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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mmf | mmf-main/mmf_cli/run.py | #!/usr/bin/env python3 -u
# Copyright (c) Facebook, Inc. and its affiliates.
import argparse
import logging
import random
import typing
import torch
from mmf.common.registry import registry
from mmf.utils.build import build_config, build_trainer
from mmf.utils.configuration import Configuration
from mmf.utils.distribu... | 4,682 | 32.934783 | 87 | py |
mmf | mmf-main/tests/test_utils.py | # Copyright (c) Facebook, Inc. and its affiliates.
import argparse
import contextlib
import itertools
import json
import os
import platform
import random
import socket
import tempfile
import unittest
from dataclasses import dataclass
from typing import Callable, Dict, List, Optional
import torch
from mmf.common.regis... | 10,165 | 28.638484 | 87 | py |
mmf | mmf-main/tests/modules/test_layers.py | # Copyright (c) Facebook, Inc. and its affiliates.
import functools
import operator
import random
import unittest
import mmf.modules.layers as layers
import numpy as np
import torch
from omegaconf import OmegaConf
class TestModuleLayers(unittest.TestCase):
def setUp(self):
torch.manual_seed(1234)
de... | 3,888 | 32.817391 | 86 | py |
mmf | mmf-main/tests/modules/test_vit.py | # Copyright (c) Facebook, Inc. and its affiliates.
import unittest
import torch
from mmf.modules.vit import ViTModel
from omegaconf import OmegaConf
from tests.test_utils import setup_proxy, skip_if_old_transformers
from torch import nn
@skip_if_old_transformers(min_version="4.5.0")
class TestViT(unittest.TestCase)... | 2,036 | 32.95 | 87 | py |
mmf | mmf-main/tests/modules/test_fusions.py | # Copyright (c) Facebook, Inc. and its affiliates.
import unittest
import mmf.modules.fusions as fusions
import torch
class TestModuleFusions(unittest.TestCase):
def setUp(self):
bsize = 2
self.x = [torch.randn(bsize, 10), torch.randn(bsize, 20)]
self.input_dims = [self.x[0].shape[-1], s... | 3,468 | 35.515789 | 88 | py |
mmf | mmf-main/tests/modules/test_encoders.py | # Copyright (c) Facebook, Inc. and its affiliates.
import tempfile
import unittest
import torch
from mmf.modules import encoders
from omegaconf import OmegaConf
from tests.test_utils import (
setup_proxy,
skip_if_no_pytorchvideo,
skip_if_old_transformers,
)
from torch import nn
class TestEncoders(unitte... | 6,036 | 35.587879 | 87 | py |
mmf | mmf-main/tests/modules/test_losses.py | # Copyright (c) Facebook, Inc. and its affiliates.
import collections
import unittest
from unittest.mock import MagicMock
import mmf.modules.losses as losses
import torch
import torch.nn as nn
import torch.nn.functional as F
from mmf.common.registry import registry
from mmf.common.sample import SampleList
RETURN_VAL... | 8,109 | 34.884956 | 88 | py |
mmf | mmf-main/tests/modules/test_metrics.py | # Copyright (c) Facebook, Inc. and its affiliates.
import os
import unittest
import mmf.modules.metrics as metrics
import torch
from mmf.common.registry import registry
from mmf.common.sample import Sample
from mmf.datasets.processors import CaptionProcessor
from mmf.utils.configuration import load_yaml
class TestMo... | 11,838 | 34.235119 | 86 | py |
mmf | mmf-main/tests/modules/test_poolers.py | # Copyright (c) Facebook, Inc. and its affiliates.
import unittest
import mmf.modules.poolers as poolers
import torch
from mmf.utils.general import get_current_device
class TestModulePoolers(unittest.TestCase):
def setUp(self):
self.k = 2
self.batch_size = 64
self.num_tokens = 10
... | 2,126 | 32.234375 | 87 | py |
mmf | mmf-main/tests/modules/test_optimizers.py | # Copyright (c) Facebook, Inc. and its affiliates.
import gc
import unittest
import torch
from mmf.models.mmbt import MMBT
from mmf.modules.hf_layers import undo_replace_with_jit
from mmf.utils.build import build_optimizer
from omegaconf import OmegaConf
from tests.test_utils import SimpleModel, skip_if_no_network
... | 1,312 | 30.261905 | 69 | py |
mmf | mmf-main/tests/common/test_batch_collator.py | # Copyright (c) Facebook, Inc. and its affiliates.
import unittest
import tests.test_utils as test_utils
import torch
from mmf.common.batch_collator import BatchCollator
from mmf.common.sample import Sample
class TestBatchCollator(unittest.TestCase):
def test_call(self):
batch_collator = BatchCollator("... | 1,154 | 31.083333 | 78 | py |
mmf | mmf-main/tests/common/test_meter.py | # Copyright (c) Facebook, Inc. and its affiliates.
import unittest
import torch
from mmf.common.meter import Meter
from mmf.common.report import Report
from mmf.common.sample import SampleList
class TestMeter(unittest.TestCase):
def test_meter_update_from_report(self):
meter = Meter()
prepared_ba... | 800 | 29.807692 | 74 | py |
mmf | mmf-main/tests/common/test_sample.py | # Copyright (c) Facebook, Inc. and its affiliates.
import unittest
import tests.test_utils as test_utils
import torch
from mmf.common.sample import (
convert_batch_to_sample_list,
Sample,
SampleList,
to_device,
)
class TestSample(unittest.TestCase):
def test_sample_working(self):
initial ... | 4,059 | 32.278689 | 86 | py |
mmf | mmf-main/tests/common/test_report.py | # Copyright (c) Facebook, Inc. and its affiliates.
import unittest
import tests.test_utils as test_utils
import torch
from mmf.common.report import Report
from mmf.common.sample import SampleList
class TestReport(unittest.TestCase):
def _build_report(self):
tensor_a = torch.tensor([[1, 2, 3, 4], [2, 3, ... | 2,035 | 29.848485 | 88 | py |
mmf | mmf-main/tests/models/test_vilbert.py | # Copyright (c) Facebook, Inc. and its affiliates.
import gc
import unittest
import tests.test_utils as test_utils
import torch
from mmf.modules.hf_layers import undo_replace_with_jit
from mmf.utils.build import build_model
from mmf.utils.configuration import Configuration
from mmf.utils.env import setup_imports, tea... | 5,729 | 40.521739 | 85 | py |
mmf | mmf-main/tests/models/test_uniter.py | # Copyright (c) Facebook, Inc. and its affiliates.
import gc
import unittest
import tests.test_utils as test_utils
import torch
from mmf.common.sample import SampleList
from mmf.models.uniter import (
UNITERForClassification,
UNITERForPretraining,
UNITERImageEmbeddings,
UNITERModelBase,
)
from mmf.util... | 10,576 | 36.112281 | 88 | py |
mmf | mmf-main/tests/models/test_mmf_transformer.py | # Copyright (c) Facebook, Inc. and its affiliates.
import gc
import unittest
import tests.test_utils as test_utils
import torch
from mmf.common.sample import SampleList
from mmf.models.mmf_transformer import MMFTransformer, MMFTransformerModalityConfig
from mmf.models.transformers.heads.mlm import MLM
from mmf.module... | 20,465 | 37.834915 | 87 | py |
mmf | mmf-main/tests/models/test_mmbt.py | # Copyright (c) Facebook, Inc. and its affiliates.
import gc
import unittest
import tests.test_utils as test_utils
import torch
from mmf.common.sample import Sample, SampleList
from mmf.models.mmbt import MMBT
from mmf.modules.encoders import (
ImageEncoderFactory,
ImageEncoderTypes,
ResNet152ImageEncoder... | 5,482 | 34.836601 | 88 | py |
mmf | mmf-main/tests/models/test_albef.py | # Copyright (c) Facebook, Inc. and its affiliates.
import unittest
import torch
from mmf.models.albef.vit import AlbefVitEncoder
from omegaconf import OmegaConf
from tests.test_utils import setup_proxy
from torch import nn
class TestAlbefEncoders(unittest.TestCase):
def setUp(self):
setup_proxy()
de... | 728 | 28.16 | 63 | py |
mmf | mmf-main/tests/models/test_cnn_lstm.py | # Copyright (c) Facebook, Inc. and its affiliates.
import gc
import os
import unittest
import numpy as np
import torch
from mmf.common.registry import registry
from mmf.common.sample import Sample, SampleList
from mmf.models.cnn_lstm import CNNLSTM
from mmf.utils.configuration import Configuration
from mmf.utils.gener... | 2,414 | 31.635135 | 72 | py |
mmf | mmf-main/tests/models/test_vinvl.py | # Copyright (c) Facebook, Inc. and its affiliates.
import unittest
import tests.test_utils as test_utils
import torch
from mmf.common.sample import SampleList
from mmf.models.vinvl import VinVLBase, VinVLForClassification, VinVLForPretraining
from mmf.utils.build import build_model
from mmf.utils.configuration import... | 7,591 | 37.343434 | 84 | py |
mmf | mmf-main/tests/models/test_vilt.py | # Copyright (c) Facebook, Inc. and its affiliates.
import gc
import unittest
import tests.test_utils as test_utils
import torch
from mmf.common.sample import SampleList
from mmf.models.vilt import ViLTImageEmbedding, ViLTTextEmbedding
from mmf.utils.build import build_model
from mmf.utils.configuration import Configu... | 3,369 | 33.742268 | 77 | py |
mmf | mmf-main/tests/models/test_visual_bert.py | # Copyright (c) Facebook, Inc. and its affiliates.
import gc
import unittest
import tests.test_utils as test_utils
import torch
from mmf.common.sample import SampleList
from mmf.modules.hf_layers import replace_with_jit, undo_replace_with_jit
from mmf.utils.build import build_model
from mmf.utils.configuration import... | 3,348 | 32.828283 | 84 | py |
mmf | mmf-main/tests/models/interfaces/test_interfaces.py | # Copyright (c) Facebook, Inc. and its affiliates.
import os
import shutil
import tempfile
import unittest
from pathlib import Path
import numpy as np
import tests.test_utils as test_utils
import torch
from mmf.models.mmbt import MMBT
from mmf.modules.hf_layers import undo_replace_with_jit
from mmf.utils.configuratio... | 3,199 | 36.209302 | 85 | py |
mmf | mmf-main/tests/models/transformers/test_heads.py | # Copyright (c) Facebook, Inc. and its affiliates.
import unittest
import torch
from mmf.common.sample import Sample
from mmf.models.transformers.heads.contrastive import ThreeWayContrastive
from mmf.models.transformers.heads.itm import ITM
from mmf.models.transformers.heads.mlm import MLM
from mmf.models.transformer... | 12,458 | 36.414414 | 88 | py |
mmf | mmf-main/tests/models/transformers/test_heads_dict.py | # Copyright (c) Facebook, Inc. and its affiliates.
import unittest
import torch
from mmf.common.sample import Sample
from mmf.models.transformers.heads.utils import build_heads_dict, HeadsDict
from mmf.modules.losses import MMFLoss
from omegaconf import OmegaConf
class TestHeadsDict(unittest.TestCase):
def setU... | 4,159 | 41.44898 | 85 | py |
mmf | mmf-main/tests/datasets/test_multi_dataset_loader.py | # Copyright (c) Facebook, Inc. and its affiliates.
import unittest
from collections import Counter
import numpy as np
import torch
from mmf.common.registry import registry
from mmf.datasets.multi_dataset_loader import MultiDatasetLoader
from omegaconf import OmegaConf
from torch.utils.data import DataLoader
from ..te... | 3,955 | 34.63964 | 87 | py |
mmf | mmf-main/tests/datasets/test_mmf_dataset_builder.py | # Copyright (c) Facebook, Inc. and its affiliates.
import functools
import os
import unittest
from unittest.mock import MagicMock
import torch
from mmf.datasets.base_dataset import BaseDataset
from mmf.datasets.mmf_dataset_builder import MMFDatasetBuilder
from mmf.utils.general import get_current_device
from omegaconf... | 2,749 | 33.375 | 79 | py |
mmf | mmf-main/tests/datasets/test_prediction_processors.py | # Copyright (c) Facebook, Inc. and its affiliates.
import unittest
import torch
from mmf.common.report import Report
from mmf.common.sample import SampleList
from mmf.datasets.processors.prediction_processors import ArgMaxPredictionProcessor
class TestDatasetProcessors(unittest.TestCase):
def setUp(self):
... | 909 | 30.37931 | 83 | py |
mmf | mmf-main/tests/datasets/test_bert_processors.py | # Copyright (c) Facebook, Inc. and its affiliates.
import unittest
import tests.test_utils as test_utils
import torch
from omegaconf import OmegaConf
class TestBERTProcessors(unittest.TestCase):
def setUp(self):
self.config = OmegaConf.create(
{
"tokenizer_config": {
... | 21,144 | 43.893843 | 87 | py |
mmf | mmf-main/tests/datasets/test_multi_datamodule.py | # Copyright (c) Facebook, Inc. and its affiliates.
import functools
import torch
from mmf.datasets.lightning_multi_datamodule import LightningMultiDataModule
from mmf.datasets.mmf_dataset_builder import MMFDatasetBuilder
from mmf.datasets.multi_datamodule import MultiDataModule
from omegaconf import OmegaConf
from tes... | 3,410 | 33.806122 | 76 | py |
mmf | mmf-main/tests/datasets/test_iteration_strategies.py | # Copyright (c) Facebook, Inc. and its affiliates.
import unittest
from collections import Counter
import numpy as np
import torch
from mmf.datasets import iteration_strategies
from tests.test_utils import NumbersDataset
class TestIterationStrategies(unittest.TestCase):
NUM_DATALOADERS = 5
def setUp(self):... | 5,479 | 31.046784 | 86 | py |
mmf | mmf-main/tests/datasets/test_processors.py | # Copyright (c) Facebook, Inc. and its affiliates.
import os
import tempfile
import unittest
import torch
from mmf.datasets.processors.image_processors import VILTImageProcessor
from mmf.datasets.processors.processors import (
CaptionProcessor,
EvalAIAnswerProcessor,
MultiClassFromFile,
MultiHotAnswerF... | 8,174 | 36.672811 | 87 | py |
mmf | mmf-main/tests/utils/test_checkpoint.py | # Copyright (c) Facebook, Inc. and its affiliates.
import contextlib
import os
import tempfile
import unittest
from copy import deepcopy
from io import StringIO
from unittest.mock import Mock, patch
import torch
from mmf.common.registry import registry
from mmf.models.base_model import BaseModel
from mmf.trainers.cal... | 22,862 | 36.236156 | 88 | py |
mmf | mmf-main/tests/utils/test_model.py | # Copyright (c) Facebook, Inc. and its affiliates.
import torch
from mmf.common.registry import registry
from torch import nn
class TestDecoderModel(nn.Module):
def __init__(self, config, vocab):
super().__init__()
self.config = config
self.vocab = vocab
def build(self):
retur... | 2,641 | 35.694444 | 80 | py |
mmf | mmf-main/tests/utils/test_text.py | # Copyright (c) Facebook, Inc. and its affiliates.
import os
import unittest
import mmf.utils.text as text_utils
import numpy as np
import torch
from mmf.common.registry import registry
from mmf.common.sample import Sample, SampleList
from mmf.utils.configuration import Configuration
from mmf.utils.env import setup_im... | 7,932 | 34.573991 | 86 | py |
mmf | mmf-main/tests/utils/test_visualize.py | # Copyright (c) Facebook, Inc. and its affiliates.
import unittest
import numpy as np
import torch
from mmf.utils.features.visualizing_image import SingleImageViz
class TestVisualize(unittest.TestCase):
objids = np.array(["obj0", "obj1", "obj2", "obj3"])
attrids = np.array(["attr0", "attr1", "attr2", "attr3... | 3,313 | 27.817391 | 85 | py |
mmf | mmf-main/tests/trainers/test_trainer_mocks.py | # Copyright (c) Facebook, Inc. and its affiliates.
import functools
from unittest.mock import MagicMock
import torch
from mmf.common.registry import registry
from mmf.common.sample import SampleList
from mmf.datasets.lightning_multi_datamodule import LightningMultiDataModule
from mmf.datasets.mmf_dataset_builder impo... | 5,899 | 35.645963 | 86 | py |
mmf | mmf-main/tests/trainers/test_device.py | # Copyright (c) Facebook, Inc. and its affiliates.
import unittest
import torch
from mmf.trainers.core.device import TrainerDeviceMixin
from mmf.utils.general import get_current_device
from omegaconf import OmegaConf
class DeviceMock(TrainerDeviceMixin):
def __init__(self, config):
self.config = config
... | 867 | 28.931034 | 62 | py |
mmf | mmf-main/tests/trainers/test_sharded_ddp.py | # Copyright (c) Facebook, Inc. and its affiliates.
import os
import unittest
import torch
from mmf.common.registry import registry
from mmf.trainers.mmf_trainer import MMFTrainer
from mmf.utils.build import build_optimizer
from omegaconf import OmegaConf
from tests.test_utils import SimpleModel, skip_if_no_cuda
from ... | 4,059 | 34.304348 | 87 | py |
mmf | mmf-main/tests/trainers/test_eval_loop.py | # Copyright (c) Facebook, Inc. and its affiliates.
import unittest
from unittest.mock import MagicMock, patch
import torch
from tests.trainers.test_utils import get_config_with_defaults, get_mmf_trainer
class TestEvalLoop(unittest.TestCase):
def setUp(self):
torch.manual_seed(2)
@patch(
"mm... | 905 | 32.555556 | 79 | py |
mmf | mmf-main/tests/trainers/test_training_loop.py | # Copyright (c) Facebook, Inc. and its affiliates.
import unittest
from unittest.mock import MagicMock, patch
import torch
from mmf.common.registry import registry
from mmf.utils.general import get_batch_size
from tests.test_utils import SimpleModel, SimpleNaNLossModel
from tests.trainers.test_trainer_mocks import Tr... | 8,693 | 37.131579 | 85 | py |
mmf | mmf-main/tests/trainers/test_fp16.py | # Copyright (c) Facebook, Inc. and its affiliates.
import unittest
import torch
from tests.test_utils import SimpleModel, skip_if_no_cuda
from tests.trainers.test_training_loop import TrainerTrainingLoopMock
from tests.trainers.test_utils import get_config_with_defaults
class SimpleModelWithFp16Assert(SimpleModel):... | 2,579 | 31.658228 | 86 | py |
mmf | mmf-main/tests/trainers/test_utils.py | # Copyright (c) Facebook, Inc. and its affiliates.
import os
import torch
from mmf.utils.build import build_optimizer
from mmf.utils.configuration import load_yaml
from omegaconf import OmegaConf
from tests.test_utils import SimpleLightningModel, SimpleModel
from tests.trainers.lightning.lightning_trainer_mock import... | 4,236 | 28.22069 | 86 | py |
mmf | mmf-main/tests/trainers/callbacks/test_user_callback.py | # Copyright (c) Facebook, Inc. and its affiliates.
import argparse
import os
import unittest
from copy import deepcopy
import torch
from mmf.common.registry import registry
from mmf.trainers.callbacks.lr_scheduler import LRSchedulerCallback
from mmf.utils.configuration import load_yaml
from omegaconf import OmegaConf... | 2,409 | 32.943662 | 81 | py |
mmf | mmf-main/tests/trainers/callbacks/test_logistics.py | # Copyright (c) Facebook, Inc. and its affiliates.
import argparse
import os
import tempfile
import unittest
from copy import deepcopy
from unittest.mock import Mock
import torch
from mmf.common.meter import Meter
from mmf.common.registry import registry
from mmf.common.report import Report
from mmf.models.base_model... | 4,350 | 33.259843 | 87 | py |
mmf | mmf-main/tests/trainers/callbacks/test_lr_scheduler.py | # Copyright (c) Facebook, Inc. and its affiliates.
import argparse
import os
import unittest
from copy import deepcopy
import torch
from mmf.common.registry import registry
from mmf.models.base_model import BaseModel
from mmf.trainers.callbacks.lr_scheduler import LRSchedulerCallback
from mmf.utils.configuration impo... | 2,691 | 29.942529 | 83 | py |
mmf | mmf-main/tests/trainers/lightning/test_checkpoint.py | # Copyright (c) Facebook, Inc. and its affiliates.
import contextlib
import os
import tempfile
import unittest
from unittest.mock import patch
import torch
from mmf.trainers.callbacks.checkpoint import CheckpointCallback
from mmf.trainers.callbacks.early_stopping import EarlyStoppingCallback
from mmf.trainers.lightni... | 21,628 | 40.674374 | 87 | py |
mmf | mmf-main/tests/trainers/lightning/test_lr_schedule.py | # Copyright (c) Facebook, Inc. and its affiliates.
import unittest
from unittest.mock import MagicMock, patch
import torch
from mmf.trainers.callbacks.lr_scheduler import LRSchedulerCallback
from tests.trainers.test_utils import (
get_config_with_defaults,
get_lightning_trainer,
get_mmf_trainer,
)
class... | 2,634 | 43.661017 | 86 | py |
mmf | mmf-main/tests/trainers/lightning/test_logging.py | # Copyright (c) Facebook, Inc. and its affiliates.
import unittest
from unittest.mock import MagicMock, patch
from mmf.trainers.callbacks.logistics import LogisticsCallback
from mmf.trainers.lightning_core.loop_callback import LightningLoopCallback
from mmf.utils.timer import Timer
from tests.test_utils import skip_i... | 4,666 | 34.090226 | 87 | py |
mmf | mmf-main/tests/trainers/lightning/test_loss.py | # Copyright (c) Facebook, Inc. and its affiliates.
import unittest
from unittest.mock import MagicMock, patch
from mmf.common.report import Report
from pytorch_lightning.callbacks.base import Callback
from tests.trainers.test_utils import (
get_config_with_defaults,
get_lightning_trainer,
get_mmf_trainer,... | 1,872 | 37.22449 | 88 | py |
mmf | mmf-main/tests/trainers/lightning/test_grad_accumulate.py | # Copyright (c) Facebook, Inc. and its affiliates.
import unittest
from unittest.mock import patch
import torch
from tests.trainers.test_utils import get_config_with_defaults, get_lightning_trainer
class TestLightningTrainerGradAccumulate(unittest.TestCase):
def test_grad_accumulate(self):
with patch("m... | 1,514 | 35.95122 | 85 | py |
mmf | mmf-main/tests/trainers/lightning/test_grad_clipping.py | # Copyright (c) Facebook, Inc. and its affiliates.
import unittest
from unittest.mock import MagicMock, patch
import torch
from mmf.utils.general import clip_gradients
from pytorch_lightning.callbacks.base import Callback
from tests.trainers.test_utils import (
get_config_with_defaults,
get_lightning_trainer,... | 3,401 | 34.4375 | 88 | py |
mmf | mmf-main/tests/trainers/lightning/test_validation.py | # Copyright (c) Facebook, Inc. and its affiliates.
import gc
import unittest
from typing import Any, Dict, Optional
from unittest.mock import MagicMock, patch
from mmf.common.meter import Meter
from mmf.trainers.callbacks.logistics import LogisticsCallback
from mmf.trainers.lightning_core.loop_callback import Lightni... | 7,195 | 35.160804 | 88 | py |
mmf | mmf-main/docs/source/conf.py | #!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates.
#
# mmf documentation build configuration file, created by
# sphinx-quickstart on Tue Apr 23 10:42:55 2019.
#
# This file is execfile()d with the current directory set to its
# containing dir.
#
# Note that not all possible configuration values ... | 6,304 | 28.600939 | 80 | py |
ntga | ntga-main/plot_learning_dynamics.py | """
This code is based on Disentangling Trainability and Generalization in Deep Neural Networks (L. Xiao et al. 2020)
Paper link: https://arxiv.org/pdf/1912.13053.pdf
Colab link: https://colab.research.google.com/github/google/neural-tangents/blob/master/
notebooks/Disentangling_Trainability_and_Generalization.ipynb#sc... | 10,002 | 46.183962 | 122 | py |
ntga | ntga-main/evaluate.py | import os
import argparse
import numpy as onp
import tensorflow as tf
from utils import *
from tqdm import tqdm
# Plotting
from IPython.display import set_matplotlib_formats
set_matplotlib_formats('pdf', 'svg')
import matplotlib.pyplot as plt
import seaborn as sns
from mpl_toolkits.axes_grid1 import make_axes_locatabl... | 12,903 | 41.870432 | 110 | py |
ntga | ntga-main/generate_attack.py | import os
import argparse
import jax.numpy as np
from jax.api import grad, jit, vmap
from jax import random
from jax.config import config
config.update('jax_enable_x64', True)
from neural_tangents import stax
from models.dnn_infinite import DenseGroup
from models.cnn_infinite import ConvGroup
from attacks.projected_gr... | 12,096 | 52.290749 | 129 | py |
ntga | ntga-main/utils_jax.py | import jax.numpy as np
from jax.api import grad, jit, vmap
from jax import lax
from jax.experimental.stax import logsoftmax
from jax.config import config
config.update('jax_enable_x64', True)
from functools import partial
import neural_tangents as nt
from neural_tangents import stax
# -
# Kernel Construction
_Kernel ... | 4,994 | 31.019231 | 91 | py |
ntga | ntga-main/attacks/utils.py | import jax.numpy as np
def one_hot(x, k, dtype=np.float32):
"""Create a one-hot encoding of x of size k."""
return np.array(x[:, None] == np.arange(k), dtype)
def partial_flatten(x):
"""Flatten all but the first dimension of an ndarray."""
return np.reshape(x, (x.shape[0], -1))
def clip_eta(eta, norm... | 1,320 | 37.852941 | 109 | py |
ntga | ntga-main/attacks/fast_gradient_method.py | # -*- coding: utf-8 -*-
import jax.numpy as np
from attacks.utils import one_hot
def fast_gradient_method(model_fn, kernel_fn, grads_fn, x_train, y_train, x_test, y_test, t=None,
loss='cross-entropy', fx_train_0=0., fx_test_0=0., eps=0.3, norm=np.inf,
clip_min=None,... | 4,821 | 53.795455 | 103 | py |
ntga | ntga-main/attacks/projected_gradient_descent.py | import jax.numpy as np
from attacks.utils import clip_eta, one_hot
from attacks.fast_gradient_method import fast_gradient_method
def projected_gradient_descent(model_fn, kernel_fn, grads_fn, x_train, y_train, x_test, y_test, t=None,
loss='cross-entropy', fx_train_0=0., fx_test_0=0., ep... | 5,504 | 56.34375 | 112 | py |
ntga | ntga-main/models/dnn_relu.py | # +
import tensorflow as tf
from tensorflow.keras import layers
def DNN_ReLU(input_shape, num_classes):
inputs = tf.keras.Input(input_shape)
x = layers.Dense(512, activation='relu')(inputs)
x = layers.Dense(512, activation='relu')(x)
x = layers.Dense(512, activation='relu')(x)
x = layers.Dense(512,... | 466 | 34.923077 | 64 | py |
ntga | ntga-main/models/cnn_infinite.py | from neural_tangents import stax
def ConvBlock(channels, W_std, b_std, strides=(1, 1)):
return stax.serial(stax.Conv(out_chan=channels, filter_shape=(5, 5), strides=strides,
padding='SAME', W_std=W_std, b_std=b_std),
stax.Relu(do_backprop=True))
def ConvGr... | 990 | 44.045455 | 90 | py |
ntga | ntga-main/models/resnet.py | import tensorflow as tf
from models.residual_block import make_basic_block_layer, make_bottleneck_layer
class ResNetTypeISmall(tf.keras.Model):
def __init__(self, num_classes, layer_params):
super(ResNetTypeISmall, self).__init__()
self.conv1 = tf.keras.layers.Conv2D(filters=64,
... | 8,373 | 45.010989 | 99 | py |
ntga | ntga-main/models/cnn.py | # +
import tensorflow as tf
from tensorflow.keras import layers
def CNN(input_shape, num_classes):
inputs = tf.keras.Input(input_shape)
x = layers.Conv2D(64, (5, 5), padding='same', activation='relu')(inputs)
x = layers.MaxPool2D(pool_size=3, strides=2, padding='same')(x)
x = layers.BatchNormalization(... | 784 | 36.380952 | 76 | py |
ntga | ntga-main/models/densenet.py | import tensorflow as tf
import tensorflow.keras as keras
import tensorflow.keras.layers as layers
def dense_block(x, blocks, name, growth_rate = 32):
for i in range(blocks):
x = conv_block(x, growth_rate, name=name + '_block' + str(i + 1))
return x
def transition_block(x, reduction, name):
x = lay... | 2,454 | 45.320755 | 116 | py |
ntga | ntga-main/models/residual_block.py | import tensorflow as tf
class BasicBlock(tf.keras.layers.Layer):
def __init__(self, filter_num, stride=1):
super(BasicBlock, self).__init__()
self.conv1 = tf.keras.layers.Conv2D(filters=filter_num,
kernel_size=(3, 3),
... | 3,883 | 41.681319 | 74 | py |
ntga | ntga-main/models/dnn_infinite.py | from neural_tangents import stax
def DenseBlock(neurons, W_std, b_std):
return stax.serial(stax.Dense(neurons, W_std, b_std),
stax.Erf())
def DenseGroup(n, neurons, W_std, b_std):
"""
:param n: int. Number of layers.
:param neurons: int. Number of neurons in each layer (finite-... | 751 | 36.6 | 81 | py |
ntga | ntga-main/models/dnn.py | # +
import tensorflow as tf
from tensorflow.keras import layers
def DNN(input_shape, num_classes):
inputs = tf.keras.Input(input_shape)
x = layers.Dense(512)(inputs)
x = tf.math.erf(x)
x = layers.Dense(512)(x)
x = tf.math.erf(x)
x = layers.Dense(512)(x)
x = tf.math.erf(x)
x = layers.Den... | 455 | 25.823529 | 57 | py |
Pocket2Mol | Pocket2Mol-main/sample.py | from copy import deepcopy
import os
# import sys
# sys.path.append('.')
import shutil
import argparse
# import random
import torch
import numpy as np
from torch_geometric.data import Batch
from easydict import EasyDict
from tqdm.auto import tqdm
from rdkit import Chem
from models.maskfill import MaskFillModelVN
from m... | 12,798 | 36.979228 | 151 | py |
Pocket2Mol | Pocket2Mol-main/sample_for_pdb.py | import os
import argparse
import warnings
from easydict import EasyDict
from Bio import BiopythonWarning
from Bio.PDB.PDBParser import PDBParser
from Bio.PDB.Selection import unfold_entities
from rdkit import Chem
from utils.protein_ligand import PDBProtein
from sample import * # Import everything from `sample.py`
... | 9,326 | 39.907895 | 168 | py |
Pocket2Mol | Pocket2Mol-main/train.py | # import sys
# sys.path.append('.')
import os
import shutil
import argparse
from tqdm.auto import tqdm
import torch
from torch.nn.utils import clip_grad_norm_
import torch.utils.tensorboard
# import torch_geometric
# assert not torch_geometric.__version__.startswith('2'), 'Please use torch_geometric lower than version ... | 10,909 | 43.713115 | 182 | py |
Pocket2Mol | Pocket2Mol-main/evaluation/evaluate.py | import os
import argparse
from copy import deepcopy
import torch
from tqdm.auto import tqdm
from rdkit.Chem.QED import qed
import sys
sys.path.append('.')
from utils.reconstruct import reconstruct_from_generated_with_edges
from sascorer import compute_sa_score
from evaluation.docking import *
from utils.misc import *
... | 1,799 | 31.142857 | 98 | py |
Pocket2Mol | Pocket2Mol-main/evaluation/scoring_func.py | import os
from time import sleep
import numpy as np
import torch
from tqdm import tqdm
from copy import deepcopy
from rdkit import Chem, DataStructs
from rdkit.Chem import AllChem, Descriptors, Crippen, Lipinski
from rdkit.Chem.QED import qed
from easydict import EasyDict
from utils.reconstruct import reconstruct_from_... | 5,160 | 37.804511 | 125 | py |
Pocket2Mol | Pocket2Mol-main/models/embedding.py | import torch
from torch.nn import Module, Linear, Embedding
from torch.nn import functional as F
class AtomEmbedding(Module):
def __init__(self, in_scalar, in_vector,
out_scalar, out_vector, vector_normalizer=20.):
super().__init__()
assert in_vector == 1
self.in_scalar = ... | 959 | 39 | 91 | py |
Pocket2Mol | Pocket2Mol-main/models/sample.py | import random
import torch
import numpy as np
from torch.nn import functional as F
def add_ligand_atom_to_data(data, pos, element, bond_index, bond_type, type_map=[6,7,8,9,15,16,17]):
"""
"""
data = data.clone()
data.ligand_context_pos = torch.cat([
data.ligand_context_pos, pos.view(1, 3).to(... | 5,617 | 41.240602 | 184 | py |
Pocket2Mol | Pocket2Mol-main/models/maskfill.py | import torch
from torch.nn import Module
from torch.nn import functional as F
from .encoders import get_encoder_vn
from .fields import get_field_vn
from .common import *
from .embedding import AtomEmbedding
from .frontier import FrontierLayerVN
from .position import PositionPredictor
# from .debug import check_true_bo... | 25,232 | 51.350622 | 185 | py |
Pocket2Mol | Pocket2Mol-main/models/invariant.py | import torch
import torch.nn.functional as F
from torch.nn import Module, Linear, LeakyReLU
import numpy as np
import torch.nn as nn
from torch_geometric.nn import global_mean_pool
from math import pi as PI
EPS = 1e-6
# from utils.profile import lineprofile
class MessageModule(Module):
def __init__(self, node_sca... | 5,429 | 39.522388 | 110 | py |
Pocket2Mol | Pocket2Mol-main/models/position.py | import torch
from torch.nn import Module, Sequential
from torch.nn import functional as F
from .invariant import GVPerceptronVN, GVLinear
import math
GAUSSIAN_COEF = 1.0 / math.sqrt(2 * math.pi)
class PositionPredictor(Module):
def __init__(self, in_sca, in_vec, num_filters, n_component):
super().__init__... | 3,133 | 36.759036 | 93 | py |
Pocket2Mol | Pocket2Mol-main/models/common.py | import math
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.nn.modules.loss import _WeightedLoss
from torch_scatter import scatter_mean, scatter_add
from torch_geometric.nn import knn, knn_graph
# from torch_geometric.nn.pool import knn_graph
import numpy as np
def split_tensor_by_batch(x... | 16,336 | 37.259953 | 150 | py |
Pocket2Mol | Pocket2Mol-main/models/frontier.py | import torch
from torch.nn import Module, Sequential
from torch.nn import functional as F
from .invariant import GVPerceptronVN, GVLinear
class FrontierLayerVN(Module):
def __init__(self, in_sca, in_vec, hidden_dim_sca, hidden_dim_vec):
super().__init__()
self.net = Sequential(
GVPerc... | 640 | 28.136364 | 75 | py |
Pocket2Mol | Pocket2Mol-main/models/encoders/cftfm.py | import torch
from torch.nn import Module, ModuleList, LeakyReLU, LayerNorm
from torch_scatter import scatter_sum
from math import pi as PI
from models.common import GaussianSmearing, EdgeExpansion
from models.invariant import GVLinear, VNLeakyReLU, MessageModule
class CFTransformerEncoderVN(Module):
def __in... | 4,374 | 39.509259 | 156 | py |
Pocket2Mol | Pocket2Mol-main/models/fields/classifier.py | import torch
from torch.nn import Module, Sequential, LayerNorm
from torch_scatter import scatter_add, scatter_softmax, scatter_sum
from math import pi as PI
from ..common import GaussianSmearing, EdgeExpansion
from ..invariant import GVLinear, GVPerceptronVN, MessageModule
class SpatialClassifierVN(Module):
de... | 10,061 | 44.945205 | 158 | py |
Pocket2Mol | Pocket2Mol-main/utils/misc.py | import os
import time
import random
import logging
import torch
import numpy as np
import yaml
from easydict import EasyDict
from logging import Logger
from tqdm.auto import tqdm
class BlackHole(object):
def __setattr__(self, name, value):
pass
def __call__(self, *args, **kwargs):
return self
... | 2,706 | 25.80198 | 87 | py |
Pocket2Mol | Pocket2Mol-main/utils/data.py | import copy
import torch
import numpy as np
# from torch_geometric.data import Data, DataLoader, Batch
from torch_geometric.data import Data, Batch
from torch_geometric.loader import DataLoader
FOLLOW_BATCH = [] #['protein_element', 'ligand_context_element', 'pos_real', 'pos_fake']
class ProteinLigandData(Data):
... | 4,038 | 36.055046 | 199 | py |
Pocket2Mol | Pocket2Mol-main/utils/train.py | import copy
import warnings
import numpy as np
import torch
import torch.nn as nn
from torch_geometric.data import Data, Batch
def repeat_data(data: Data, num_repeat) -> Batch:
datas = [copy.deepcopy(data) for i in range(num_repeat)]
return Batch.from_data_list(datas)
def repeat_batch(batch: Batch, num_repea... | 1,376 | 24.981132 | 75 | py |
Pocket2Mol | Pocket2Mol-main/utils/transforms.py | import copy
# from multiprocessing import context
import os
import sys
sys.path.append('.')
import random
import time
import uuid
from itertools import compress
# from tqdm import tqdm
import torch
import torch.nn.functional as F
import numpy as np
from torch_geometric.nn.pool import knn_graph
from torch_geometric.tran... | 41,757 | 46.506257 | 153 | py |
Pocket2Mol | Pocket2Mol-main/utils/datasets/pl.py | import os
import pickle
import lmdb
import torch
from torch.utils.data import Dataset
from tqdm.auto import tqdm
from ..protein_ligand import PDBProtein, parse_sdf_file
from ..data import ProteinLigandData, torchify_dict
class PocketLigandPairDataset(Dataset):
def __init__(self, raw_path, transform=None):
... | 4,143 | 32.419355 | 127 | py |
Pocket2Mol | Pocket2Mol-main/utils/datasets/__init__.py | import pickle
import torch
import os
import numpy as np
from torch.utils.data import Subset
from .pl import PocketLigandPairDataset
def get_dataset(config, *args, **kwargs):
name = config.name
root = config.path
if name == 'pl':
dataset = PocketLigandPairDataset(root, *args, **kwargs)
else:
... | 1,405 | 30.244444 | 97 | py |
transformers-interpret | transformers-interpret-master/test/text/test_explainer.py | import torch
from torch import Tensor
from transformers import (
AutoModelForCausalLM,
AutoModelForMaskedLM,
AutoModelForPreTraining,
AutoTokenizer,
PreTrainedModel,
PreTrainedTokenizer,
PreTrainedTokenizerFast,
)
from transformers_interpret import BaseExplainer
DISTILBERT_MODEL = AutoMode... | 7,528 | 35.371981 | 104 | py |
transformers-interpret | transformers-interpret-master/transformers_interpret/explainer.py | import inspect
import re
from abc import ABC, abstractmethod, abstractproperty
from typing import List, Tuple, Union
import torch
from transformers import PreTrainedModel, PreTrainedTokenizer
class BaseExplainer(ABC):
def __init__(
self,
model: PreTrainedModel,
tokenizer: PreTrainedTokeni... | 8,528 | 33.253012 | 120 | py |
transformers-interpret | transformers-interpret-master/transformers_interpret/attributions.py | from typing import Callable, List, Optional, Tuple, Union
import torch
import torch.nn as nn
from captum.attr import LayerIntegratedGradients
from captum.attr import visualization as viz
from transformers_interpret.errors import AttributionsNotCalculatedError
class Attributions:
def __init__(self, custom_forwar... | 4,999 | 36.878788 | 109 | py |
transformers-interpret | transformers-interpret-master/transformers_interpret/explainers/text/token_classification.py | import warnings
from typing import Dict, List, Optional, Union
import torch
from captum.attr import visualization as viz
from torch.nn.modules.sparse import Embedding
from transformers import PreTrainedModel, PreTrainedTokenizer
from transformers_interpret import BaseExplainer
from transformers_interpret.attributions... | 11,212 | 35.763934 | 120 | py |
transformers-interpret | transformers-interpret-master/transformers_interpret/explainers/text/sequence_classification.py | import warnings
from typing import Dict, List, Optional, Tuple, Union
import torch
from captum.attr import visualization as viz
from torch.nn.modules.sparse import Embedding
from transformers import PreTrainedModel, PreTrainedTokenizer
from transformers_interpret import BaseExplainer, LIGAttributions
from transformer... | 21,363 | 39.927203 | 131 | py |
transformers-interpret | transformers-interpret-master/transformers_interpret/explainers/text/question_answering.py | import warnings
from typing import Union
import torch
from captum.attr import visualization as viz
from torch.nn.modules.sparse import Embedding
from transformers import PreTrainedModel, PreTrainedTokenizer
from transformers_interpret import BaseExplainer, LIGAttributions
from transformers_interpret.errors import (
... | 13,219 | 33.881266 | 124 | py |
transformers-interpret | transformers-interpret-master/transformers_interpret/explainers/text/multilabel_classification.py | from typing import List, Optional
import torch
from captum.attr import visualization as viz
from transformers import PreTrainedModel, PreTrainedTokenizer
from .sequence_classification import SequenceClassificationExplainer
SUPPORTED_ATTRIBUTION_TYPES = ["lig"]
class MultiLabelClassificationExplainer(SequenceClassi... | 7,257 | 39.775281 | 131 | py |
transformers-interpret | transformers-interpret-master/transformers_interpret/explainers/text/zero_shot_classification.py | from typing import List, Tuple
import torch
from captum.attr import visualization as viz
from torch.nn.modules.sparse import Embedding
from transformers import PreTrainedModel, PreTrainedTokenizer
from transformers_interpret import LIGAttributions
from transformers_interpret.errors import AttributionTypeNotSupportedE... | 12,556 | 40.442244 | 117 | py |
D2Det | D2Det-master/setup.py | #!/usr/bin/env python
import os
import subprocess
import time
from setuptools import find_packages, setup
import torch
from torch.utils.cpp_extension import BuildExtension, CUDAExtension
def readme():
with open('README.md', encoding='utf-8') as f:
content = f.read()
return content
MAJOR = 1
MINOR =... | 10,018 | 32.734007 | 125 | py |
D2Det | D2Det-master/tools/test.py | import argparse
import os
import os
import os.path as osp
import sys
sys.path.insert(0, osp.join(osp.dirname(osp.abspath(__file__)), '../'))
import mmcv
import torch
from mmcv.parallel import MMDataParallel, MMDistributedDataParallel
from mmcv.runner import get_dist_info, init_dist, load_checkpoint
from mmdet.apis im... | 6,133 | 33.077778 | 79 | py |
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