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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BackwardCompatibilityML-dev | BackwardCompatibilityML-dev/backwardcompatibilityml/tensorflow/helpers.py | import tensorflow.compat.v2 as tf
def bc_fit(h2, training_set=None, testing_set=None, epochs=None, bc_loss=None, optimizer=None):
"""
This function is used to train a model h2, using an instance of a Tensorflow BCLoss function
that has been instantiated using an existing model h1 and regularization parame... | 2,952 | 34.154762 | 96 | py |
BackwardCompatibilityML-dev | BackwardCompatibilityML-dev/backwardcompatibilityml/tensorflow/loss/new_error.py | import tensorflow.compat.v2 as tf
class BCNLLLoss(object):
"""
Backward Compatibility New Error Negative Log Likelihood Loss
This class implements the backward compatibility loss function
with the underlying loss function being the Negative Log Likelihood
loss.
Note that the final layer of e... | 9,939 | 35.544118 | 87 | py |
BackwardCompatibilityML-dev | BackwardCompatibilityML-dev/backwardcompatibilityml/tensorflow/loss/strict_imitation.py | import tensorflow.compat.v2 as tf
import tensorflow.compat.v1 as tf1
class BCStrictImitationNLLLoss(object):
"""
Strict Imitation Negative Log Likelihood Loss
This class implements the strict imitation loss function
with the underlying loss function being the Negative Log Likelihood
loss.
No... | 10,303 | 36.74359 | 124 | py |
BackwardCompatibilityML-dev | BackwardCompatibilityML-dev/development/model-comparison/app.py | # Copyright (c) Microsoft Corporation
# Licensed under the MIT License.
import os
import copy
import io
import numpy as np
import torch
import torch.nn as nn
import torch.optim as optim
import torch.nn.functional as F
import torchvision
import random
from backwardcompatibilityml import loss as bcloss
from backwardcomp... | 7,441 | 36.585859 | 139 | py |
BackwardCompatibilityML-dev | BackwardCompatibilityML-dev/development/compatibility-analysis/app.py | # Copyright (c) Microsoft Corporation
# Licensed under the MIT License.
import os
import copy
import io
import numpy as np
import torch
import torch.nn as nn
import torch.optim as optim
import torch.nn.functional as F
import torchvision
import random
from backwardcompatibilityml import loss as bcloss
from backwardcomp... | 8,557 | 37.723982 | 139 | py |
PPGANs-Privacy-preserving-GANs | PPGANs-Privacy-preserving-GANs-master/PPGANS/gradient_noise.py | import inspect
import keras
from keras import backend as K
def _get_shape(x):
if hasattr(x, 'dense_shape'):
return x.dense_shape
return K.shape(x)
def add_gradient_noise(BaseOptimizer):
if not (
inspect.isclass(BaseOptimizer) and
issubclass(BaseOptimizer, keras.optimizers.Optimi... | 1,618 | 28.981481 | 99 | py |
PPGANs-Privacy-preserving-GANs | PPGANs-Privacy-preserving-GANs-master/PPGANS/dpgan.py | import os
os.environ["KERAS_BACKEND"] = "tensorflow"
import numpy as np
from tqdm import tqdm
import matplotlib.pyplot as plt
from keras.layers import Input
from keras.models import Model, Sequential
from keras.layers.core import Reshape, Dense, Dropout, Flatten
from keras.layers.advanced_activations import LeakyReLU... | 5,294 | 33.835526 | 117 | py |
GDANet | GDANet-main/main_cls.py | from __future__ import print_function
import os
import argparse
import torch
import torch.nn as nn
import torch.optim as optim
from torch.optim.lr_scheduler import CosineAnnealingLR
from util.data_util import ModelNet40
from model.GDANet_cls import GDANET
import numpy as np
from torch.utils.data import DataLoader
from ... | 9,519 | 41.123894 | 119 | py |
GDANet | GDANet-main/main_ptseg.py | from __future__ import print_function
import os
import argparse
import torch
import torch.optim as optim
from torch.optim.lr_scheduler import CosineAnnealingLR, StepLR
from util.data_util import PartNormalDataset
import torch.nn.functional as F
import torch.nn as nn
from model.GDANet_ptseg import GDANet
import numpy as... | 20,206 | 44.511261 | 151 | py |
GDANet | GDANet-main/voting_eval_modelnet.py | from __future__ import print_function
import os
import argparse
import torch
import torch.nn as nn
import torch.nn.functional as F
from util.data_util import ModelNet40
from model.GDANet_cls import GDANET
import numpy as np
from torch.utils.data import DataLoader
from util.util import cal_loss, IOStream
import sklearn.... | 4,737 | 37.836066 | 118 | py |
GDANet | GDANet-main/util/data_util.py | import glob
import h5py
import numpy as np
from torch.utils.data import Dataset
import os
import json
def load_data(partition):
all_data = []
all_label = []
for h5_name in glob.glob('./data/modelnet40_ply_hdf5_2048/ply_data_%s*.h5' % partition):
f = h5py.File(h5_name)
data = f['data'][:].a... | 6,371 | 37.853659 | 116 | py |
GDANet | GDANet-main/util/GDANet_util.py | import torch
from torch import nn
def knn(x, k):
inner = -2*torch.matmul(x.transpose(2, 1), x)
xx = torch.sum(x**2, dim=1, keepdim=True)
pairwise_distance = -xx - inner - xx.transpose(2, 1)
idx = pairwise_distance.topk(k=k, dim=-1)[1] # (batch_size, num_points, k)
return idx, pairwise_distance... | 7,130 | 32.478873 | 101 | py |
GDANet | GDANet-main/util/util.py | import numpy as np
import torch
import torch.nn.functional as F
def cal_loss(pred, gold, smoothing=True):
''' Calculate cross entropy loss, apply label smoothing if needed. '''
gold = gold.contiguous().view(-1) # gold is the groudtruth label in the dataloader
if smoothing:
eps = 0.2
n_cl... | 2,582 | 35.9 | 151 | py |
GDANet | GDANet-main/model/GDANet_ptseg.py | import torch.nn as nn
import torch
import torch.nn.functional as F
from util.GDANet_util import local_operator_withnorm, local_operator, GDM, SGCAM
class GDANet(nn.Module):
def __init__(self, num_classes):
super(GDANet, self).__init__()
self.bn1 = nn.BatchNorm2d(64, momentum=0.1)
self.bn1... | 4,987 | 37.96875 | 92 | py |
GDANet | GDANet-main/model/GDANet_cls.py | import torch.nn as nn
import torch
import torch.nn.functional as F
from util.GDANet_util import local_operator, GDM, SGCAM
class GDANET(nn.Module):
def __init__(self):
super(GDANET, self).__init__()
self.bn1 = nn.BatchNorm2d(64, momentum=0.1)
self.bn11 = nn.BatchNorm2d(64, momentum=0.1)
... | 4,241 | 36.210526 | 85 | py |
PosterLayout-CVPR2023 | PosterLayout-CVPR2023-main/dataloader.py | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Wed Nov 2 18:13:15 2022
@author: kinsleyhsu
"""
import os
import numpy as np
import torch
from torch.utils.data import Dataset, DataLoader
from torchvision import transforms
from pandas import read_csv
from PIL import Image
from designSeq import reorder
... | 4,825 | 34.485294 | 106 | py |
PosterLayout-CVPR2023 | PosterLayout-CVPR2023-main/eval.py | # -*- coding: utf-8 -*-
"""
Created on Sun Oct 23 20:18:00 2022
@author: kinsleyhsu
"""
import torch
import os
import copy
import numpy as np
import cv2
from PIL import Image, ImageDraw
from math import log
gpu = torch.cuda.is_available()
device_ids = [0, 1, 2, 3]
device = torch.device(f"cuda:{device_ids[0]}" if gpu... | 12,437 | 29.560197 | 115 | py |
PosterLayout-CVPR2023 | PosterLayout-CVPR2023-main/infer.py | # -*- coding: utf-8 -*-
"""
Created on Sat Oct 15 14:08:12 2022
@author: kinsleyhsu
"""
import torch
import torch.nn as nn
import torch.optim as optim
from torch.utils.data import DataLoader
from dataloader import canvas
from model import generator # import your model module
from PIL import Image, ImageDraw
import ma... | 3,151 | 28.457944 | 102 | py |
SGAttack | SGAttack-master/src/classifier/gcn.py | import tensorflow as tf
from distutils.version import LooseVersion
if LooseVersion(tf.__version__) > LooseVersion("1.14"):
import tensorflow.compat.v1 as tf
if LooseVersion(tf.__version__) > LooseVersion("2.0"):
tf.disable_v2_behavior()
import numpy as np
import scipy.sparse as sp
from tensorflow.keras.initial... | 9,629 | 41.422907 | 165 | py |
spacepy | spacepy-main/tests/test_plot_utils.py | #!/usr/bin/env python2.6
# -*- coding: utf-8 -*-
"""
Test suite for plot.utils
Copyright 2010-2015 Los Alamos National Security, LLC.
"""
try:
import cStringIO as StringIO
sio = StringIO.StringIO
except ImportError:
import io
sio = io.BytesIO
import datetime
import unittest
import warnings
import ma... | 6,543 | 38.185629 | 97 | py |
dipn | dipn-main/main.py | #!/usr/bin/env python
import time
import os
import random
import threading
import argparse
import matplotlib.pyplot as plt
import numpy as np
import scipy as sc
import cv2
from collections import namedtuple
import torch
from torch.autograd import Variable
from robot import Robot
from trainer import Trainer
from logger... | 47,659 | 55.805721 | 275 | py |
dipn | dipn-main/push_main.py | import time
import os
import random
import threading
import argparse
import matplotlib.pyplot as plt
import numpy as np
import scipy as sc
import cv2
from collections import namedtuple
import torch
from torch.autograd import Variable
from robot import Robot
from trainer import Trainer
from logger import Logger
import u... | 40,740 | 55.74234 | 264 | py |
dipn | dipn-main/train_maskrcnn.py | import torch
import torchvision
from dataset import SegmentationDataset
import utils
import argparse
import time
import os
from vision.coco_utils import get_coco_api_from_dataset
from vision.coco_eval import CocoEvaluator
import vision.transforms as T
import math
from torchvision.models.detection.faster_rcnn import Fas... | 10,784 | 37.655914 | 125 | py |
dipn | dipn-main/utils.py | import struct
from collections import defaultdict, deque
import time
import datetime
import torch.distributed as dist
import math
import numpy as np
import cv2
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.autograd import Variable
def get_pointcloud(color_img, depth_img, camera_intrins... | 23,455 | 36.5296 | 127 | py |
dipn | dipn-main/logger.py | import time
import datetime
import os
import numpy as np
import cv2
import torch
# import h5py
class Logger():
def __init__(self, continue_logging, logging_directory):
# Create directory to save data
timestamp = time.time()
timestamp_value = datetime.datetime.fromtimestamp(timestamp)
... | 6,784 | 59.044248 | 156 | py |
dipn | dipn-main/dataset.py | from torch.utils.data.sampler import Sampler
import os
import numpy as np
import torch
import torch.utils.data
import cv2
import imutils
from torchvision.transforms import functional as TF
from PIL import Image
import torchvision
import random
from constants import is_real, workspace_limits, heightmap_resolution, PUSH_... | 46,102 | 49.166485 | 312 | py |
dipn | dipn-main/train_push_prediction.py | import torch
from torchvision import transforms as T
from push_net import PushPredictionNet
from dataset import PushPredictionMultiDataset, ClusterRandomSampler
from torch.utils.data.sampler import RandomSampler
import utils
import argparse
import time
import os
import numpy as np
import cv2
from torch.utils.tensorboar... | 36,316 | 45.204835 | 279 | py |
dipn | dipn-main/action_utils_mask.py | import cv2
import imutils
import math
from constants import is_real, workspace_limits, DEPTH_MIN, colors_lower, colors_upper, resolution_pad, resolution, resolution_crop, padding_width, heightmap_resolution, distance
import random
import numpy as np
from scipy import spatial
import torch
from dataset import PushPredict... | 20,903 | 49.25 | 178 | py |
dipn | dipn-main/models.py | #!/usr/bin/env python
from collections import OrderedDict
import numpy as np
from scipy import ndimage
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.autograd import Variable
import torchvision
import matplotlib.pyplot as plt
import time
from vision.backbone_utils import resnet_fpn_net
f... | 14,982 | 47.332258 | 134 | py |
dipn | dipn-main/push_net.py | import torch
import torch.nn as nn
import torch.nn.functional as F
from vision.resnet import ResNetSmallNoFC, Bottleneck, BasicBlock
from vision.backbone_utils import resent_backbone
from collections import OrderedDict
from torchvision.models.segmentation.fcn import FCN, FCNHead
from torchvision.models.segmentation.dee... | 4,125 | 37.203704 | 115 | py |
dipn | dipn-main/train_foreground.py | import torch
from torchvision import transforms as T
from models import reinforcement_net
from dataset import ForegroundDataset
import utils
import argparse
import time
import os
from constants import PUSH_Q, GRASP_Q
def get_data_loader(dataset_root, batch_size, fine_tuning_num=None):
# use our dataset and define... | 8,946 | 36.911017 | 160 | py |
dipn | dipn-main/trainer.py | import os
import time
import numpy as np
import cv2
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.autograd import Variable
from utils import CrossEntropyLoss2d
from models import reinforcement_net, reactive_net
from scipy import ndimage
import matplotlib.pyplot as plt
from constants impo... | 39,811 | 53.536986 | 193 | py |
dipn | dipn-main/vision/backbone_utils.py | from collections import OrderedDict
from torch import nn
from torchvision.ops.feature_pyramid_network import FeaturePyramidNetwork, LastLevelMaxPool
import torch.nn.functional as F
from torchvision.ops import misc as misc_nn_ops
from ._utils import IntermediateLayerGetter
from . import resnet
from torchvision.models.se... | 13,594 | 44.166113 | 159 | py |
dipn | dipn-main/vision/_utils.py | from collections import OrderedDict
import torch
from torch import nn
from torch.jit.annotations import Dict
from torch.nn import functional as F
class IntermediateLayerGetter(nn.ModuleDict):
"""
Module wrapper that returns intermediate layers from a model
It has a strong assumption that the modules hav... | 2,641 | 37.289855 | 89 | py |
dipn | dipn-main/vision/resnet.py | import torch
import torch.nn as nn
from torchvision.models.utils import load_state_dict_from_url
__all__ = ['ResNet', 'resnet10', 'resnet18', 'resnet34', 'resnet50', 'resnet101',
'resnet152', 'resnext50_32x4d', 'resnext101_32x8d',
'wide_resnet50_2', 'wide_resnet101_2']
model_urls = {
'resn... | 18,977 | 39.989201 | 107 | py |
dipn | dipn-main/vision/coco_utils.py | import copy
import os
from PIL import Image
import torch
import torch.utils.data
import torchvision
from pycocotools import mask as coco_mask
from pycocotools.coco import COCO
class FilterAndRemapCocoCategories(object):
def __init__(self, categories, remap=True):
self.categories = categories
sel... | 7,759 | 34.272727 | 83 | py |
dipn | dipn-main/vision/coco_eval.py | import json
import tempfile
import numpy as np
import copy
import time
import torch
import torch._six
from pycocotools.cocoeval import COCOeval
from pycocotools.coco import COCO
import pycocotools.mask as mask_util
from collections import defaultdict
import utils
class CocoEvaluator(object):
def __init__(self... | 11,999 | 33.383954 | 107 | py |
dipn | dipn-main/vision/transforms.py | import random
import torch
from torchvision.transforms import functional as F
def _flip_coco_person_keypoints(kps, width):
flip_inds = [0, 2, 1, 4, 3, 6, 5, 8, 7, 10, 9, 12, 11, 14, 13, 16, 15]
flipped_data = kps[:, flip_inds]
flipped_data[..., 0] = width - flipped_data[..., 0]
# Maintain COCO conven... | 1,358 | 28.543478 | 74 | py |
Impact-of-FC-layers | Impact-of-FC-layers-master/Source Codes/CIFAR-10/CNN2_CIFAR10.py | from __future__ import print_function
import keras
from keras.utils import np_utils
import scipy
from keras.datasets import cifar10
from keras.preprocessing.image import ImageDataGenerator
from keras.models import Sequential
from keras.preprocessing import image
from keras.applications.imagenet_utils import preprocess_... | 7,662 | 39.760638 | 120 | py |
Impact-of-FC-layers | Impact-of-FC-layers-master/Source Codes/CIFAR-10/CNN4_CIFAR-10.py | from __future__ import print_function
import keras
from keras.utils import np_utils
from keras.datasets import cifar10
from keras.preprocessing.image import ImageDataGenerator
from keras.models import Sequential
from keras.preprocessing import image
from keras.applications.imagenet_utils import preprocess_input
from k... | 9,307 | 41.697248 | 120 | py |
Impact-of-FC-layers | Impact-of-FC-layers-master/Source Codes/CIFAR-10/CNN1_CIFAR10.py | from __future__ import print_function
import keras
from keras.utils import np_utils
import scipy
from keras.datasets import cifar10
from keras.preprocessing.image import ImageDataGenerator
from keras.models import Sequential
from keras.preprocessing import image
from keras.applications.imagenet_utils import preprocess_... | 6,958 | 40.177515 | 143 | py |
Impact-of-FC-layers | Impact-of-FC-layers-master/Source Codes/CIFAR-10/CNN3_CIFAR10.py | from __future__ import print_function
import keras
from keras.utils import np_utils
import scipy
from keras.datasets import cifar10
from keras.preprocessing.image import ImageDataGenerator
from keras.models import Sequential
from keras.preprocessing import image
from keras.applications.imagenet_utils import preprocess_... | 8,874 | 42.719212 | 139 | py |
Impact-of-FC-layers | Impact-of-FC-layers-master/Source Codes/Tiny ImageNet/CNN1_TinyImageNet.py | from __future__ import print_function
import keras
from keras.utils import np_utils
import scipy
from keras.preprocessing.image import ImageDataGenerator
from keras.models import Sequential
from keras.preprocessing import image
from keras.applications.imagenet_utils import preprocess_input
from keras.layers import Dens... | 8,411 | 36.891892 | 120 | py |
Impact-of-FC-layers | Impact-of-FC-layers-master/Source Codes/Tiny ImageNet/CNN2_TinyImageNet.py | from __future__ import print_function
import keras
from keras.utils import np_utils
import scipy
from keras.preprocessing.image import ImageDataGenerator
from keras.models import Sequential
from keras.preprocessing import image
from keras.applications.imagenet_utils import preprocess_input
from keras.layers import Dens... | 9,158 | 37.483193 | 120 | py |
Impact-of-FC-layers | Impact-of-FC-layers-master/Source Codes/Tiny ImageNet/CNN3_TinyImageNet.py | from __future__ import print_function
import keras
from keras.utils import np_utils
import scipy
from keras.preprocessing.image import ImageDataGenerator
from keras.models import Sequential
from keras.preprocessing import image
from keras.applications.imagenet_utils import preprocess_input
from keras.layers import Dens... | 9,682 | 38.361789 | 120 | py |
Impact-of-FC-layers | Impact-of-FC-layers-master/Source Codes/CRC_HistoPhenoTypes/CNN3_CRC.py | from __future__ import print_function
import keras
from keras.utils import np_utils
from keras.datasets import cifar10
from keras.preprocessing.image import ImageDataGenerator
from keras.models import Sequential
from keras.preprocessing import image
from keras.applications.imagenet_utils import preprocess_input
from k... | 11,721 | 41.31769 | 139 | py |
Impact-of-FC-layers | Impact-of-FC-layers-master/Source Codes/CRC_HistoPhenoTypes/CNN2_CRC.py | from __future__ import print_function
import keras
from keras.utils import np_utils
from keras.datasets import cifar10
from keras.preprocessing.image import ImageDataGenerator
from keras.models import Sequential
from keras.preprocessing import image
from keras.applications.imagenet_utils import preprocess_input
from k... | 10,614 | 39.056604 | 120 | py |
Impact-of-FC-layers | Impact-of-FC-layers-master/Source Codes/CRC_HistoPhenoTypes/CNN4_CRC.py | from __future__ import print_function
import keras
from keras.utils import np_utils
from keras.datasets import cifar10
from keras.preprocessing.image import ImageDataGenerator
from keras.models import Sequential
from keras.preprocessing import image
from keras.applications.imagenet_utils import preprocess_input
from k... | 10,751 | 41.330709 | 120 | py |
Impact-of-FC-layers | Impact-of-FC-layers-master/Source Codes/CRC_HistoPhenoTypes/CNN1_CRC.py | from __future__ import print_function
from keras.utils import np_utils
from keras.datasets import cifar10
from keras.preprocessing.image import ImageDataGenerator
from keras.models import Sequential
from keras.preprocessing import image
from keras.applications.imagenet_utils import preprocess_input
from keras.layers im... | 9,325 | 39.547826 | 143 | py |
Impact-of-FC-layers | Impact-of-FC-layers-master/Source Codes/CIFAR-100/CNN1_CIFAR100.py | from __future__ import print_function
import keras
from keras.utils import np_utils
import scipy
from keras.datasets import cifar100
from keras.preprocessing.image import ImageDataGenerator
from keras.models import Sequential
from keras.preprocessing import image
from keras.applications.imagenet_utils import preprocess... | 6,999 | 40.176471 | 143 | py |
Impact-of-FC-layers | Impact-of-FC-layers-master/Source Codes/CIFAR-100/CNN3_CIFAR100.py | from __future__ import print_function
import keras
from keras.utils import np_utils
import scipy
from keras.datasets import cifar100
from keras.preprocessing.image import ImageDataGenerator
from keras.models import Sequential
from keras.preprocessing import image
from keras.applications.imagenet_utils import preprocess... | 8,887 | 42.783251 | 139 | py |
Impact-of-FC-layers | Impact-of-FC-layers-master/Source Codes/CIFAR-100/CNN4_CIFAR-100.py | from __future__ import print_function
import keras
from keras.utils import np_utils
from keras.datasets import cifar10
from keras.preprocessing.image import ImageDataGenerator
from keras.models import Sequential
from keras.preprocessing import image
from keras.applications.imagenet_utils import preprocess_input
from k... | 9,310 | 41.711009 | 120 | py |
Impact-of-FC-layers | Impact-of-FC-layers-master/Source Codes/CIFAR-100/CNN2_CIFAR100.py | from __future__ import print_function
import keras
from keras.utils import np_utils
import scipy
from keras.datasets import cifar100
from keras.preprocessing.image import ImageDataGenerator
from keras.models import Sequential
from keras.preprocessing import image
from keras.applications.imagenet_utils import preprocess... | 7,426 | 39.145946 | 120 | py |
cmcl_vqa_pl | cmcl_vqa_pl-master/run.py | import os
import copy
import pytorch_lightning as pl
os.environ["NCCL_DEBUG"] = "INFO"
from meter.config import ex
from meter.modules import METERTransformerSS
from meter.datamodules.multitask_datamodule import MTDataModule
import resource
rlimit = resource.getrlimit(resource.RLIMIT_NOFILE)
# resource.setrlimit(resou... | 2,365 | 29.333333 | 97 | py |
cmcl_vqa_pl | cmcl_vqa_pl-master/meter/datamodules/f30k_caption_karpathy_datamodule.py | from ..datasets import F30KCaptionKarpathyDataset
from .datamodule_base import BaseDataModule
from torch.utils.data import DataLoader
class F30KCaptionKarpathyDataModule(BaseDataModule):
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
@property
def dataset_cls(self):
... | 1,414 | 25.203704 | 52 | py |
cmcl_vqa_pl | cmcl_vqa_pl-master/meter/datamodules/multitask_datamodule.py | import functools
from pytorch_lightning import LightningDataModule
from torch.utils.data import DataLoader
from torch.utils.data.dataset import ConcatDataset
from torch.utils.data.distributed import DistributedSampler
from . import _datamodules
class MTDataModule(LightningDataModule):
def __init__(self, _config... | 2,711 | 31.674699 | 85 | py |
cmcl_vqa_pl | cmcl_vqa_pl-master/meter/datamodules/datamodule_base.py | import torch
from pytorch_lightning import LightningDataModule
from torch.utils.data import DataLoader
from transformers import (
DataCollatorForLanguageModeling,
DataCollatorForWholeWordMask,
BertTokenizer,
RobertaTokenizer,
AutoModelForTokenClassification
)
def get_pretrained_tokenizer(from_pre... | 6,133 | 30.947917 | 83 | py |
cmcl_vqa_pl | cmcl_vqa_pl-master/meter/datamodules/multitask_datamodule_vlp.py | import functools
from pytorch_lightning import LightningDataModule
from torch.utils.data import DataLoader
from torch.utils.data.dataset import ConcatDataset
from torch.utils.data.distributed import DistributedSampler
from . import _datamodules
class MTDataModule(LightningDataModule):
def __init__(self, _config... | 2,755 | 31.809524 | 85 | py |
cmcl_vqa_pl | cmcl_vqa_pl-master/meter/datamodules/nl_datamodule.py | import functools
from pytorch_lightning import LightningDataModule
from torch.utils.data import DataLoader
from torch.utils.data.dataset import ConcatDataset
from torch.utils.data.distributed import DistributedSampler
from . import _datamodules
class NLDataModule(LightningDataModule):
def __init__(self, _config... | 2,720 | 32.182927 | 85 | py |
cmcl_vqa_pl | cmcl_vqa_pl-master/meter/gadgets/my_metrics.py | import torch
from pytorch_lightning.metrics import Metric
class Accuracy(Metric):
def __init__(self, dist_sync_on_step=False):
super().__init__(dist_sync_on_step=dist_sync_on_step)
self.add_state("correct", default=torch.tensor(0.0), dist_reduce_fx="sum")
self.add_state("total", default=to... | 2,359 | 32.714286 | 82 | py |
cmcl_vqa_pl | cmcl_vqa_pl-master/meter/modules/clip_model.py | from collections import OrderedDict
from typing import Tuple, Union
import numpy as np
import torch
import torch.nn.functional as F
from torch import nn
class LayerNorm(nn.LayerNorm):
"""Subclass torch's LayerNorm to handle fp16."""
def forward(self, x: torch.Tensor):
orig_type = x.dtype
ret... | 11,209 | 39.179211 | 142 | py |
cmcl_vqa_pl | cmcl_vqa_pl-master/meter/modules/meter_utils.py | import torch
import random
from transformers.optimization import AdamW
from transformers import (
get_polynomial_decay_schedule_with_warmup,
get_cosine_schedule_with_warmup,
)
from .dist_utils import all_gather
from .objectives import compute_irtr_recall
from ..gadgets.my_metrics import Accuracy, VQAScore, Sca... | 12,258 | 38.801948 | 100 | py |
cmcl_vqa_pl | cmcl_vqa_pl-master/meter/modules/swin_transformer.py | """ Swin Transformer
A PyTorch impl of : `Swin Transformer: Hierarchical Vision Transformer using Shifted Windows`
- https://arxiv.org/pdf/2103.14030
Code/weights from https://github.com/microsoft/Swin-Transformer, original copyright/license info below
"""
# --------------------------------------------------------
... | 27,086 | 41.191589 | 125 | py |
cmcl_vqa_pl | cmcl_vqa_pl-master/meter/modules/bert_model.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... | 76,759 | 41.858738 | 213 | py |
cmcl_vqa_pl | cmcl_vqa_pl-master/meter/modules/meter_module.py | import torch
import torch.nn as nn
import pytorch_lightning as pl
import numpy as np
from transformers.models.bert.modeling_bert import BertConfig, BertModel, BertForMaskedLM
from .bert_model import BertCrossLayer
from . import swin_transformer as swin
from . import heads, objectives, meter_utils
from .clip_model impo... | 17,580 | 43.84949 | 138 | py |
cmcl_vqa_pl | cmcl_vqa_pl-master/meter/modules/dist_utils.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
"""
This file contains primitives for multi-gpu communication.
This is useful when doing distributed training.
"""
import functools
import logging
import numpy as np
import pickle
import torch
import torch.distributed as dist
import torch
_LOCAL_... | 7,814 | 27.837638 | 100 | py |
cmcl_vqa_pl | cmcl_vqa_pl-master/meter/modules/objectives.py | import torch
import torch.nn as nn
import torch.nn.functional as F
import os
import glob
import json
import tqdm
import functools
from torch.utils.data.distributed import DistributedSampler
from einops import rearrange
from .dist_utils import all_gather
def compute_mlm_oracle(pl_module, batch):
infer = pl_modul... | 19,551 | 34.484574 | 109 | py |
cmcl_vqa_pl | cmcl_vqa_pl-master/meter/modules/swin_helpers.py | """ Model creation / weight loading / state_dict helpers
Hacked together by / Copyright 2020 Ross Wightman
"""
import logging
import os
import math
from collections import OrderedDict
from copy import deepcopy
from typing import Any, Callable, Optional, Tuple
import torch
import torch.nn as nn
from timm.models.featu... | 23,446 | 43.323251 | 130 | py |
cmcl_vqa_pl | cmcl_vqa_pl-master/meter/modules/heads.py | import torch
import torch.nn as nn
import torch.nn.functional as F
from transformers.models.bert.modeling_bert import BertPredictionHeadTransform
class Pooler(nn.Module):
def __init__(self, hidden_size):
super().__init__()
self.dense = nn.Linear(hidden_size, hidden_size)
self.activation =... | 1,257 | 27.590909 | 83 | py |
cmcl_vqa_pl | cmcl_vqa_pl-master/meter/modules/beit_helpers.py | import torch
import json, os
from transformers import BeitModel, BeitConfig, BeitForMaskedImageModeling
def build_beit_model(original_config_dir, image_size):
config = json.load(open(os.path.join(original_config_dir, 'config.json'), 'r'))
if config['image_size'] == image_size:
return BeitForMaskedImag... | 1,649 | 42.421053 | 132 | py |
cmcl_vqa_pl | cmcl_vqa_pl-master/meter/modules/meter_clip_score.py | import torch
import torch.nn as nn
import pytorch_lightning as pl
import numpy as np
import clip
from transformers.models.bert.modeling_bert import BertConfig, BertModel, BertForMaskedLM
from .bert_model import BertCrossLayer
from . import swin_transformer as swin
from . import heads, objectives, meter_utils
from .cli... | 12,330 | 44.168498 | 138 | py |
cmcl_vqa_pl | cmcl_vqa_pl-master/meter/datasets/base_cv_dataset.py | import torch
import os, io, random
import pyarrow as pa
import numpy as np
from PIL import Image
from ..transforms import keys_to_transforms
class BaseCVDataset(torch.utils.data.Dataset):
def __init__(
self,
data_dir: str,
transform_keys: list,
image_size: int,
names: list,... | 4,744 | 32.892857 | 111 | py |
cmcl_vqa_pl | cmcl_vqa_pl-master/meter/datasets/base_dataset.py | import random
import torch
import io
import pyarrow as pa
import os
from PIL import Image
from ..transforms import keys_to_transforms
class BaseDataset(torch.utils.data.Dataset):
def __init__(
self,
data_dir: str,
transform_keys: list,
image_size: int,
names: list,
... | 10,145 | 36.577778 | 111 | py |
cmcl_vqa_pl | cmcl_vqa_pl-master/meter/datasets/base_nlp_dataset.py | import torch
from torch.utils.data import Dataset, random_split
from datasets import load_from_disk
class BaseNLPDataset(Dataset):
def __init__(self, data_dir, split, max_text_len=512):
super().__init__()
self.data_dir = data_dir
self.max_text_len = max_text_len
dataset = load_fro... | 3,425 | 40.780488 | 91 | py |
cmcl_vqa_pl | cmcl_vqa_pl-master/meter/datasets/rte_dataset.py | import os
from .base_nlp_dataset import BaseNLPDataset
import torch
class RTEDataset(BaseNLPDataset):
def __init__(self, data_dir, split, max_text_len=512):
data_dir = os.path.join(data_dir, 'rte')
super().__init__(data_dir, split, max_text_len)
def __getitem__(self, index):
data = sel... | 1,514 | 37.846154 | 104 | py |
cmcl_vqa_pl | cmcl_vqa_pl-master/meter/transforms/transform.py | from .utils import (
inception_normalize,
imagenet_normalize,
MinMaxResize,
)
from PIL import Image
from torchvision import transforms
from torchvision.transforms import Compose, Resize, CenterCrop, ToTensor, Normalize
from .randaug import RandAugment
from transformers import DetrFeatureExtractor
from funct... | 3,178 | 28.435185 | 96 | py |
cmcl_vqa_pl | cmcl_vqa_pl-master/meter/transforms/utils.py | from torchvision import transforms
from PIL import Image
class MinMaxResize:
def __init__(self, shorter=800, longer=1333):
self.min = shorter
self.max = longer
def __call__(self, x):
w, h = x.size
scale = self.min / min(w, h)
if h < w:
newh, neww = self.min... | 1,792 | 27.919355 | 98 | py |
cmcl_vqa_pl | cmcl_vqa_pl-master/meter/transforms/randaug.py | # code in this file is adpated from rpmcruz/autoaugment
# https://github.com/rpmcruz/autoaugment/blob/master/transformations.py
import random
import PIL, PIL.ImageOps, PIL.ImageEnhance, PIL.ImageDraw
import numpy as np
import torch
from PIL import Image
def ShearX(img, v): # [-0.3, 0.3]
assert -0.3 <= v <= 0.3
... | 6,990 | 24.892593 | 134 | py |
ChineseGLUE | ChineseGLUE-master/baselines/models/xlnet/data_utils.py | # -*- coding: utf-8 -*-
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import json
import os
import random
from absl import flags
import absl.logging as _logging # pylint: disable=unused-import
import numpy as np
import tensorflow as tf
from prepro_u... | 29,866 | 31.605895 | 80 | py |
ChineseGLUE | ChineseGLUE-master/baselines/models_pytorch/classifier_pytorch/run_classifier.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... | 29,378 | 55.173996 | 181 | py |
ChineseGLUE | ChineseGLUE-master/baselines/models_pytorch/classifier_pytorch/convert_albert_original_tf_checkpoint_to_pytorch.py | # coding=utf-8
# Copyright 2018 The HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable... | 2,991 | 40.555556 | 110 | py |
ChineseGLUE | ChineseGLUE-master/baselines/models_pytorch/classifier_pytorch/convert_ernie_original_pad_checkpoint_to_pytorch.py | #!/usr/bin/env python
# encoding: utf-8
import collections
import os
import sys
import numpy as np
import argparse
import paddle.fluid as fluid
import torch
import json
if not os.path.exists('ERNIE'):
os.system('git clone https://github.com/PaddlePaddle/ERNIE.git')
sys.path = ['./ERNIE'] + sys.path
try:
from m... | 8,630 | 38.774194 | 119 | py |
ChineseGLUE | ChineseGLUE-master/baselines/models_pytorch/classifier_pytorch/convert_bert_original_tf_checkpoint_to_pytorch.py | # coding=utf-8
# Copyright 2018 The HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable... | 2,577 | 38.060606 | 101 | py |
ChineseGLUE | ChineseGLUE-master/baselines/models_pytorch/classifier_pytorch/convert_xlnet_original_tf_checkpoint_to_pytorch.py | # coding=utf-8
# Copyright 2018 The HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable... | 4,334 | 40.285714 | 126 | py |
ChineseGLUE | ChineseGLUE-master/baselines/models_pytorch/classifier_pytorch/tools/common.py | import os
import random
import torch
import numpy as np
import json
import pickle
import torch.nn as nn
from collections import OrderedDict
from pathlib import Path
import logging
logger = logging.getLogger()
def print_config(config):
info = "Running with the following configs:\n"
for k, v in config.items():
... | 10,704 | 29.240113 | 128 | py |
ChineseGLUE | ChineseGLUE-master/baselines/models_pytorch/classifier_pytorch/processors/glue.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... | 24,809 | 34.544413 | 130 | py |
ChineseGLUE | ChineseGLUE-master/baselines/models_pytorch/classifier_pytorch/transformers/optimization.py | # coding=utf-8
# Copyright 2018 The Google AI Language Team Authors and The HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICEN... | 8,635 | 44.452632 | 130 | py |
ChineseGLUE | ChineseGLUE-master/baselines/models_pytorch/classifier_pytorch/transformers/__main__.py | # coding: utf8
def main():
import sys
if (len(sys.argv) < 4 or len(sys.argv) > 6) or sys.argv[1] not in ["bert", "gpt", "transfo_xl", "gpt2", "xlnet", "xlm"]:
print(
"This command line utility let you convert original (author released) model checkpoint to pytorch.\n"
"It should be used a... | 7,082 | 53.484615 | 135 | py |
ChineseGLUE | ChineseGLUE-master/baselines/models_pytorch/classifier_pytorch/transformers/configuration_utils.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... | 10,772 | 50.793269 | 296 | py |
ChineseGLUE | ChineseGLUE-master/baselines/models_pytorch/classifier_pytorch/transformers/modeling_distilbert.py | # coding=utf-8
# Copyright 2019-present, the HuggingFace Inc. team, The Google AI Language Team and Facebook, Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.or... | 34,864 | 49.237752 | 201 | py |
ChineseGLUE | ChineseGLUE-master/baselines/models_pytorch/classifier_pytorch/transformers/modeling_utils.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... | 43,409 | 52.06846 | 472 | py |
ChineseGLUE | ChineseGLUE-master/baselines/models_pytorch/classifier_pytorch/transformers/modeling_bert.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... | 59,643 | 50.864348 | 187 | py |
ChineseGLUE | ChineseGLUE-master/baselines/models_pytorch/classifier_pytorch/transformers/modeling_gpt2.py | # coding=utf-8
# Copyright 2018 The OpenAI Team Authors and 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 copy of the License... | 33,126 | 48.965309 | 148 | py |
ChineseGLUE | ChineseGLUE-master/baselines/models_pytorch/classifier_pytorch/transformers/modeling_openai.py | # coding=utf-8
# Copyright 2018 The OpenAI Team Authors and 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 copy of the License... | 30,836 | 48.57717 | 148 | py |
ChineseGLUE | ChineseGLUE-master/baselines/models_pytorch/classifier_pytorch/transformers/tokenization_bert.py | # coding=utf-8
# Copyright 2018 The Google AI Language Team Authors and The HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICEN... | 22,451 | 43.636183 | 183 | py |
ChineseGLUE | ChineseGLUE-master/baselines/models_pytorch/classifier_pytorch/transformers/configuration_ctrl.py | # coding=utf-8
# Copyright 2018 Salesforce and 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 copy of the License at
#
# htt... | 5,775 | 39.111111 | 120 | py |
ChineseGLUE | ChineseGLUE-master/baselines/models_pytorch/classifier_pytorch/transformers/file_utils.py | """
Utilities for working with the local dataset cache.
This file is adapted from the AllenNLP library at https://github.com/allenai/allennlp
Copyright by the AllenNLP authors.
"""
from __future__ import (absolute_import, division, print_function, unicode_literals)
import sys
import json
import logging
import os
impor... | 11,622 | 34.763077 | 144 | py |
ChineseGLUE | ChineseGLUE-master/baselines/models_pytorch/classifier_pytorch/transformers/__init__.py | __version__ = "2.1.1"
# Work around to update TensorFlow's absl.logging threshold which alters the
# default Python logging output behavior when present.
# see: https://github.com/abseil/abseil-py/issues/99
# and: https://github.com/tensorflow/tensorflow/issues/26691#issuecomment-500369493
try:
import absl.logging... | 5,761 | 58.402062 | 109 | py |
ChineseGLUE | ChineseGLUE-master/baselines/models_pytorch/classifier_pytorch/transformers/modeling_transfo_xl.py | # coding=utf-8
# Copyright 2018 Google AI, Google Brain and Carnegie Mellon University 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 Lice... | 39,657 | 43.50954 | 157 | py |
ChineseGLUE | ChineseGLUE-master/baselines/models_pytorch/classifier_pytorch/transformers/modeling_albert.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... | 54,163 | 49.810507 | 153 | py |
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