python_code stringlengths 0 992k | repo_name stringlengths 8 46 | file_path stringlengths 5 162 |
|---|---|---|
from functools import partial
from typing import List
import time
import torch
import unittest
from apex.transformer._ucc_util import HAS_UCC
from apex.transformer import parallel_state
from apex.transformer.enums import ModelType
from apex.transformer.tensor_parallel import model_parallel_cuda_manual_seed
from apex... | GeneSplice-main | GeneSplice/apex/tests/L0/run_transformer/test_gpt_minimal.py |
import torch
from torch.testing._internal import common_utils
from torch.utils.data import Dataset
from torch.utils.data import DataLoader
from apex.transformer.pipeline_parallel.utils import _split_batch_into_microbatch as split_batch_into_microbatch
class MyIterableDataset(Dataset):
def __init__(self, start, e... | GeneSplice-main | GeneSplice/apex/tests/L0/run_transformer/test_batch_sampler.py |
import logging
import unittest
import typing
import torch
import torch.nn as nn
from torch.testing._internal import common_utils
from apex.transformer import parallel_state
from apex.transformer.tensor_parallel import layers
from apex.transformer.testing.commons import set_random_seed
from apex.transformer.testing.di... | GeneSplice-main | GeneSplice/apex/tests/L0/run_transformer/test_layers.py |
GeneSplice-main | GeneSplice/apex/tests/L0/run_transformer/__init__.py | |
import logging
from typing import List, Optional
from torch.testing._internal import common_utils
logging.getLogger("torch").setLevel(logging.WARNING)
from apex.transformer import parallel_state
from apex.transformer.pipeline_parallel.utils import (
_reconfigure_microbatch_calculator,
get_micro_batch_size,
... | GeneSplice-main | GeneSplice/apex/tests/L0/run_transformer/test_microbatches.py |
import logging
import torch.testing
from torch.testing._internal import common_utils
logging.getLogger("torch").setLevel(logging.WARNING)
from apex.transformer import parallel_state
from apex.transformer.tensor_parallel import data as data_utils
from apex.transformer.testing.distributed_test_base import NcclDistribu... | GeneSplice-main | GeneSplice/apex/tests/L0/run_transformer/test_data.py |
import torch
import unittest
from apex.transformer.testing import global_vars
from apex.transformer.testing.standalone_bert import bert_model_provider
from apex.transformer.pipeline_parallel.schedules.common import (
_get_params_for_weight_decay_optimization, build_model
)
from apex.transformer.pipeline_parallel.sc... | GeneSplice-main | GeneSplice/apex/tests/L0/run_transformer/test_bert_minimal.py |
import logging
import torch
from torch.testing._internal import common_utils
logging.getLogger("torch").setLevel(logging.WARNING)
from apex.transformer import parallel_state
from apex.transformer.tensor_parallel import utils
from apex.transformer.testing.distributed_test_base import NcclDistributedTestBase
logging.... | GeneSplice-main | GeneSplice/apex/tests/L0/run_transformer/test_transformer_utils.py |
import logging
import os
from torch.testing._internal import common_utils
logging.getLogger("torch").setLevel(logging.WARNING)
from apex.transformer import parallel_state
from apex.transformer.testing.distributed_test_base import NcclDistributedTestBase
from apex.transformer.testing.distributed_test_base import UccD... | GeneSplice-main | GeneSplice/apex/tests/L0/run_transformer/test_parallel_state.py |
import contextlib
import logging
import itertools
import os
from datetime import datetime
from packaging.version import parse, Version
import re
from typing import Optional, Tuple, List
import unittest
import torch
from torch.testing._internal import common_utils
from apex._autocast_utils import _get_autocast_dtypes
... | GeneSplice-main | GeneSplice/apex/tests/L0/run_transformer/test_pipeline_parallel_fwd_bwd.py |
import logging
import torch
from torch.testing._internal import common_utils
from apex.transformer import parallel_state
from apex.transformer.tensor_parallel import mappings
from apex.transformer.testing.distributed_test_base import NcclDistributedTestBase
from apex.transformer.testing.distributed_test_base import U... | GeneSplice-main | GeneSplice/apex/tests/L0/run_transformer/test_mapping.py |
import logging
import torch
from torch.testing._internal import common_utils
logging.getLogger("torch").setLevel(logging.WARNING)
from apex.transformer import parallel_state
from apex.transformer import tensor_parallel
from apex.transformer.testing.distributed_test_base import NcclDistributedTestBase
from apex.trans... | GeneSplice-main | GeneSplice/apex/tests/L0/run_transformer/test_random.py |
import unittest
import functools as ft
import itertools as it
from apex import amp
from apex.amp import _amp_state
import torch
from torch import nn
import torch.nn.functional as F
from torch.nn import Parameter
from utils import common_init, HALF, FLOAT,\
ALWAYS_HALF, ALWAYS_FLOAT, MATCH_INPUT
class MyModel(to... | GeneSplice-main | GeneSplice/apex/tests/L0/run_amp/test_multiple_models_optimizers_losses.py |
import unittest
import functools as ft
import itertools as it
from apex import amp
import torch
from torch import nn
import torch.nn.functional as F
from utils import common_init, HALF, FLOAT,\
ALWAYS_HALF, ALWAYS_FLOAT, MATCH_INPUT
try:
import amp_C
from amp_C import multi_tensor_l2norm
from apex.multi_t... | GeneSplice-main | GeneSplice/apex/tests/L0/run_amp/test_multi_tensor_l2norm.py |
import unittest
import functools as ft
import itertools as it
from apex import amp
from apex.amp import _amp_state
import torch
from torch import nn
import torch.nn.functional as F
from torch.nn import Parameter
from utils import common_init, HALF, FLOAT,\
ALWAYS_HALF, ALWAYS_FLOAT, MATCH_INPUT
try:
import a... | GeneSplice-main | GeneSplice/apex/tests/L0/run_amp/test_fused_sgd.py |
import unittest
import functools as ft
import itertools as it
from apex import amp
from apex.amp import _amp_state
import torch
from torch import nn
import torch.nn.functional as F
from torch.nn import Parameter
from utils import common_init, HALF, FLOAT,\
ALWAYS_HALF, ALWAYS_FLOAT, MATCH_INPUT
class MyModel(to... | GeneSplice-main | GeneSplice/apex/tests/L0/run_amp/test_add_param_group.py |
GeneSplice-main | GeneSplice/apex/tests/L0/run_amp/__init__.py | |
import unittest
import itertools as it
from apex import amp
import torch
from torch import nn
import torch.nn.functional as F
from utils import common_init, HALF, FLOAT, DTYPES
class TestPromotion(unittest.TestCase):
def setUp(self):
self.handle = amp.init(enabled=True)
common_init(self)
de... | GeneSplice-main | GeneSplice/apex/tests/L0/run_amp/test_promotion.py |
import unittest
import functools as ft
import itertools as it
from apex import amp
import torch
from torch import nn
import torch.nn.functional as F
from math import floor
from utils import common_init, HALF, FLOAT,\
ALWAYS_HALF, ALWAYS_FLOAT, MATCH_INPUT
try:
import amp_C
from amp_C import multi_tensor_axp... | GeneSplice-main | GeneSplice/apex/tests/L0/run_amp/test_multi_tensor_axpby.py |
import torch
HALF = 'torch.cuda.HalfTensor'
FLOAT = 'torch.cuda.FloatTensor'
DTYPES = [torch.half, torch.float]
ALWAYS_HALF = {torch.float: HALF,
torch.half: HALF}
ALWAYS_FLOAT = {torch.float: FLOAT,
torch.half: FLOAT}
MATCH_INPUT = {torch.float: FLOAT,
torch.half: HALF}... | GeneSplice-main | GeneSplice/apex/tests/L0/run_amp/utils.py |
import unittest
from apex import amp
import random
import torch
from torch import nn
from utils import common_init, HALF
class TestRnnCells(unittest.TestCase):
def setUp(self):
self.handle = amp.init(enabled=True)
common_init(self)
def tearDown(self):
self.handle._deactivate()
d... | GeneSplice-main | GeneSplice/apex/tests/L0/run_amp/test_rnn.py |
import unittest
import functools as ft
import itertools as it
from apex import amp
import torch
from torch import nn
import torch.nn.functional as F
from utils import common_init, HALF, FLOAT,\
ALWAYS_HALF, ALWAYS_FLOAT, MATCH_INPUT
try:
import amp_C
from amp_C import multi_tensor_scale
from apex.multi_t... | GeneSplice-main | GeneSplice/apex/tests/L0/run_amp/test_multi_tensor_scale.py |
import unittest
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from apex import amp
from utils import common_init, FLOAT
class MyModel(torch.nn.Module):
def __init__(self):
super(MyModel, self).__init__()
self.conv1 = nn.Conv2d(3, 6, 3, 1, 1)
... | GeneSplice-main | GeneSplice/apex/tests/L0/run_amp/test_checkpointing.py |
import unittest
import functools as ft
import itertools as it
from apex import amp
from apex.amp import _amp_state
import torch
from torch import nn
import torch.nn.functional as F
from utils import common_init, HALF, FLOAT,\
ALWAYS_HALF, ALWAYS_FLOAT, MATCH_INPUT
def get_reference_grad(i, w, ops):
# Creati... | GeneSplice-main | GeneSplice/apex/tests/L0/run_amp/test_cache.py |
import unittest
import torch
from torch import nn
from torch.nn import Parameter
from apex import amp
from apex.parallel.LARC import LARC
from utils import common_init
class MyModel(torch.nn.Module):
def __init__(self, unique):
super(MyModel, self).__init__()
self.weight0 = Parameter(
... | GeneSplice-main | GeneSplice/apex/tests/L0/run_amp/test_larc.py |
import unittest
import functools as ft
import itertools as it
from apex import amp
import torch
from torch import nn
import torch.nn.functional as F
from utils import common_init, HALF, FLOAT,\
ALWAYS_HALF, ALWAYS_FLOAT, MATCH_INPUT
def run_layer_test(test_case, fns, expected, input_shape, test_backward=True):
... | GeneSplice-main | GeneSplice/apex/tests/L0/run_amp/test_basic_casts.py |
import unittest
import torch
import torch.nn as nn
from apex.fp16_utils import FP16Model
class DummyBlock(nn.Module):
def __init__(self):
super(DummyBlock, self).__init__()
self.conv = nn.Conv2d(10, 10, 2)
self.bn = nn.BatchNorm2d(10, affine=True)
def forward(self, x):
retu... | GeneSplice-main | GeneSplice/apex/tests/L0/run_fp16util/test_fp16util.py |
GeneSplice-main | GeneSplice/apex/tests/L0/run_fp16util/__init__.py | |
import unittest
import torch
import apex
from apex.transformer.testing.distributed_test_base import NcclDistributedTestBase
def init_model_and_optimizer():
model = torch.nn.Linear(1, 1, bias=False).cuda()
optimizer = torch.optim.SGD(model.parameters(), 1.0)
return model, optimizer
@unittest.skipUnless... | GeneSplice-main | GeneSplice/apex/tests/L0/run_deprecated/test_deprecated_warning.py |
"""Tests for c++ MLP"""
from itertools import product
from time import time
import torch
from torch import nn
from torch.testing._internal import common_utils
from torch.testing._internal.common_device_type import instantiate_device_type_tests
from torch.testing._internal.common_device_type import onlyCUDA
from apex.... | GeneSplice-main | GeneSplice/apex/tests/L0/run_mlp/test_mlp.py |
import os
import logging
import itertools
from typing import Optional, Tuple, List
import unittest
import torch
from torch.testing._internal import common_utils
from torch.testing._internal import common_cuda
from torch.testing._internal import common_distributed
from apex._autocast_utils import _get_autocast_dtypes
... | GeneSplice-main | GeneSplice/apex/tests/L1/transformer/pipeline_parallel_fwd_bwd_ucc_async.py |
import argparse
import os
import shutil
import time
import torch
import torch.nn as nn
import torch.nn.parallel
import torch.backends.cudnn as cudnn
import torch.distributed as dist
import torch.optim
import torch.utils.data
import torch.utils.data.distributed
import torchvision.transforms as transforms
import torchvi... | GeneSplice-main | GeneSplice/apex/tests/L1/common/main_amp.py |
import argparse
import torch
parser = argparse.ArgumentParser(description='Compare')
parser.add_argument('--opt-level', type=str)
parser.add_argument('--keep-batchnorm-fp32', type=str, default=None)
parser.add_argument('--loss-scale', type=str, default=None)
parser.add_argument('--fused-adam', action='store_true')
par... | GeneSplice-main | GeneSplice/apex/tests/L1/common/compare.py |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
#
# PyTorch documentation build configuration file, created by
# sphinx-quickstart on Fri Dec 23 13:31:47 2016.
#
# This file is execfile()d with the current directory set to its
# containing dir.
#
# Note that not all possible configuration values are present in this
# au... | GeneSplice-main | GeneSplice/apex/docs/source/conf.py |
from __future__ import print_function
import argparse
import os
import random
import torch
import torch.nn as nn
import torch.nn.parallel
import torch.backends.cudnn as cudnn
import torch.optim as optim
import torch.utils.data
import torchvision.datasets as dset
import torchvision.transforms as transforms
import torchv... | GeneSplice-main | GeneSplice/apex/examples/dcgan/main_amp.py |
import torch
import argparse
import os
from apex import amp
# FOR DISTRIBUTED: (can also use torch.nn.parallel.DistributedDataParallel instead)
from apex.parallel import DistributedDataParallel
parser = argparse.ArgumentParser()
# FOR DISTRIBUTED: Parse for the local_rank argument, which will be supplied
# automatica... | GeneSplice-main | GeneSplice/apex/examples/simple/distributed/distributed_data_parallel.py |
import argparse
import os
import shutil
import time
import torch
import torch.nn as nn
import torch.nn.parallel
import torch.backends.cudnn as cudnn
import torch.distributed as dist
import torch.optim
import torch.utils.data
import torch.utils.data.distributed
import torchvision.transforms as transforms
import torchvi... | GeneSplice-main | GeneSplice/apex/examples/imagenet/main_amp.py |
import logging
import math
from typing import Callable, Optional, Tuple
import torch
from torch.optim.optimizer import Optimizer
log = logging.getLogger(__name__)
class DecoupledLionW(Optimizer):
"""
DecoupledLionW is an optimizer designed to improve training performance and convergence for deep learning ... | DecoupledLionW-main | lion.py |
from setuptools import setup, find_packages
setup(
name = 'decoupledLionW',
packages = find_packages(exclude=[]),
version = '0.1.2',
license='MIT',
description = 'Lion Optimizer - Pytorch',
author = 'Kye Gomez',
author_email = 'kye@apac.ai',
long_description_content_type = 'text/markdown',
url = 'htt... | DecoupledLionW-main | setup.py |
OpenBioMed-main | open_biomed/__init__.py | |
OpenBioMed-main | open_biomed/tasks/__init__.py | |
import logging
logger = logging.getLogger(__name__)
import os
import sys
sys.path.append(os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__)))))
import argparse
import json
import math
from tqdm import tqdm
import torch
from torch.autograd import Variable
from torch.utils.data import DataLoader... | OpenBioMed-main | open_biomed/tasks/multi_modal_task/mtr.py |
import logging
logger = logging.getLogger(__name__)
import os
os.environ["TOKENIZERS_PARALLELISM"] = "false"
import sys
sys.path.append(os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__)))))
import argparse
import json
from tqdm import tqdm
import numpy as np
import torch
from torch.utils.data... | OpenBioMed-main | open_biomed/tasks/multi_modal_task/text2smigen.py |
import logging
logger = logging.getLogger(__name__)
import os
os.environ["TOKENIZERS_PARALLELISM"] = "false"
import sys
sys.path.append(os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__)))))
import argparse
import json
from tqdm import tqdm
import numpy as np
import torch
from torch.utils.data... | OpenBioMed-main | open_biomed/tasks/multi_modal_task/molqa.py |
"""
import logging
logger = logging.getLogger(__name__)
import os
import sys
sys.path.append(os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__)))))
import argparse
import json
import math
import numpy as np
import pickle
from rdkit import Chem
from rdkit.Chem import Draw, Descriptors
import t... | OpenBioMed-main | open_biomed/tasks/multi_modal_task/moledit.py |
import logging
logger = logging.getLogger(__name__)
import os
os.environ["TOKENIZERS_PARALLELISM"] = "false"
import sys
sys.path.append(os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__)))))
import argparse
import json
from tqdm import tqdm
import numpy as np
import torch
import torch.nn as nn... | OpenBioMed-main | open_biomed/tasks/multi_modal_task/molcap.py |
import logging
logger = logging.getLogger(__name__)
import os
os.environ["TOKENIZERS_PARALLELISM"] = "false"
import sys
sys.path.append(os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__)))))
import random
import argparse
import json
import numpy as np
from tqdm import tqdm
import torch
import ... | OpenBioMed-main | open_biomed/tasks/prot_task/ppi.py |
import logging
logger = logging.getLogger(__name__)
import os
import sys
sys.path.append(os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__)))))
import argparse
import json
from tqdm import tqdm
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.d... | OpenBioMed-main | open_biomed/tasks/cell_task/ctc.py |
import logging
logger = logging.getLogger(__name__)
import os
import sys
sys.path.append(os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__)))))
import argparse
import copy
import math
import numpy as np
import json
from tqdm import tqdm
import torch
import torch.nn as nn
import torch.nn.functi... | OpenBioMed-main | open_biomed/tasks/mol_task/drp.py |
import logging
logger = logging.getLogger(__name__)
import os
import sys
sys.path.append(os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__)))))
import random
import argparse
import json
import numpy as np
from tqdm import tqdm
import torch
import torch.nn as nn
import torch.nn.functional as F
... | OpenBioMed-main | open_biomed/tasks/mol_task/dti.py |
import logging
logger = logging.getLogger(__name__)
import os
import sys
sys.path.append(os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__)))))
import argparse
import json
from tqdm import tqdm
import random
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
... | OpenBioMed-main | open_biomed/tasks/mol_task/dp.py |
OpenBioMed-main | open_biomed/tasks/mol_task/__init__.py | |
import logging
logger = logging.getLogger(__name__)
from abc import ABC, abstractmethod
import os
import csv
import torch
from torch.utils.data import Dataset
from feature.mol_featurizer import MolMultiModalFeaturizer
from feature.text_featurizer import TextTransformerTokFeaturizer
from utils.mol_utils import valid... | OpenBioMed-main | open_biomed/datasets/molcap_dataset.py |
from abc import ABC, abstractmethod
import logging
logger = logging.getLogger(__name__)
import copy
import numpy as np
import json
import os.path as osp
from tqdm import tqdm
import torch
from torch.utils.data import Dataset
from torch_geometric.data import Data
from feature.protein_featurizer import SUPPORTED_PROTE... | OpenBioMed-main | open_biomed/datasets/ppi_dataset.py |
import logging
logger = logging.getLogger(__name__)
from abc import ABC, abstractmethod
import os
import csv
import json
import torch
from torch.utils.data import Dataset
from feature.mol_featurizer import SUPPORTED_MOL_FEATURIZER
from feature.text_featurizer import SUPPORTED_TEXT_FEATURIZER
from utils.mol_utils im... | OpenBioMed-main | open_biomed/datasets/molqa_dataset.py |
OpenBioMed-main | open_biomed/datasets/__init__.py | |
"""
Dataset for Molecule-Text Retrieval
"""
from abc import ABC, abstractmethod
import logging
logger = logging.getLogger(__name__)
import os
import os.path as osp
import copy
import random
import rdkit.Chem as Chem
from rdkit import RDLogger
RDLogger.DisableLog("rdApp.*")
import numpy as np
import torch
from torch.u... | OpenBioMed-main | open_biomed/datasets/mtr_dataset.py |
from abc import ABC, abstractmethod
import logging
logger = logging.getLogger(__name__)
import os
import copy
import scanpy
import numpy as np
from sklearn.model_selection import StratifiedShuffleSplit
import torch
from torch.utils.data import Dataset
from feature.cell_featurizer import SUPPORTED_CELL_FEATURIZER
cl... | OpenBioMed-main | open_biomed/datasets/ctc_dataset.py |
from abc import ABC, abstractmethod
import torch
from torch.utils.data import Dataset
from feature.text_featurizer import SUPPORTED_TEXT_FEATURIZER
class Text2MolGenDataset(Dataset, ABC):
def __init__(self, path, config):
super(Text2MolGenDataset, self).__init__()
self.path = path
self.co... | OpenBioMed-main | open_biomed/datasets/text2mol_dataset.py |
from abc import ABC, abstractmethod
import logging
logger = logging.getLogger(__name__)
import copy
import pickle
import json
import random
import numpy as np
import pandas as pd
import os.path as osp
import torch
from torch.utils.data import Dataset
from torch_geometric.data import Batch
from feature.mol_featurizer... | OpenBioMed-main | open_biomed/datasets/drp_dataset.py |
from abc import ABC, abstractmethod
import logging
logger = logging.getLogger(__name__)
import copy
from enum import Enum
import numpy as np
import pandas as pd
import os
from rdkit import Chem
import os
import sys
import torch
from torch.utils.data import Dataset
from rdkit.Chem import AllChem, Descriptors
from fea... | OpenBioMed-main | open_biomed/datasets/dp_dataset.py |
from abc import ABC, abstractmethod
import logging
logger = logging.getLogger(__name__)
import copy
import numpy as np
import pandas as pd
import pickle
import os
import json
from tqdm import tqdm
from collections import OrderedDict
import torch
from torch.utils.data import Dataset
from feature.mol_featurizer import... | OpenBioMed-main | open_biomed/datasets/dti_dataset.py |
import numpy as np
import sklearn.metrics as metrics
def roc_auc(y_true, y_pred):
fpr, tpr, _ = metrics.roc_curve(y_true, y_pred)
roc_auc = metrics.auc(fpr, tpr)
return roc_auc
def pr_auc(y_true, y_pred):
precision, recall, _ = metrics.precision_recall_curve(y_true, y_pred)
pr_auc = metrics.auc(re... | OpenBioMed-main | open_biomed/utils/metrics.py |
import logging
logger = logging.getLogger(__name__)
import math
import numpy as np
from rdkit import Chem
from rdkit.Chem.Scaffolds.MurckoScaffold import MurckoScaffoldSmiles
import json
import collections
from utils.cluster import cluster_with_sim_matrix, merge_cluster
from utils.prot_utils import get_normalized_ctd... | OpenBioMed-main | open_biomed/utils/split_utils.py |
import numpy as np
from tqdm import tqdm
def to_clu_sparse(data):
s = "%d %d %d" % (data.shape[0], data.shape[1], np.sum(data))
s_row = [""] * data.shape[0]
non_zero_row, non_zero_col = np.where(data > 0)
for i in tqdm(range(len(non_zero_row))):
s_row[non_zero_row[i]] += " %d %f" % (non_zero_co... | OpenBioMed-main | open_biomed/utils/matrix_utils.py |
import math
import torch
from torch.optim.lr_scheduler import _LRScheduler
class CosineAnnealingWarmupRestarts(_LRScheduler):
"""
optimizer (Optimizer): Wrapped optimizer.
first_cycle_steps (int): First cycle step size.
cycle_mult(float): Cycle steps magnification. Default: -1.
max... | OpenBioMed-main | open_biomed/utils/schedulars.py |
#import os
#import sys
#sys.path.append(os.path.dirname(__file__))
import os
import numpy as np
import random
import torch
import datetime
from utils.distributed_utils import *
from utils.metrics import *
from utils.mol_utils import *
from utils.cell_utils import *
from utils.kg_utils import *
from utils.matrix_utils... | OpenBioMed-main | open_biomed/utils/__init__.py |
import math
import torch
from torch.optim import Optimizer
from torch.optim.optimizer import required
from torch.nn.utils import clip_grad_norm_
def warmup_cosine(x, warmup=0.002):
if x < warmup:
return x/warmup
return 0.5 * (1.0 + torch.cos(math.pi * x))
def warmup_constant(x, warmup=0.002):
if ... | OpenBioMed-main | open_biomed/utils/optimizers.py |
from feature.mol_featurizer import SUPPORTED_MOL_FEATURIZER, MolMultiModalFeaturizer
from feature.protein_featurizer import SUPPORTED_PROTEIN_FEATURIZER, ProteinMultiModalFeaturizer
from feature.cell_featurizer import SUPPORTED_CELL_FEATURIZER
from feature.text_featurizer import SUPPORTED_TEXT_FEATURIZER
from utils.col... | OpenBioMed-main | open_biomed/utils/data_utils.py |
import logging
logger = logging.getLogger(__name__)
from abc import ABC, abstractmethod
import os
import json
import pandas as pd
import numpy as np
import pickle
from tqdm import tqdm
import random
import torch
from rdkit import Chem
from utils.cell_utils import load_hugo2ncbi
class KG(object):
def __init__(s... | OpenBioMed-main | open_biomed/utils/kg_utils.py |
import logging
logger = logging.getLogger(__name__)
from abc import ABC, abstractmethod
import numpy as np
def load_hugo2ncbi():
ncbi2hugo = {}
hugo2ncbi = {}
try:
with open("../assets/drp/enterez_NCBI_to_hugo_gene_symbol_march_2019.txt", "r") as f:
for line in f.readlines():
... | OpenBioMed-main | open_biomed/utils/cell_utils.py |
import logging
logger = logging.getLogger(__name__)
import numpy as np
class UFS(object):
def __init__(self, n):
self.fa = list(range(n))
def merge(self, x, y):
self.fa[x] = self.find(y)
def find(self, x):
self.fa[x] = self.find(self.fa[x]) if self.fa[x] != x else x
re... | OpenBioMed-main | open_biomed/utils/cluster.py |
import os
import torch
import torch.distributed as dist
def is_dist_avail_and_initialized():
if not dist.is_available():
return False
if not dist.is_initialized():
return False
return True
def is_main_process():
return get_rank() == 0
def get_rank():
if not is_dist_avail_and_init... | OpenBioMed-main | open_biomed/utils/distributed_utils.py |
import numpy as np
import math
def get_normalized_ctd(proteins):
from PyBioMed.PyProtein import CTD
ctds = []
for prot in proteins:
ctds.append(np.array(list(CTD.CalculateCTD(prot).values())))
ctds = np.array(ctds)
for i in range(ctds.shape[1]):
mean = np.mean(ctds[:, i])
va... | OpenBioMed-main | open_biomed/utils/prot_utils.py |
from abc import ABC, abstractmethod
import torch
from torch_geometric.data import Data, Batch
from transformers import BatchEncoding, DataCollatorWithPadding, BertTokenizer, T5Tokenizer, GPT2Tokenizer, EsmTokenizer
from utils.mol_utils import SmilesTokenizer
name2tokenizer = {
"bert": BertTokenizer,
"t5": T5T... | OpenBioMed-main | open_biomed/utils/collators.py |
import os
import sys
sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
import torch
import logging
logger = logging.getLogger(__name__)
import re
from utils.data_utils import DataProcessorFast
from utils.mol_utils import valid_smiles
from utils.collators import ToDevice
class Conversatio... | OpenBioMed-main | open_biomed/utils/chat_utils.py |
import logging
logger = logging.getLogger(__name__)
import argparse
import csv
import collections
import json
import numpy as np
import os
import pickle
import re
from typing import List, Optional
import rdkit.Chem as Chem
from rdkit.Chem import MolStandardize
from rdkit import RDLogger
RDLogger.DisableLog("rdApp.*")... | OpenBioMed-main | open_biomed/utils/mol_utils.py |
import torch.nn as nn
activation = {
"sigmoid": nn.Sigmoid(),
"softplus": nn.Softplus(),
"relu": nn.ReLU(),
"gelu": nn.GELU(),
"tanh": nn.Tanh(),
}
class MLP(nn.Module):
def __init__(self, config, input_dim, output_dim):
super(MLP, self).__init__()
self.model = nn.Sequential()
... | OpenBioMed-main | open_biomed/models/predictor.py |
from abc import ABC, abstractmethod
import torch.nn as nn
class MolEncoder(nn.Module, ABC):
def __init__(self):
super(MolEncoder, self).__init__()
@abstractmethod
def encode_mol(self, mol):
raise NotImplementedError
class ProteinEncoder(nn.Module, ABC):
def __init__(self):
sup... | OpenBioMed-main | open_biomed/models/base_models.py |
from models.molecule import *
from models.protein import *
from models.cell import *
from models.knowledge import *
from models.text import *
from models.multimodal import *
SUPPORTED_MOL_ENCODER = {
"cnn": MolCNN,
"tgsa": GINTGSA,
"graphcl": GraphCL,
"graphmvp": GraphMVP,
"molclr": MolCLR,
"mg... | OpenBioMed-main | open_biomed/models/__init__.py |
from typing import OrderedDict
import torch
import torch.nn as nn
import torch.nn.functional as F
from models.base_models import ProteinEncoder
class Conv1dReLU(nn.Module):
'''
kernel_size=3, stride=1, padding=1
kernel_size=5, stride=1, padding=2
kernel_size=7, stride=1, padding=3
'''
def __i... | OpenBioMed-main | open_biomed/models/protein/mcnn.py |
from models.protein.cnn import ProtCNN, CNNGRU, CNNPIPR
from models.protein.mcnn import MCNN
from models.protein.prottrans import ProtTrans
| OpenBioMed-main | open_biomed/models/protein/__init__.py |
import math
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.autograd import Variable
from models.base_models import ProteinEncoder
class ProtCNN(ProteinEncoder):
def __init__(self, config):
super(ProtCNN, self).__init__()
self.output_dim = config["output_dim"]
... | OpenBioMed-main | open_biomed/models/protein/cnn.py |
import torch
import torch.nn as nn
from transformers import AutoModel
from models.base_models import ProteinEncoder
class ProtTrans(ProteinEncoder):
def __init__(self, config):
super(ProtTrans, self).__init__()
self.max_length = config["max_length"]
self.output_dim = config["output_dim"]
... | OpenBioMed-main | open_biomed/models/protein/prottrans.py |
import logging
logger = logging.getLogger(__name__)
import json
import torch
import torch.nn as nn
from torch_geometric.data import Batch
from transformers.modeling_outputs import BaseModelOutput
from models import SUPPORTED_MOL_ENCODER, SUPPORTED_TEXT_ENCODER, SUPPORTED_TEXT_DECODER
from models.multimodal import KV... | OpenBioMed-main | open_biomed/models/task_model/molqa_model.py |
import json
import torch
import torch.nn as nn
from models import SUPPORTED_MOL_ENCODER, SUPPORTED_TEXT_ENCODER
class MTRModel(nn.Module):
def __init__(self, config):
super(MTRModel, self).__init__()
mol_config = json.load(open(config["structure"]["config_path"], "r"))
text_config = json.... | OpenBioMed-main | open_biomed/models/task_model/mtr_model.py |
import torch
import torch.nn as nn
from models import SUPPORTED_CELL_ENCODER
class CTCModel(nn.Module):
def __init__(self, config, num_labels):
super(CTCModel, self).__init__()
self.encoder_name = config["structure"]["name"]
self.encoder = SUPPORTED_CELL_ENCODER[config["structure"]["name"]... | OpenBioMed-main | open_biomed/models/task_model/ctc_model.py |
import torch
import torch.nn as nn
from transformers.modeling_outputs import BaseModelOutput
from models.multimodal.molt5 import MolT5
from models import SUPPORTED_MOL_ENCODER
from utils.mol_utils import convert_pyg_batch
class MolCapModel(nn.Module):
def __init__(self, config):
super(MolCapModel, self)... | OpenBioMed-main | open_biomed/models/task_model/molcap_model.py |
import torch
import torch.nn as nn
from models.molecule.gin_tgsa import GINTGSA
from models.cell import CellGAT
from models.cell import SUPPORTED_CELL_ENCODER
class ConvPooler(nn.Module):
def __init__(self, dim, full_seq_len):
super().__init__()
self.full_seq_len = full_seq_len
self.pad_gen... | OpenBioMed-main | open_biomed/models/task_model/drp_model.py |
import json
import torch
import torch.nn as nn
from transformers.modeling_outputs import BaseModelOutput
from models import SUPPORTED_TEXT_ENCODER
from models.multimodal.molt5 import MolT5
class Text2SMILESModel(nn.Module):
def __init__(self, config):
super(Text2SMILESModel, self).__init__()
self... | OpenBioMed-main | open_biomed/models/task_model/text2smi_model.py |
import logging
logger = logging.getLogger(__name__)
import torch
import torch.nn as nn
import json
from transformers import AutoModel
from models import SUPPORTED_MOL_ENCODER, SUPPORTED_PROTEIN_ENCODER
from models.predictor import MLP
class DTIModel(nn.Module):
def __init__(self, config, pred_dim):
super... | OpenBioMed-main | open_biomed/models/task_model/dti_model.py |
import torch
import torch.nn as nn
import json
from transformers import AutoModel
from models import SUPPORTED_MOL_ENCODER
from models.multimodal.molfm.molfm import MolFM
activation = {
"sigmoid": nn.Sigmoid(),
"softplus": nn.Softplus(),
"relu": nn.ReLU(),
"gelu": nn.GELU(),
"tanh": nn.Tanh(),
}
... | OpenBioMed-main | open_biomed/models/task_model/dp_model.py |
import json
import torch
import torch.nn as nn
from models import SUPPORTED_PROTEIN_ENCODER, SUPPORTED_KNOWLEDGE_ENCODER
from models.predictor import MLP
class PPISeqModel(nn.Module):
def __init__(self, config, num_classes):
super(PPISeqModel, self).__init__()
protein_encoder_config = json.load(o... | OpenBioMed-main | open_biomed/models/task_model/ppi_model.py |
import torch
import torch.nn as nn
class DeepCDR(torch.nn.Module):
def __init__(self, input_dim, output_dim=100, **kwargs):
super().__init__()
self.linear1 = nn.Linear(input_dim, 256)
self.dropout = nn.Dropout(0.1)
self.norm = nn.BatchNorm1d(256)
self.linear2 = nn.Linear(256... | OpenBioMed-main | open_biomed/models/cell/deepcdr.py |
import math
import numpy as np
import torch
import torch.nn.functional as F
from torch import nn
from torch.cuda.amp import autocast
from torch.autograd.function import Function
from torch.utils.checkpoint import get_device_states, set_device_states
from einops import rearrange, repeat
from operator import itemgetter
... | OpenBioMed-main | open_biomed/models/cell/performer.py |
from models.cell.gat import CellGAT
from models.cell.performer import PerformerLM
from models.cell.performer_celllm import PerformerLM_CellLM
from models.cell.deepcdr import DeepCDR
SUPPORTED_CELL_ENCODER = {
"scbert": PerformerLM,
"celllm": PerformerLM_CellLM,
"gat": CellGAT,
"deepcdr": DeepCDR
} | OpenBioMed-main | open_biomed/models/cell/__init__.py |
import math
import numpy as np
import torch
import torch.nn.functional as F
from torch import nn
from torch.cuda.amp import autocast
from torch.autograd.function import Function
from torch.utils.checkpoint import get_device_states, set_device_states
from einops import rearrange, repeat
from operator import itemgetter
... | OpenBioMed-main | open_biomed/models/cell/performer_celllm.py |
import torch
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
from torch_geometric.nn import GATConv, max_pool
class CellGAT(torch.nn.Module):
def __init__(self, num_feature, layer_cell, dim_cell, cluster_predefine):
super().__init__()
self.num_feature = num_feature
... | OpenBioMed-main | open_biomed/models/cell/gat.py |
from models.multimodal.bert import MolBERT
from models.multimodal.biomedgpt import BioMedGPTCLIP, BioMedGPTV
from models.multimodal.kv_plm import KVPLM
from models.multimodal.momu import MoMu
from models.multimodal.molfm.molfm import MolFM
from models.multimodal.molfm.drugfm import DrugFM
from models.multimodal.molt5 i... | OpenBioMed-main | open_biomed/models/multimodal/__init__.py |
import torch
import torch.nn as nn
from transformers import T5Tokenizer, T5ForConditionalGeneration
from models.base_models import MolEncoder, TextEncoder
class MolT5(MolEncoder, TextEncoder):
def __init__(self, config):
super(MolT5, self).__init__()
self.main_model = T5ForConditionalGeneration.fr... | OpenBioMed-main | open_biomed/models/multimodal/molt5.py |
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