patch_merging import PatchMerging
from seg.models.swin.swin_blocks import BasicLayer
from monai.networks.blocks import UnetrBasicBlock
from monai.utils impo... | 43,029 | 42.377016 | 114 | py |
HRSTNet | HRSTNet-main/seg/models/nn_unet.py | # Copyright (c) MONAI Consortium
# 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 to in writing, so... | 18,686 | 46.549618 | 122 | py |
HRSTNet | HRSTNet-main/seg/models/hrtrans.py | import torch
import torch.nn as nn
from seg.utils.register import MODEL_REGISTRY
from torch.nn import LayerNorm
from monai.networks.blocks import PatchEmbed
from typing import Sequence, Type
from monai.utils import ensure_tuple_rep
from seg.models.hrtrans_utils import FinalPatchExpand, HRTransStages, FinalStage
import ... | 5,322 | 32.904459 | 80 | py |
HRSTNet | HRSTNet-main/seg/models/vt_unet_utils.py | import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.checkpoint as checkpoint
import logging
from einops import rearrange
from mmcv.runner import load_checkpoint
from monai.networks.layers import trunc_normal_
from monai.networks.layers import DropPath
from monai.netw... | 42,855 | 40.486931 | 127 | py |
HRSTNet | HRSTNet-main/seg/models/extending_nnunet.py |
# Copyright 2020 Division of Medical Image Computing, German Cancer Research Center (DKFZ), Heidelberg, Germany
#
# 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:/... | 42,868 | 47.113356 | 147 | py |
HRSTNet | HRSTNet-main/seg/models/swin/window_attention.py | import torch
import torch.nn as nn
from typing import Sequence
from monai.networks.layers import trunc_normal_
class WindowAttention(nn.Module):
"""
Window based multi-head self attention module with relative position bias based on: "Liu et al.,
Swin Transformer: Hierarchical Vision Transformer using Shif... | 4,959 | 42.893805 | 114 | py |
HRSTNet | HRSTNet-main/seg/models/swin/swin_transformer.py | import torch
import torch.nn as nn
import torch.nn.functional as F
from typing import Sequence, Type
from monai.networks.blocks import PatchEmbed
from torch.nn import LayerNorm
from seg.models.swin.swin_blocks import BasicLayer
from seg.models.swin.patch_merging import PatchMerging
from monai.utils import optional_impo... | 4,980 | 37.022901 | 82 | py |
HRSTNet | HRSTNet-main/seg/models/swin/swin_utils.py | import torch
def compute_mask(dims, window_size, shift_size, device):
"""Computing region masks based on: "Liu et al.,
Swin Transformer: Hierarchical Vision Transformer using Shifted Windows
<https://arxiv.org/abs/2103.14030>"
https://github.com/microsoft/Swin-Transformer
Args:
dims: dim... | 4,654 | 34 | 120 | py |
HRSTNet | HRSTNet-main/seg/models/swin/swin_blocks.py | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.checkpoint as checkpoint
from torch.nn import LayerNorm
import numpy as np
from typing import Sequence, Type
from monai.utils import optional_import
from monai.networks.blocks import MLPBlock as Mlp
from monai.networks.layers import D... | 11,995 | 40.365517 | 118 | py |
HRSTNet | HRSTNet-main/seg/models/swin/patch_merging.py | import torch
import torch.nn as nn
import torch.nn.functional as F
from typing import Type
from torch.nn import LayerNorm
class PatchMerging(nn.Module):
"""
Patch merging layer based on: "Liu et al.,
Swin Transformer: Hierarchical Vision Transformer using Shifted Windows
<https://arxiv.org/abs/2103.14... | 2,202 | 32.378788 | 89 | py |
HRSTNet | HRSTNet-main/seg/engine/visualization.py | import os
import time
import shutil
import numpy as np
import torch
from torch.cuda.amp import GradScaler, autocast
from tensorboardX import SummaryWriter
import torch.nn.parallel
from seg.utils.dist_utils import distributed_all_gather
import seg.utils.dist_utils as dist_utils
import torch.utils.data.distributed
from m... | 5,065 | 43.052174 | 118 | py |
HRSTNet | HRSTNet-main/seg/engine/launch.py | import logging
from datetime import timedelta
import torch
import torch.distributed as dist
import torch.multiprocessing as mp
import seg.utils.dist_utils as dist_utils
DEFAULT_TIMEOUT = timedelta(minutes=30)
def _find_free_port():
import socket
sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
... | 4,128 | 31.769841 | 95 | py |
HRSTNet | HRSTNet-main/seg/engine/defaults.py | import seg.utils.dist_utils as dist_utils
from torch.nn.parallel import DistributedDataParallel
import torch
def create_ddp_model(model, cfgs, *, fp16_compression=False, **kwargs):
"""
Create a DistributedDataParallel model if there are >1 processes.
Args:
model: a torch.nn.Module
fp16_co... | 1,071 | 41.88 | 152 | py |
HRSTNet | HRSTNet-main/seg/engine/trainer.py | # Copyright 2020 - 2021 MONAI Consortium
# 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 to in wri... | 84,685 | 50.574909 | 202 | py |
HRSTNet | HRSTNet-main/seg/utils/optimizer_utils.py | # Copyright 2020 - 2021 MONAI Consortium
# 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 to in wri... | 6,510 | 36.854651 | 119 | py |
HRSTNet | HRSTNet-main/seg/utils/dist_utils.py | import torch
import numpy as np
import math
import torch.distributed as dist
"""
A torch process group which only includes processes that on the same machine as the current process.
This variable is set when processes are spawned by `launch()` in "engine/launch.py".
"""
_LOCAL_PROCESS_GROUP = None
def distributed... | 5,084 | 33.591837 | 111 | py |
HRSTNet | HRSTNet-main/seg/utils/data_utils.py | # Copyright 2020 - 2021 MONAI Consortium
# 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 to in wri... | 2,672 | 42.112903 | 112 | py |
HRSTNet | HRSTNet-main/seg/utils/collect_env.py | # Copyright (c) Facebook, Inc. and its affiliates.
import importlib
import numpy as np
import os
import re
import subprocess
import sys
from collections import defaultdict
import PIL
import torch
import torchvision
from tabulate import tabulate
__all__ = ["collect_env_info"]
def collect_torch_env():
try:
... | 7,468 | 33.578704 | 96 | py |
HRSTNet | HRSTNet-main/seg/utils/register.py | from typing import Any
import pydoc
from fvcore.common.registry import Registry # for backward compatibility.
# predictor registry
MODEL_REGISTRY = Registry('MODEl')
"""
``Registry`` and `locate` provide ways to map a string (typically found
in config files) to callable objects.
"""
__all__ = ["Registry", "locate... | 1,180 | 24.673913 | 96 | py |
HRSTNet | HRSTNet-main/seg/utils/env.py | import logging
import numpy as np
import os
import random
from datetime import datetime
import torch
def seed_all_rng(seed=None):
"""
Set the random seed for the RNG in torch, numpy and python.
Args:
seed (int): if None, will use a strong random seed.
"""
if seed is None:
seed = (... | 690 | 24.592593 | 68 | py |
HRSTNet | HRSTNet-main/datasets/abdomen.py | # Copyright 2020 - 2021 MONAI Consortium
# 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 to in wri... | 41,140 | 46.727378 | 132 | py |
HRSTNet | HRSTNet-main/datasets/vt_unet_brats/batch_utils.py | import random
import torch.nn.functional as F
from torch.utils.data._utils.collate import default_collate
def custom_collate(batch):
batch = pad_batch_to_max_shape(batch)
return default_collate(batch)
def determinist_collate(batch):
batch = pad_batch_to_max_shape(batch)
return default_collate(batch... | 2,138 | 37.890909 | 105 | py |
HRSTNet | HRSTNet-main/datasets/vt_unet_brats/brats.py | import pathlib
import SimpleITK as sitk
import numpy as np
import torch
from sklearn.model_selection import KFold
from torch.utils.data.dataset import Dataset
from .config import get_brats_folder, get_test_brats_folder
from .image_utils import pad_or_crop_image, irm_min_max_preprocess, zscore_normalise
from skimage.tr... | 8,995 | 47.627027 | 139 | py |
physics-aware-downsampling | physics-aware-downsampling-main/main.py | import collections
import json
from typing import Dict, NamedTuple
import torch
import torch.distributed as dist
import torch.nn as nn
from absl import app
from absl import flags
from absl import logging
from torch.utils.data import distributed as dist_data
import trainer
import utils.tensorboard as tb
from data impo... | 11,082 | 43.870445 | 180 | py |
physics-aware-downsampling | physics-aware-downsampling-main/ground_truth.py | import enum
import logging
import multiprocessing
import os
import time
import tqdm
import numpy as np
import pandas as pd
import torch
from hydraulics import boundary
from hydraulics import saint_venant
from hydraulics import simulation_utils as sim_utils
INDEX_PATH = '/home/usgs_dem_data/dem/index.csv'
FLUX_OUTPUT... | 7,136 | 41.230769 | 84 | py |
physics-aware-downsampling | physics-aware-downsampling-main/evaluation.py | import collections
import csv
import datetime
import json
import os
from typing import Dict, NamedTuple
import torch
import torch.nn as nn
from absl import app
from absl import flags
from absl import logging
from torch.utils.data import distributed as dist_data
import trainer
from data import data
from hydraulics imp... | 6,627 | 40.685535 | 99 | py |
physics-aware-downsampling | physics-aware-downsampling-main/trainer.py | import time
from typing import Optional, Tuple
import torch
import torch.distributed as dist
import torch.nn as nn
from absl import logging
from hydraulics import boundary
from hydraulics import simulation_utils as sim_utils
from utils import meters
from utils import model_utils
from utils import tensorboard as tb
fr... | 6,145 | 41.979021 | 80 | py |
physics-aware-downsampling | physics-aware-downsampling-main/models/swe_model.py | from typing import Optional
import torch
import torch.nn as nn
from torch.utils import checkpoint
import hydraulics.simulation_utils as sim_utils
__all__ = ['swe_model']
class SweModel(nn.Module):
def __init__(self, downsample_model, numerical_solver, coarse_dx,
coarse_n_x, alpha, simulation_m... | 3,050 | 39.144737 | 77 | py |
physics-aware-downsampling | physics-aware-downsampling-main/models/averagenet.py | # Lint as: python3
import torch
import torch.nn as nn
__all__ = ['average_net']
class AverageNet(nn.Module):
def __init__(self, downsample_factor: int = 16):
super(AverageNet, self).__init__()
self.dummy_parameter = nn.Parameter(torch.zeros(1), True)
self.avg = nn.AvgPool2d(kernel_size=d... | 451 | 19.545455 | 65 | py |
physics-aware-downsampling | physics-aware-downsampling-main/models/edge_preserving.py | # Lint as: python3
import torch
import torch.nn as nn
import cv2
import numpy as np
__all__ = ['edge_preserve']
class EdgePreserve(nn.Module):
def __init__(self, downsample_factor: int = 16):
super(EdgePreserve, self).__init__()
self.kernel_size = 3
self.sigma_space = 50
self.sig... | 1,145 | 29.972973 | 77 | py |
physics-aware-downsampling | physics-aware-downsampling-main/models/detour.py | # Lint as: python3
import torch
import torch.nn as nn
import torch.nn.functional
from torch.utils import checkpoint
batch_norm = nn.BatchNorm2d
group_norm = nn.GroupNorm
_use_group_norm = False
def get_norm_layer(num_channels):
if _use_group_norm:
num_channels_per_group = 16
return nn.GroupNorm(... | 5,964 | 34.933735 | 80 | py |
physics-aware-downsampling | physics-aware-downsampling-main/utils/evaluation_viewer.py | import base64
import sys
from dataclasses import dataclass
import cv2
import jinja2
import numpy as np
import torch
from matplotlib import figure as plt_fig
from matplotlib.backends import backend_agg
from utils import visualization as viz
@dataclass
class Sample:
"""Class for storing sample outputs."""
sam... | 3,828 | 42.022472 | 80 | py |
physics-aware-downsampling | physics-aware-downsampling-main/utils/optimization.py | import torch
import torch.nn as nn
import torch.nn.functional as F
class InundationLoss(nn.Module):
def __init__(self, threshold: float = 0.5, reduction: str = 'mean'):
super(InundationLoss, self).__init__()
self.reduction = reduction
self.threshold = threshold
def forward(self, input... | 1,441 | 36.947368 | 80 | py |
physics-aware-downsampling | physics-aware-downsampling-main/utils/visualization.py | import logging
from typing import List, Optional
import matplotlib
import matplotlib.animation as animation
import matplotlib.cm
import matplotlib.pyplot as plt
import numpy as np
import torch
from matplotlib.colors import LightSource
def render_hillshade_water_image(z: np.ndarray, h: np.ndarray,
... | 5,160 | 34.840278 | 80 | py |
physics-aware-downsampling | physics-aware-downsampling-main/utils/model_utils.py | # Lint as: python3
import logging
from typing import Optional, Mapping, Text
import torch
import torch.nn as nn
def get_model_gradients(model: nn.Module) -> Mapping[Text, torch.Tensor]:
gradients = {}
for name, weight in model.named_parameters():
gradients[name] = weight.grad.data.clone()
return ... | 1,282 | 31.075 | 73 | py |
physics-aware-downsampling | physics-aware-downsampling-main/utils/tensorboard.py | # Lint as: python3
import os
from datetime import datetime
from typing import Optional, Mapping, Any, Sequence
import matplotlib.pyplot as plt
import torch
import torch.utils.tensorboard as tensorboard
class TensorBoard(object):
def __init__(self):
self.log_dir = None
self.writer = None
... | 4,444 | 27.49359 | 80 | py |
physics-aware-downsampling | physics-aware-downsampling-main/utils/meters.py | import torch
class AverageMeter(object):
"""Computes and stores the average and current value"""
def __init__(self):
self.reset()
def reset(self):
self.val = 0
self.sum = 0
self.count = 0
def update(self, val, n=1):
self.val = val
self.sum += val * n
... | 1,155 | 21.666667 | 61 | py |
physics-aware-downsampling | physics-aware-downsampling-main/data/data.py | import enum
import logging
from ast import literal_eval
import numpy as np
import pandas as pd
import torch
class GroundTruthType(enum.Enum):
RAIN, FLUX = range(2)
FLUX_INDEX_PATH = '/home/usgs_dem_data/flux_ground_truth/'
RAIN_INDEX_PATH = '/home/usgs_dem_data/rain_ground_truth/'
TRAIN_INDEX_NAME = 'train_gro... | 4,047 | 39.48 | 78 | py |
physics-aware-downsampling | physics-aware-downsampling-main/source/utils.py | import numpy as np
import pandas as pd
from typing import Optional, Sequence
from ast import literal_eval
import torch
def read_index_row(index: pd.DataFrame, row: int):
dem = torch.tensor(np.load(index.iloc[row][1]))
influx = literal_eval(index.iloc[row][2])
outflux = literal_eval(index.iloc[row][3])
... | 1,764 | 29.964912 | 80 | py |
physics-aware-downsampling | physics-aware-downsampling-main/hydraulics/saint_venant.py | # Lint as: python3
from typing import Optional, Tuple
import torch
import torch.nn as nn
import torch.nn.functional as F
G = 9.8
MANNING_COEFF_FLOODPLAIN = 0.05
_X_AXIS = 3
_Y_AXIS = 2
_EPSILON = 1e-8
class SaintVenantFlux(nn.Module):
"""1D saint venant equations with flux and height variables.
Implemente... | 5,431 | 42.111111 | 80 | py |
physics-aware-downsampling | physics-aware-downsampling-main/hydraulics/boundary.py | import abc
import enum
from typing import Sequence, Tuple
import torch
import torch.nn.functional as F
G = 9.8
OUTFLUX_SLOPE = 0.2
def _flux_location_to_indices(dem_shape: int, flux_location: torch.Tensor,
down_sample_factor: int):
x, y, length = flux_location
rows = dem_shape
... | 6,425 | 42.127517 | 79 | py |
physics-aware-downsampling | physics-aware-downsampling-main/hydraulics/simulation_utils.py | import torch
import torch.nn as nn
G = 9.8
def downsample(z: torch.Tensor, ds_factor: int) -> torch.Tensor:
"""downsample 2d tensor z by a factor of ds_factor.
z should have 3 dimensions of (batch size, rows, cols). if z is provided
with 2 dimensions, a third (batch size = 1) is deduced automatically.
... | 897 | 27.0625 | 78 | py |
mumin-build | mumin-build-main/src/mumin/embedder.py | """Compute node embeddings for the dataset"""
import json
import warnings
from functools import partial
from typing import Dict, List, Tuple, Union
import numpy as np
import pandas as pd
import torch
from transformers import (
AutoFeatureExtractor,
AutoModel,
AutoModelForImageClassification,
AutoToken... | 13,768 | 32.913793 | 87 | py |
mumin-build | mumin-build-main/src/mumin/dgl.py | """Functions related to exporting the dataset to the Deep Graph Library"""
import json
from pathlib import Path
from typing import Dict, Tuple, Union
import numpy as np
import pandas as pd
from torch import Tensor
def build_dgl_dataset(
nodes: Dict[str, pd.DataFrame], relations: Dict[Tuple[str, str, str], pd.Da... | 12,485 | 37.183486 | 87 | py |
itabqa | itabqa-master/itabqa/experiments.py | import json
import math
import multiprocessing
import os
from collections import Counter, defaultdict
from contextlib import closing
from multiprocessing.pool import Pool
from typing import List
import numpy as np
import pandas as pd
import spacy
import torch
from tqdm import tqdm
from transformers import Pipeline, pi... | 16,730 | 29.200361 | 135 | py |
itabqa | itabqa-master/itabqa/m_gramformer.py | import torch
from gramformer import Gramformer
def set_seed(seed):
torch.manual_seed(seed)
if torch.cuda.is_available():
torch.cuda.manual_seed_all(seed)
set_seed(1212)
gf = Gramformer(models = 1, use_gpu=False) # 1=corrector, 2=detector
influent_sentences = [
"He are moving here.",
"I am doing fi... | 1,006 | 28.617647 | 73 | py |
dreamerv2 | dreamerv2-main/dreamerv2/agent.py | import tensorflow as tf
from tensorflow.keras import mixed_precision as prec
import common
import expl
class Agent(common.Module):
def __init__(self, config, obs_space, act_space, step):
self.config = config
self.obs_space = obs_space
self.act_space = act_space['action']
self.step = step
self.... | 13,833 | 40.295522 | 78 | py |
dreamerv2 | dreamerv2-main/dreamerv2/train.py | import collections
import functools
import logging
import os
import pathlib
import re
import sys
import warnings
try:
import rich.traceback
rich.traceback.install()
except ImportError:
pass
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
logging.getLogger().setLevel('ERROR')
warnings.filterwarnings('ignore', '.*box bo... | 7,266 | 35.888325 | 79 | py |
dreamerv2 | dreamerv2-main/dreamerv2/common/tfutils.py | import pathlib
import pickle
import re
import numpy as np
import tensorflow as tf
from tensorflow.keras import mixed_precision as prec
try:
from tensorflow.python.distribute import values
except Exception:
from google3.third_party.tensorflow.python.distribute import values
tf.tensor = tf.convert_to_tensor
for ba... | 4,877 | 31.092105 | 76 | py |
dreamerv2 | dreamerv2-main/dreamerv2/common/nets.py | import re
import numpy as np
import tensorflow as tf
from tensorflow.keras import layers as tfkl
from tensorflow_probability import distributions as tfd
from tensorflow.keras.mixed_precision import experimental as prec
import common
class EnsembleRSSM(common.Module):
def __init__(
self, ensemble=5, stoch=3... | 15,219 | 35.324582 | 79 | py |
DocSCAN | DocSCAN-main/src/DocSCAN.py | import sys, os, json, argparse
import pandas as pd
from sentence_transformers import SentenceTransformer
from utils.memory import MemoryBank
import torch
from utils.DocSCAN_utils import DocScanDataset, DocScanModel
from utils.losses import SCANLoss
from utils.kneelocator import KneeLocator
from sklearn.feature_extracti... | 12,571 | 41.472973 | 159 | py |
DocSCAN | DocSCAN-main/src/DocSCAN_paper_replication.py | import sys, os, json, argparse
import pandas as pd
from sentence_transformers import SentenceTransformer
from utils.memory import MemoryBank
import torch
from utils.DocSCAN_utils import DocScanDataset, DocScanModel
from utils.losses import SCANLoss
from sklearn.feature_extraction.text import TfidfVectorizer
from tqdm i... | 9,966 | 39.681633 | 165 | py |
DocSCAN | DocSCAN-main/src/utils/losses.py | import torch
import torch.nn as nn
import torch.nn.functional as F
from sklearn.utils.class_weight import compute_class_weight
import numpy as np
EPS=1e-8
def entropy(x, input_as_probabilities):
"""
Helper function to compute the entropy over the batch
input: batch w/ shape [b, num_classes]
output: ... | 2,197 | 31.80597 | 98 | py |
DocSCAN | DocSCAN-main/src/utils/memory.py | """
Authors: Wouter Van Gansbeke, Simon Vandenhende
Licensed under the CC BY-NC 4.0 license (https://creativecommons.org/licenses/by-nc/4.0/)
"""
import numpy as np
import torch
from sklearn import metrics
def evaluate(y, preds):
print(metrics.classification_report(y, preds))
#print(metrics.confusion_matrix(y, preds... | 2,129 | 32.809524 | 103 | py |
DocSCAN | DocSCAN-main/src/utils/DocSCAN_utils.py | import argparse
import os
import json
import pandas as pd
import torch
import random
import gc
from collections import defaultdict
import numpy as np
from torch.utils.data import Dataset
from transformers import AdamW, get_linear_schedule_with_warmup
from transformers import DistilBertTokenizerFast, DistilBertModel
f... | 2,482 | 29.280488 | 79 | py |
DocSCAN | DocSCAN-main/src/utils/utils.py | import numpy as np
from scipy.optimize import linear_sum_assignment
from sklearn import metrics
def _hungarian_match(flat_preds, flat_targets, preds_k, targets_k):
# Based on implementation from IIC
num_samples = len(flat_targets)
assert (preds_k == targets_k) # one to one
num_k = preds_k
num_cor... | 2,858 | 41.044118 | 181 | py |
EntityDescriptionGeneration | EntityDescriptionGeneration-master/test/common.py | # Copyright 2017--2019 Amazon.com, Inc. or its affiliates. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License"). You may not
# use this file except in compliance with the License. A copy of the License
# is located at
#
# http://aws.amazon.com/apache2.0/
#
# or in the "license" fi... | 26,288 | 47.236697 | 140 | py |
EntityDescriptionGeneration | EntityDescriptionGeneration-master/test/common_image_captioning.py | # Copyright 2018 Amazon.com, Inc. or its affiliates. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License"). You may not
# use this file except in compliance with the License. A copy of the License
# is located at
#
# http://aws.amazon.com/apache2.0/
#
# or in the "license" file acc... | 14,551 | 46.555556 | 135 | py |
EntityDescriptionGeneration | EntityDescriptionGeneration-master/test/unit/test_layers.py | # Copyright 2017 Amazon.com, Inc. or its affiliates. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License"). You may not
# use this file except in compliance with the License. A copy of the License
# is located at
#
# http://aws.amazon.com/apache2.0/
#
# or in the "license" file acc... | 5,378 | 37.697842 | 109 | py |
EntityDescriptionGeneration | EntityDescriptionGeneration-master/test/unit/test_decoder.py | # Copyright 2017 Amazon.com, Inc. or its affiliates. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License"). You may not
# use this file except in compliance with the License. A copy of the License
# is located at
#
# http://aws.amazon.com/apache2.0/
#
# or in the "license" file acc... | 6,314 | 47.206107 | 120 | py |
EntityDescriptionGeneration | EntityDescriptionGeneration-master/test/unit/test_loss.py | # Copyright 2017, 2018 Amazon.com, Inc. or its affiliates. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License"). You may not
# use this file except in compliance with the License. A copy of the License
# is located at
#
# http://aws.amazon.com/apache2.0/
#
# or in the "license" f... | 7,049 | 43.904459 | 115 | py |
EntityDescriptionGeneration | EntityDescriptionGeneration-master/test/unit/test_encoder.py | # Copyright 2017 Amazon.com, Inc. or its affiliates. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License"). You may not
# use this file except in compliance with the License. A copy of the License
# is located at
#
# http://aws.amazon.com/apache2.0/
#
# or in the "license" file acc... | 11,983 | 51.792952 | 124 | py |
EntityDescriptionGeneration | EntityDescriptionGeneration-master/test/unit/test_rnn.py | # Copyright 2017 Amazon.com, Inc. or its affiliates. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License"). You may not
# use this file except in compliance with the License. A copy of the License
# is located at
#
# http://aws.amazon.com/apache2.0/
#
# or in the "license" file acc... | 11,111 | 47.103896 | 122 | py |
EntityDescriptionGeneration | EntityDescriptionGeneration-master/test/unit/test_constraints.py | # Copyright 2018 Amazon.com, Inc. or its affiliates. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License"). You may not
# use this file except in compliance with the License. A copy of the License
# is located at
#
# http://aws.amazon.com/apache2.0/
#
# or in the "license" file acc... | 13,212 | 44.878472 | 130 | py |
EntityDescriptionGeneration | EntityDescriptionGeneration-master/test/unit/test_inference.py | # Copyright 2017, 2018 Amazon.com, Inc. or its affiliates. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License"). You may not
# use this file except in compliance with the License. A copy of the License
# is located at
#
# http://aws.amazon.com/apache2.0/
#
# or in the "license" fi... | 36,544 | 50.183473 | 146 | py |
EntityDescriptionGeneration | EntityDescriptionGeneration-master/test/unit/test_operator.py | # Copyright 2018 Amazon.com, Inc. or its affiliates. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License"). You may not
# use this file except in compliance with the License. A copy of the License
# is located at
#
# http://aws.amazon.com/apache2.0/
#
# or in the "license" file acc... | 1,480 | 30.510638 | 78 | py |
EntityDescriptionGeneration | EntityDescriptionGeneration-master/test/unit/test_coverage.py | # Copyright 2017 Amazon.com, Inc. or its affiliates. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License"). You may not
# use this file except in compliance with the License. A copy of the License
# is located at
#
# http://aws.amazon.com/apache2.0/
#
# or in the "license" file acc... | 7,982 | 53.678082 | 126 | py |
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