repo stringlengths 1 99 | file stringlengths 13 215 | code stringlengths 12 59.2M | file_length int64 12 59.2M | avg_line_length float64 3.82 1.48M | max_line_length int64 12 2.51M | extension_type stringclasses 1
value |
|---|---|---|---|---|---|---|
STR | STR-master/utils/conv_type.py | from torch.nn import init
import torch
import torch.autograd as autograd
import torch.nn as nn
import torch.nn.functional as F
import math
from args import args as parser_args
import numpy as np
DenseConv = nn.Conv2d
def sparseFunction(x, s, activation=torch.relu, f=torch.sigmoid):
return torch.sign(x)*activati... | 3,471 | 30.853211 | 154 | py |
STR | STR-master/utils/logging.py | import abc
import tqdm
from torch.utils.tensorboard import SummaryWriter
class ProgressMeter(object):
def __init__(self, num_batches, meters, prefix=""):
self.batch_fmtstr = self._get_batch_fmtstr(num_batches)
self.meters = meters
self.prefix = prefix
def display(self, batch, tqdm_wr... | 3,167 | 25.621849 | 78 | py |
STR | STR-master/utils/builder.py | from args import args
import math
import torch
import torch.nn as nn
import utils.conv_type
import utils.bn_type
class Builder(object):
def __init__(self, conv_layer, bn_layer, first_layer=None):
self.conv_layer = conv_layer
self.bn_layer = bn_layer
self.first_layer = first_layer or conv... | 5,356 | 30.327485 | 88 | py |
STR | STR-master/utils/eval_utils.py | import torch
def accuracy(output, target, topk=(1,)):
"""Computes the accuracy over the k top predictions for the specified values of k"""
with torch.no_grad():
maxk = max(topk)
batch_size = target.size(0)
_, pred = output.topk(maxk, 1, True, True)
pred = pred.t()
corr... | 563 | 28.684211 | 88 | py |
STR | STR-master/utils/net_utils.py | from functools import partial
import os
import pathlib
import shutil
import math
import torch
import torch.nn as nn
def save_checkpoint(state, is_best, filename="checkpoint.pth", save=False):
filename = pathlib.Path(filename)
if not filename.parent.exists():
os.makedirs(filename.parent)
torch.s... | 1,847 | 21.536585 | 75 | py |
STR | STR-master/data/utils.py | import torch
from torch.utils.data.dataset import Dataset
def one_batch_dataset(dataset, batch_size):
print("==> Grabbing a single batch")
perm = torch.randperm(len(dataset))
one_batch = [dataset[idx.item()] for idx in perm[:batch_size]]
class _OneBatchWrapper(Dataset):
def __init__(self):
... | 525 | 21.869565 | 66 | py |
STR | STR-master/data/imagenet.py | import os
import torch
from torchvision import datasets, transforms
import torch.multiprocessing
import h5py
import os
import numpy as np
torch.multiprocessing.set_sharing_strategy("file_system")
class ImageNet:
def __init__(self, args):
super(ImageNet, self).__init__()
data_root = os.path.join(... | 4,309 | 30.691176 | 89 | py |
augmentation-corruption-fbr_main | augmentation-corruption-fbr_main/experiments/severity_scan.py | # Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import hydra
from hydra.utils import instantiate
import logging
from overlap.train_net import train_net
from overlap.test_net import test_net
... | 4,768 | 33.810219 | 100 | py |
augmentation-corruption-fbr_main | augmentation-corruption-fbr_main/experiments/test_cifar10.py | # Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import hydra
from hydra.utils import instantiate
import logging
from overlap.train_net import train_net
from overlap.test_net import test_net
... | 1,308 | 25.18 | 65 | py |
augmentation-corruption-fbr_main | augmentation-corruption-fbr_main/experiments/train_imagenet.py | # Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import hydra
from hydra.utils import instantiate
import logging
from overlap.train_net import train_net
from overlap.test_net import test_net
... | 3,301 | 29.859813 | 87 | py |
augmentation-corruption-fbr_main | augmentation-corruption-fbr_main/experiments/closest_augs.py | # Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import hydra
from hydra.utils import instantiate
import logging
from overlap.train_net import train_net
from overlap.test_net import test_net
... | 6,088 | 41.284722 | 149 | py |
augmentation-corruption-fbr_main | augmentation-corruption-fbr_main/experiments/severity_scan_imagenet.py | # Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import hydra
from hydra.utils import instantiate
import logging
from overlap.train_net import train_net
from overlap.test_net import test_net
... | 4,942 | 34.307143 | 100 | py |
augmentation-corruption-fbr_main | augmentation-corruption-fbr_main/experiments/train_imagenet_jsd.py | # Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import hydra
from hydra.utils import instantiate
import logging
from overlap.train_net_jsd import train_net
from overlap.test_net import test_... | 3,398 | 30.183486 | 87 | py |
augmentation-corruption-fbr_main | augmentation-corruption-fbr_main/experiments/feature_corrupt_error.py | # Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import hydra
from hydra.utils import instantiate
import logging
from overlap.train_net import train_net
from overlap.test_net import test_net
... | 4,670 | 40.336283 | 115 | py |
augmentation-corruption-fbr_main | augmentation-corruption-fbr_main/experiments/train_cifar10_jsd.py | # Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import hydra
from hydra.utils import instantiate
import logging
from overlap.train_net_jsd import train_net
from overlap.test_net import test_... | 1,841 | 27.338462 | 65 | py |
augmentation-corruption-fbr_main | augmentation-corruption-fbr_main/experiments/train_cifar10.py | # Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import hydra
from hydra.utils import instantiate
import logging
from overlap.train_net import train_net
from overlap.test_net import test_net
... | 1,744 | 26.698413 | 65 | py |
augmentation-corruption-fbr_main | augmentation-corruption-fbr_main/experiments/test_imagenet.py | # Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import hydra
from hydra.utils import instantiate
import logging
from overlap.train_net import train_net
from overlap.test_net import test_net
... | 2,623 | 27.835165 | 73 | py |
augmentation-corruption-fbr_main | augmentation-corruption-fbr_main/experiments/overlap/train_net_jsd.py | # Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import torch
import logging
import os
import time
import datetime
import torch.nn as nn
import torch.nn.functional as F
log = logging.getLogg... | 6,417 | 37.202381 | 135 | py |
augmentation-corruption-fbr_main | augmentation-corruption-fbr_main/experiments/overlap/feature_extractor.py | # Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import torch
import numpy as np
from hydra.utils import instantiate
from .train_net import train_net
class Network(object):
def __in... | 2,017 | 32.081967 | 99 | py |
augmentation-corruption-fbr_main | augmentation-corruption-fbr_main/experiments/overlap/test_net.py | # Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import torch
import logging
from .utils import logging as lu
log = logging.getLogger(__name__)
def test_net(model, test_dataset, batch_size,... | 1,338 | 30.880952 | 99 | py |
augmentation-corruption-fbr_main | augmentation-corruption-fbr_main/experiments/overlap/extract_features.py | # Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import torch
import logging
from .utils import logging as lu
import numpy as np
import os
log = logging.getLogger(__name__)
def distributed_... | 4,564 | 43.320388 | 165 | py |
augmentation-corruption-fbr_main | augmentation-corruption-fbr_main/experiments/overlap/test_corrupt_net.py | # Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import torch
import logging
from .utils import logging as lu
from omegaconf import open_dict
from .augmentations.utils import aug_finder
from... | 4,659 | 37.196721 | 169 | py |
augmentation-corruption-fbr_main | augmentation-corruption-fbr_main/experiments/overlap/datasets.py | # Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
from . import augmentations as aug
from .augmentations.utils.converters import NumpyToTensor, PilToNumpy
from .augmentations.utils.aug_finder ... | 33,803 | 45.117326 | 157 | py |
augmentation-corruption-fbr_main | augmentation-corruption-fbr_main/experiments/overlap/models.py | # Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import math
import torch
import torch.nn as nn
import torch.nn.functional as F
class ResHead(nn.Module):
"""ResNet head."""
def __in... | 8,640 | 35.459916 | 103 | py |
augmentation-corruption-fbr_main | augmentation-corruption-fbr_main/experiments/overlap/train_net.py | # Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import torch
import logging
import os
import time
import datetime
log = logging.getLogger(__name__)
def eta_str(eta_td):
"""Converts an ... | 5,251 | 35.727273 | 135 | py |
augmentation-corruption-fbr_main | augmentation-corruption-fbr_main/experiments/overlap/wideresnet.py | # This source code is adapted from code licensed under the MIT license
# found in third_party/wideresnet_license from the root directory of
# this source tree.
"""WideResNet implementation (https://arxiv.org/abs/1605.07146)."""
import math
import torch
import torch.nn as nn
import torch.nn.functional as F
class Bas... | 4,461 | 29.986111 | 79 | py |
augmentation-corruption-fbr_main | augmentation-corruption-fbr_main/experiments/overlap/augmentations/imagenetc.py | # This source code is adapted from code licensed under the license at
# third_party/imagenetc_license from the root directory of the repository
# Originally available: github.com/hendrycks/robustness
# Modifications Copyright (c) Facebook, Inc. and its affiliates,
# licensed under the MIT license found in the LICENSE... | 25,528 | 31.940645 | 143 | py |
augmentation-corruption-fbr_main | augmentation-corruption-fbr_main/experiments/overlap/augmentations/utils/converters.py | # Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import numpy as np
from PIL import Image
import torch
class PilToNumpy(object):
def __init__(self, as_float=False, scaled_to_one=False):
... | 1,351 | 29.044444 | 68 | py |
augmentation-corruption-fbr_main | augmentation-corruption-fbr_main/imagenet_c_bar/test_c_bar.py | # Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
from transform_finder import build_transform
import torch
import torchvision as tv
from utils.converters import PilToNumpy, NumpyToTensor
CIF... | 4,285 | 36.596491 | 105 | py |
augmentation-corruption-fbr_main | augmentation-corruption-fbr_main/imagenet_c_bar/make_cifar10_c_bar.py | # Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import argparse
import torchvision as tv
from transform_finder import build_transform
from utils.converters import PilToNumpy, NumpyToPil
impo... | 3,650 | 37.840426 | 105 | py |
augmentation-corruption-fbr_main | augmentation-corruption-fbr_main/imagenet_c_bar/make_imagenet_c_bar.py | # Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import argparse
import torchvision as tv
from transform_finder import build_transform
from utils.converters import PilToNumpy, NumpyToPil
impo... | 4,256 | 36.342105 | 87 | py |
augmentation-corruption-fbr_main | augmentation-corruption-fbr_main/imagenet_c_bar/utils/converters.py | # Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import numpy as np
from PIL import Image
import torch
class PilToNumpy(object):
def __init__(self, as_float=False, scaled_to_one=False):
... | 1,351 | 29.044444 | 68 | py |
augmentation-corruption-fbr_main | augmentation-corruption-fbr_main/notebook_utils/training_loop.py | # Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import math
import torch
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
def train_model(model, dataset, num_workers... | 1,811 | 27.3125 | 95 | py |
augmentation-corruption-fbr_main | augmentation-corruption-fbr_main/notebook_utils/wideresnet.py | # This source code is adapted from code licensed under the MIT license
# found in third_party/wideresnet_license from the root directory of
# this source tree.
"""WideResNet implementation (https://arxiv.org/abs/1605.07146)."""
import math
import torch
import torch.nn as nn
import torch.nn.functional as F
import nump... | 4,479 | 30.111111 | 79 | py |
jupyter-book | jupyter-book-master/tests/test_config.py | # from pathlib import Path
import jsonschema
import pytest
import sphinx as sphinx_build
from jupyter_book.cli.main import sphinx
from jupyter_book.config import get_final_config, validate_yaml
pytest_plugins = "pytester"
SPHINX_VERSION = f".sphinx{sphinx_build.version_info[0]}"
@pytest.mark.parametrize(
"user... | 9,373 | 32.359431 | 123 | py |
jupyter-book | jupyter-book-master/jupyter_book/config.py | """A small sphinx extension to let you configure a site with YAML metadata."""
import json
import sys
from functools import lru_cache
from pathlib import Path
from typing import Optional, Union
import docutils
import jsonschema
import sphinx
import yaml
from sphinx.util import logging
from .utils import _message_box
... | 18,195 | 38.04721 | 135 | py |
jupyter-book | jupyter-book-master/jupyter_book/pdf.py | """Commands to facilitate conversion to PDF."""
import asyncio
import os
from copy import copy
from pathlib import Path
from .utils import _error, _message_box
# LaTeX Documents Tuple Spec
LATEX_DOCUMENTS = (
"startdocname",
"targetname",
"title",
"author",
"theme",
"toctree_only",
)
def htm... | 5,554 | 30.5625 | 86 | py |
ec-darkpattern | ec-darkpattern-master/darkpattern-auto-detection-deeplearning/trainer/trainer.py | from typing import Any, List, Tuple
import torch
from torch import nn, optim
from torch.utils.data import DataLoader
class Trainer:
def __init__(
self,
net: nn.Module,
optimizer: optim.Optimizer,
critetion: nn.Module,
lr_scheduler: Any,
device: torch.device,
) ... | 3,588 | 30.482456 | 82 | py |
ec-darkpattern | ec-darkpattern-master/darkpattern-auto-detection-deeplearning/models/nn/bert.py | import torch
from torch import nn
from transformers import BertModel
class DarkpatternClassifierBert(nn.Module):
def __init__(
self,
pretrained: str = "bert-base-uncased",
dropout_rate: float = 0.1,
output_layer: nn.Linear = nn.Linear(in_features=768, out_features=2),
):
... | 801 | 31.08 | 77 | py |
ec-darkpattern | ec-darkpattern-master/darkpattern-auto-detection-deeplearning/experiments/train.py | from os.path import join
from typing import List
import hydra
import numpy as np
import pandas as pd
import torch
from const.path import CONFIG_PATH, DATASET_TSV_PATH, NN_MODEL_PICKLES_PATH
from omegaconf import DictConfig
from sklearn import metrics
from sklearn.model_selection import StratifiedKFold
from torch impor... | 6,125 | 30.415385 | 88 | py |
ec-darkpattern | ec-darkpattern-master/darkpattern-auto-detection-deeplearning/utils/dataset.py | from typing import Callable, List, Tuple
import torch
from torch.utils.data import Dataset
class DarkpatternDataset(Dataset):
def __init__(
self,
texts: List[str],
labels: List[int],
text_to_tensor: Callable[[str], torch.Tensor],
) -> None:
self.texts: List[str] = text... | 723 | 26.846154 | 74 | py |
ec-darkpattern | ec-darkpattern-master/darkpattern-auto-detection-deeplearning/utils/random_seed.py | import os
import random
import numpy as np
import torch
def set_random_seed(random_seed: int = 42) -> None:
random.seed(random_seed)
os.environ["PYTHONHASHSEED"] = str(random_seed)
np.random.seed(random_seed)
torch.manual_seed(random_seed)
torch.cuda.manual_seed(random_seed)
torch.backends.cu... | 341 | 21.8 | 51 | py |
ec-darkpattern | ec-darkpattern-master/darkpattern-auto-detection-deeplearning/utils/text.py | import torch
from transformers import PreTrainedTokenizer
def tensor_to_text(tensor: torch.Tensor, tokenizer: PreTrainedTokenizer) -> str:
"""
Convert tensor to text.
"""
return tokenizer.decode(tensor)
def text_to_tensor(
text: str, tokenizer: PreTrainedTokenizer, max_length: int
) -> torch.Ten... | 493 | 22.52381 | 80 | py |
dolphin | dolphin-main/video_utils.py | import imageio
import torch
import numpy as np
import decord
import torchvision
from einops import rearrange
from torchvision.transforms import Resize, InterpolationMode
from utils import get_new_video_name
def prepare_video(
video_path: str,
resolution: int,
device,
dtype=torch.float16,
normaliz... | 2,467 | 28.73494 | 87 | py |
dolphin | dolphin-main/utils.py | import os, sys, uuid
import importlib
import numpy as np
import torch
import random
def instantiate_from_config(config, **kwargs):
if not "target" in config:
raise KeyError("Expected key `target` to instantiate.")
return get_obj_from_str(config["target"])(**config.get("params", dict()), **kwargs)
d... | 1,588 | 25.932203 | 87 | py |
dolphin | dolphin-main/modules/text2video_zero/utils.py | import os
import cv2
import numpy as np
import torch
import torchvision
from torchvision.transforms import Resize, InterpolationMode
import imageio
from einops import rearrange
from PIL import Image
import decord
def create_gif(frames, fps, rescale=False, path=None):
if path is None:
dir = "temporal"
... | 3,963 | 32.880342 | 87 | py |
dolphin | dolphin-main/modules/text2video_zero/model.py | import os
from enum import Enum
import numpy as np
import tomesd
import torch
from diffusers import (
StableDiffusionInstructPix2PixPipeline,
StableDiffusionControlNetPipeline,
ControlNetModel,
UNet2DConditionModel,
)
from diffusers.schedulers import EulerAncestralDiscreteScheduler, DDIMScheduler
from ... | 17,263 | 33.528 | 193 | py |
dolphin | dolphin-main/modules/text2video_zero/__init__.py | import torch
from .model import (
CannyText2VideoModel,
PoseText2VideoModel,
DepthText2VideoModel,
VideoPix2PixModel,
Text2VideoModel,
)
from utils import generate_video_name_mp4, get_new_video_name
class CannyText2Video:
def __init__(self, device):
self.device = device
self.... | 3,031 | 28.153846 | 80 | py |
dolphin | dolphin-main/modules/text2video_zero/text_to_video_pipeline.py | from diffusers import StableDiffusionPipeline
import torch
from dataclasses import dataclass
from typing import Callable, List, Optional, Union
import numpy as np
from diffusers.utils import deprecate, logging, BaseOutput
from einops import rearrange, repeat
from torch.nn.functional import grid_sample
import torchvisio... | 24,513 | 33.72238 | 143 | py |
dolphin | dolphin-main/modules/blip/__init__.py | import torch
import numpy as np
from transformers import AutoProcessor, Blip2ForConditionalGeneration
from PIL import Image
from video_utils import prepare_video
class ImageCaptioning:
def __init__(self, device):
print("Initializing BLIP2 for ImageCaptioning")
self.device = device
self.pr... | 1,840 | 37.354167 | 83 | py |
dolphin | dolphin-main/modules/modelscope_t2v/__init__.py | from __future__ import annotations
import random
import tempfile
import imageio
import numpy as np
import torch
from diffusers import DiffusionPipeline, DPMSolverMultistepScheduler
from utils import generate_video_name_mp4
def to_video(frames: list[np.ndarray], fps: int, out_file=None) -> str:
if out_file is N... | 1,838 | 26.863636 | 87 | py |
dolphin | dolphin-main/modules/annotator/__init__.py | import cv2
import torch
import numpy as np
from einops import rearrange
from torchvision.transforms import Resize, InterpolationMode
from .util import HWC3
from .openpose import OpenposeDetector
from .midas import MidasDetector
from utils import get_new_video_name
from video_utils import prepare_video, create_video
... | 4,607 | 36.770492 | 84 | py |
dolphin | dolphin-main/modules/annotator/midas/utils.py | """Utils for monoDepth."""
import sys
import re
import numpy as np
import cv2
import torch
def read_pfm(path):
"""Read pfm file.
Args:
path (str): path to file
Returns:
tuple: (data, scale)
"""
with open(path, "rb") as file:
color = None
width = None
heig... | 4,582 | 23.121053 | 88 | py |
dolphin | dolphin-main/modules/annotator/midas/api.py | # based on https://github.com/isl-org/MiDaS
import cv2
import os
import torch
import torch.nn as nn
from torchvision.transforms import Compose
from .midas.dpt_depth import DPTDepthModel
from .midas.midas_net import MidasNet
from .midas.midas_net_custom import MidasNet_small
from .midas.transforms import Resize, Norma... | 5,309 | 28.831461 | 124 | py |
dolphin | dolphin-main/modules/annotator/midas/__init__.py | import cv2
import numpy as np
import torch
from einops import rearrange
from .api import MiDaSInference
class MidasDetector:
def __init__(self, device=None):
self.device = device or torch.device(
"cuda" if torch.cuda.is_available() else "cpu"
)
self.model = MiDaSInference(mode... | 1,544 | 35.785714 | 81 | py |
dolphin | dolphin-main/modules/annotator/midas/midas/base_model.py | import torch
class BaseModel(torch.nn.Module):
def load(self, path):
"""Load model from file.
Args:
path (str): file path
"""
parameters = torch.load(path, map_location=torch.device('cpu'))
if "optimizer" in parameters:
parameters = parameters["mod... | 367 | 20.647059 | 71 | py |
dolphin | dolphin-main/modules/annotator/midas/midas/midas_net.py | """MidashNet: Network for monocular depth estimation trained by mixing several datasets.
This file contains code that is adapted from
https://github.com/thomasjpfan/pytorch_refinenet/blob/master/pytorch_refinenet/refinenet/refinenet_4cascade.py
"""
import torch
import torch.nn as nn
from .base_model import BaseModel
f... | 2,709 | 34.194805 | 130 | py |
dolphin | dolphin-main/modules/annotator/midas/midas/vit.py | import torch
import torch.nn as nn
import timm
import types
import math
import torch.nn.functional as F
class Slice(nn.Module):
def __init__(self, start_index=1):
super(Slice, self).__init__()
self.start_index = start_index
def forward(self, x):
return x[:, self.start_index :]
class... | 14,625 | 28.727642 | 96 | py |
dolphin | dolphin-main/modules/annotator/midas/midas/dpt_depth.py | import torch
import torch.nn as nn
import torch.nn.functional as F
from .base_model import BaseModel
from .blocks import (
FeatureFusionBlock,
FeatureFusionBlock_custom,
Interpolate,
_make_encoder,
forward_vit,
)
def _make_fusion_block(features, use_bn):
return FeatureFusionBlock_custom(
... | 3,154 | 27.681818 | 89 | py |
dolphin | dolphin-main/modules/annotator/midas/midas/midas_net_custom.py | """MidashNet: Network for monocular depth estimation trained by mixing several datasets.
This file contains code that is adapted from
https://github.com/thomasjpfan/pytorch_refinenet/blob/master/pytorch_refinenet/refinenet/refinenet_4cascade.py
"""
import torch
import torch.nn as nn
from .base_model import BaseModel
f... | 5,207 | 39.6875 | 168 | py |
dolphin | dolphin-main/modules/annotator/midas/midas/blocks.py | import torch
import torch.nn as nn
from .vit import (
_make_pretrained_vitb_rn50_384,
_make_pretrained_vitl16_384,
_make_pretrained_vitb16_384,
forward_vit,
)
def _make_encoder(backbone, features, use_pretrained, groups=1, expand=False, exportable=True, hooks=None, use_vit_only=False, use_readout="ign... | 9,242 | 25.947522 | 150 | py |
dolphin | dolphin-main/modules/annotator/openpose/hand.py | import cv2
import json
import numpy as np
import math
import time
from scipy.ndimage.filters import gaussian_filter
import matplotlib.pyplot as plt
import matplotlib
import torch
from skimage.measure import label
from .model import handpose_model
from . import util
class Hand(object):
def __init__(self, model_pa... | 3,588 | 32.542056 | 85 | py |
dolphin | dolphin-main/modules/annotator/openpose/model.py | import torch
from collections import OrderedDict
import torch
import torch.nn as nn
def make_layers(block, no_relu_layers):
layers = []
for layer_name, v in block.items():
if "pool" in layer_name:
layer = nn.MaxPool2d(kernel_size=v[0], stride=v[1], padding=v[2])
layers.append(... | 8,853 | 34.416 | 87 | py |
dolphin | dolphin-main/modules/annotator/openpose/util.py | import math
import numpy as np
import matplotlib
import cv2
def padRightDownCorner(img, stride, padValue):
h = img.shape[0]
w = img.shape[1]
pad = 4 * [None]
pad[0] = 0 # up
pad[1] = 0 # left
pad[2] = 0 if (h % stride == 0) else stride - (h % stride) # down
pad[3] = 0 if (w % stride ==... | 8,372 | 32.626506 | 121 | py |
dolphin | dolphin-main/modules/annotator/openpose/__init__.py | import os
os.environ["KMP_DUPLICATE_LIB_OK"] = "TRUE"
import torch
import numpy as np
from . import util
from .body import Body
from .hand import Hand
from ..util import annotator_ckpts_path
body_model_path = "https://huggingface.co/lllyasviel/ControlNet/resolve/main/annotator/ckpts/body_pose_model.pth"
hand_model_... | 2,170 | 40.75 | 113 | py |
dolphin | dolphin-main/modules/annotator/openpose/body.py | import cv2
import numpy as np
import math
from scipy.ndimage.filters import gaussian_filter
import torch
from torchvision import transforms
from . import util
from .model import bodypose_model
class Body(object):
def __init__(self, model_path, device=None):
self.device = device or torch.device(
... | 13,127 | 38.18806 | 123 | py |
dolphin | dolphin-main/modules/mplug/get_video_caption.py | import ruamel.yaml as yaml
import numpy as np
import torch
import torch.nn as nn
from .models.model_caption_mplug_vatex import MPLUG
from .models.vit import interpolate_pos_embed, resize_pos_embed
from .models.tokenization_bert import BertTokenizer
from decord import VideoReader
import decord
import os
config_path = ... | 3,736 | 31.495652 | 162 | py |
dolphin | dolphin-main/modules/mplug/models/model_caption_mplug_vatex.py | from functools import partial
from .vit import VisionTransformer
from .modeling_mplug import BertConfig, BertModel, BertPrefixModel, FusionModel
from .visual_transformers import initialize_clip
from .predictor import TextGenerator
import torch
from torch import nn
import torch.nn.functional as F
import numpy as np
... | 9,712 | 35.242537 | 135 | py |
dolphin | dolphin-main/modules/mplug/models/predictor.py | #!/usr/bin/env python
""" Translator Class and builder """
from __future__ import print_function
import torch.nn as nn
import torch.nn.functional as F
import os
import math
import json
import torch
def _make_causal_mask(input_ids_shape: torch.Size, dtype: torch.dtype, past_key_values_length: int = 0, device='cuda:0'... | 21,697 | 40.726923 | 149 | py |
dolphin | dolphin-main/modules/mplug/models/vit.py | # Copyright (c) 2015-present, Facebook, Inc.
# All rights reserved.
import math
import torch
import torch.nn as nn
import torch.nn.functional as F
from functools import partial
from timm.models.vision_transformer import _cfg, PatchEmbed
from timm.models.registry import register_model
from timm.models.layers import tru... | 9,634 | 41.822222 | 132 | py |
dolphin | dolphin-main/modules/mplug/models/visual_transformers.py | import copy
import json
import logging
import math
import os
import shutil
import tarfile
import tempfile
import sys
from io import open
import torch.nn.functional as F
import torch
from torch import nn
from torch.nn import CrossEntropyLoss, SmoothL1Loss
import numpy as np
from .clip import clip
def resize_pos_embed... | 3,991 | 35.290909 | 132 | py |
dolphin | dolphin-main/modules/mplug/models/modeling_mplug.py | # coding=utf-8
# Copyright 2018 The Google AI Language Team Authors and The HuggingFace Inc. team.
# Copyright (c) 2018, NVIDIA CORPORATION. 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.
# You may obtain a cop... | 112,740 | 43.526461 | 213 | py |
dolphin | dolphin-main/modules/mplug/models/clip/clip.py | import hashlib
import os
import urllib
import warnings
from typing import Union, List
import torch
from PIL import Image
from torchvision.transforms import Compose, Resize, CenterCrop, ToTensor, Normalize
from tqdm import tqdm
from .model import build_model
from .simple_tokenizer import SimpleTokenizer as _Tokenizer
... | 6,098 | 37.601266 | 142 | py |
dolphin | dolphin-main/modules/mplug/models/clip/model.py | from collections import OrderedDict
from typing import Tuple, Union
import torch
import torch.nn.functional as F
from torch import nn
class Bottleneck(nn.Module):
expansion = 4
def __init__(self, inplanes, planes, stride=1):
super().__init__()
# all conv layers have stride 1. an avgpool is ... | 18,310 | 39.511062 | 178 | py |
jericho | jericho-master/jericho/game_info.py | # Copyright (C) 2018 Microsoft Corporation
# This program is free software; you can redistribute it and/or
# modify it under the terms of the GNU General Public License
# as published by the Free Software Foundation; either version 2
# of the License, or (at your option) any later version.
# This program is distribut... | 417,373 | 752.382671 | 16,184 | py |
BBA_measures_classification | BBA_measures_classification-main/grad_opt.py | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
@author: olympio
"""
import matplotlib.pyplot as plt
import numpy as np
import data_gen as dg
from aux_functions import *
import train_classifier as tc
from numpy.random import default_rng
rng = default_rng()
import tensorflow as tf
N1=500 #Number of points in class ... | 3,182 | 32.505263 | 113 | py |
scicite | scicite-master/scicite/training/multitask_trainer_two_tasks.py | """
This module is an extended trainer based on the allennlp's default trainer to handle multitask training
for two auxiliary tasks
A :class:`~allennlp.training.trainer.Trainer` is responsible for training a
:class:`~allennlp.models.model.Model`.
Typically you might create a configuration file specifying the mode... | 58,023 | 48.977606 | 119 | py |
scicite | scicite-master/scicite/training/train_multitask_two_tasks.py | """
The `train_multitask` subcommand that can be used to train the model in the multitask fashion
It requires a configuration file and a directory in
which to write the results.
.. code-block:: bash
$ allennlp train --help
usage: allennlp train [-h] -s SERIALIZATION_DIR [-r] [-o OVERRIDES]
... | 23,845 | 46.692 | 143 | py |
scicite | scicite-master/scicite/training/multitask_trainer.py | """
This module is an extended trainer based on the allennlp's default trainer to handle multitask training
A :class:`~allennlp.training.trainer.Trainer` is responsible for training a
:class:`~allennlp.models.model.Model`.
Typically you might create a configuration file specifying the model and
training parameters an... | 55,632 | 48.407638 | 114 | py |
scicite | scicite-master/scicite/training/train_multitask.py | """
The ``train`` subcommand can be used to train a model.
It requires a configuration file and a directory in
which to write the results.
.. code-block:: bash
$ allennlp train --help
usage: allennlp train [-h] -s SERIALIZATION_DIR [-r] [-o OVERRIDES]
[--file-friendly-logging]
... | 20,414 | 45.083521 | 113 | py |
scicite | scicite-master/scicite/models/scaffold_bilstm_attention_classifier.py | import operator
from copy import deepcopy
from distutils.version import StrictVersion
from typing import Dict, Optional
import allennlp
import numpy as np
import torch
import torch.nn.functional as F
from allennlp.common import Params
from allennlp.data import Instance
from allennlp.data import Vocabulary
from allennl... | 15,619 | 47.8125 | 125 | py |
scicite | scicite-master/scicite/dataset_readers/citation_data_reader_aclarc.py | """ Data reader for AllenNLP """
from typing import Dict, List
import json
import jsonlines
import logging
import torch
from allennlp.data import Field
from overrides import overrides
from allennlp.common import Params
from allennlp.common.file_utils import cached_path
from allennlp.data.dataset_readers.dataset_read... | 6,739 | 44.234899 | 109 | py |
scicite | scicite-master/scicite/dataset_readers/citation_data_reader_scicite.py | """ Data reader for AllenNLP """
from typing import Dict, List
import json
import logging
import torch
from allennlp.data import Field
from overrides import overrides
from allennlp.common import Params
from allennlp.common.file_utils import cached_path
from allennlp.data.dataset_readers.dataset_reader import Dataset... | 7,766 | 45.232143 | 109 | py |
sharpDARTS | sharpDARTS-master/cnn/warmup_scheduler.py | # https://github.com/ildoonet/pytorch-gradual-warmup-lr
# License: MIT
from torch.optim.lr_scheduler import _LRScheduler
class GradualWarmupScheduler(_LRScheduler):
""" Gradually warm-up(increasing) learning rate in optimizer.
Proposed in 'Accurate, Large Minibatch SGD: Training ImageNet in 1 Hour'.
Args... | 1,749 | 41.682927 | 122 | py |
sharpDARTS | sharpDARTS-master/cnn/costar_baseline_model.py | import math
import torch
import torch.nn as nn
from torch.nn import functional as F
# from . import operations
# from . import genotypes
# from .operations import ReLUConvBN
# from .operations import ConvBNReLU
# from .operations import FactorizedReduce
# from .operations import Identity
from torch.autograd import Vari... | 7,916 | 35.652778 | 146 | py |
sharpDARTS | sharpDARTS-master/cnn/test.py | import os
import sys
import glob
import numpy as np
import torch
import utils
import logging
import argparse
import torch.nn as nn
import genotypes
import torch.utils
import torchvision.datasets as dset
import torch.backends.cudnn as cudnn
from torch.autograd import Variable
from model import NetworkCIFAR as Network
... | 3,599 | 33.285714 | 102 | py |
sharpDARTS | sharpDARTS-master/cnn/train_costar.py | # source: https://github.com/NVIDIA/apex/tree/master/examples/imagenet
# license: BSD 3-Clause
#
# to install apex:
# pip3 install --user --upgrade -e . --global-option="build_ext" --global-option="--cpp_ext" --global-option="--cuda_ext"
#
# ### Multi-process training with FP16_Optimizer, dynamic loss scaling
# $ p... | 37,252 | 47.192755 | 315 | py |
sharpDARTS | sharpDARTS-master/cnn/main_fp16_optimizer.py | # source: https://github.com/NVIDIA/apex/tree/master/examples/imagenet
# license: BSD 3-Clause
#
# to install apex:
# pip3 install --user --upgrade -e . --global-option="build_ext" --global-option="--cpp_ext" --global-option="--cuda_ext"
#
# ### Multi-process training with FP16_Optimizer, dynamic loss scaling
# $ p... | 34,031 | 42.352866 | 365 | py |
sharpDARTS | sharpDARTS-master/cnn/cifar10_1.py | # Source: https://github.com/kharvd/cifar-10.1-pytorch
# License: MIT
import io
import os
import os.path
import pickle
import numpy as np
from PIL import Image
import torch.utils.data as data
from torchvision.datasets.utils import download_url, check_integrity
def load_new_test_data(root, version='default'):
d... | 4,555 | 31.542857 | 105 | py |
sharpDARTS | sharpDARTS-master/cnn/architect.py | import torch
import numpy as np
import torch.nn as nn
from torch.autograd import Variable
def _concat(xs):
return torch.cat([x.view(-1) for x in xs])
class Architect(object):
def __init__(self, model, args):
self.network_momentum = args.momentum
self.network_weight_decay = args.weight_decay
self.mo... | 3,429 | 35.88172 | 130 | py |
sharpDARTS | sharpDARTS-master/cnn/train_imagenet.py | import os
import sys
import numpy as np
import time
import torch
import utils
import glob
import random
import logging
import argparse
import json
import torch.nn as nn
import genotypes
import torch.utils
import torchvision.datasets as dset
import torchvision.transforms as transforms
import torch.backends.cudnn as cudn... | 8,489 | 38.859155 | 170 | py |
sharpDARTS | sharpDARTS-master/cnn/visualize_whole_network.py | # Example commands to run:
#
# python3 visualize_whole_network.py --multi_channel
#
# python3 visualize_whole_network.py --dataset imagenet --arch SHARP_DARTS --auxiliary
#
# Set matplotlib backend to Agg
# *MUST* be done BEFORE importing hiddenlayer or libs that import matplotlib
import matplotlib
matplotlib.use... | 6,350 | 38.447205 | 141 | py |
sharpDARTS | sharpDARTS-master/cnn/utils.py | # Some data loading code is from https://github.com/DRealArun/darts/ with the same license as darts.
import os
import time
import numpy as np
import logging
import torch
import shutil
import argparse
import glob
import json
import csv
import torchvision.transforms as transforms
from torch.autograd import Variable
impor... | 21,363 | 32.225505 | 151 | py |
sharpDARTS | sharpDARTS-master/cnn/model.py | import math
import torch
import torch.nn as nn
from torch.nn import functional as F
import numpy as np
from genotypes import PRIMITIVES, MULTICHANNELNET_PRIMITIVES
from operations import *
import operations
# from . import operations
# from . import genotypes
# from .operations import ReLUConvBN
# from .operations impo... | 34,793 | 39.552448 | 220 | py |
sharpDARTS | sharpDARTS-master/cnn/dataset.py | # Code to load various datasets for training.
#
# Some data loading code is from https://github.com/DRealArun/darts/ with the same license as DARTS.
import os
import sys
import time
import glob
import numpy as np
import torch
import utils
import logging
import argparse
import torch.nn as nn
import genotypes
import torc... | 14,281 | 46.926174 | 164 | py |
sharpDARTS | sharpDARTS-master/cnn/model_search.py | import torch
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
from operations import *
import operations
from torch.autograd import Variable
from genotypes import PRIMITIVES, MULTICHANNELNET_PRIMITIVES
from genotypes import Genotype
import networkx as nx
from networkx.readwrite import json_graph... | 27,726 | 42.188474 | 218 | py |
sharpDARTS | sharpDARTS-master/cnn/train_search.py | import os
import sys
import time
import glob
import numpy as np
import torch
import utils
import logging
import argparse
import copy
import torch.nn as nn
import torch.utils
import torch.nn.functional as F
import torchvision.datasets as dset
import torch.backends.cudnn as cudnn
import networkx as nx
from torch.autogra... | 18,217 | 43.434146 | 161 | py |
sharpDARTS | sharpDARTS-master/cnn/flops_counter.py | # source: https://github.com/sovrasov/flops-counter.pytorch
# license: MIT
import torch.nn as nn
import torch
import numpy as np
def flops_to_string(flops):
if flops // 10**9 > 0:
return str(round(flops / 10.**9, 2)) + 'GMac'
elif flops // 10**6 > 0:
return str(round(flops / 10.**6, 2)) + 'MMac... | 8,348 | 30.988506 | 100 | py |
sharpDARTS | sharpDARTS-master/cnn/test_imagenet.py | import os
import sys
import numpy as np
import torch
import utils
import glob
import random
import logging
import argparse
import torch.nn as nn
import genotypes
import torch.utils
import torchvision.datasets as dset
import torchvision.transforms as transforms
import torch.backends.cudnn as cudnn
from torch.autograd i... | 3,791 | 32.557522 | 104 | py |
sharpDARTS | sharpDARTS-master/cnn/train.py | import os
import sys
import time
import glob
import json
import copy
import numpy as np
import torch
import utils
import logging
import argparse
import torch.nn as nn
import torch.utils
import torchvision.datasets as dset
import torch.backends.cudnn as cudnn
from torch.autograd import Variable
from model import Networ... | 17,359 | 47.627451 | 172 | py |
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