repo stringlengths 2 99 | file stringlengths 13 225 | code stringlengths 0 18.3M | file_length int64 0 18.3M | avg_line_length float64 0 1.36M | max_line_length int64 0 4.26M | extension_type stringclasses 1
value |
|---|---|---|---|---|---|---|
transformers | transformers-main/utils/check_config_docstrings.py | # coding=utf-8
# Copyright 2022 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... | 3,279 | 34.268817 | 105 | py |
transformers | transformers-main/utils/notification_service_doc_tests.py | # Copyright 2022 The HuggingFace Team. 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
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicabl... | 12,860 | 32.755906 | 117 | py |
transformers | transformers-main/utils/custom_init_isort.py | # coding=utf-8
# Copyright 2021 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... | 10,282 | 39.644269 | 116 | py |
transformers | transformers-main/utils/check_self_hosted_runner.py | import argparse
import json
import subprocess
def get_runner_status(target_runners, token):
offline_runners = []
cmd = (
f'curl -H "Accept: application/vnd.github+json" -H "Authorization: Bearer {token}"'
" https://api.github.com/repos/huggingface/transformers/actions/runners"
)
outpu... | 1,538 | 28.037736 | 106 | py |
transformers | transformers-main/utils/update_tiny_models.py | # coding=utf-8
# Copyright 2023 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... | 7,272 | 35.18408 | 119 | py |
transformers | transformers-main/utils/check_doc_toc.py | # coding=utf-8
# Copyright 2022 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... | 3,385 | 33.20202 | 116 | py |
transformers | transformers-main/utils/tests_fetcher.py | # coding=utf-8
# Copyright 2021 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... | 36,418 | 40.574201 | 120 | py |
transformers | transformers-main/utils/check_doctest_list.py | # coding=utf-8
# Copyright 2023 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... | 1,574 | 38.375 | 111 | py |
transformers | transformers-main/utils/get_modified_files.py | # coding=utf-8
# Copyright 2020 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... | 1,506 | 39.72973 | 122 | py |
transformers | transformers-main/utils/print_env.py | #!/usr/bin/env python3
# coding=utf-8
# Copyright 2020 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
#
# Unles... | 1,723 | 28.724138 | 86 | py |
transformers | transformers-main/utils/create_dummy_models.py | # coding=utf-8
# Copyright 2022 The HuggingFace Inc. team. 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
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless r... | 67,548 | 42.834523 | 120 | py |
transformers | transformers-main/utils/check_copies.py | # coding=utf-8
# Copyright 2020 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... | 23,859 | 40.067126 | 126 | py |
transformers | transformers-main/utils/check_tf_ops.py | # coding=utf-8
# Copyright 2020 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... | 3,574 | 34.04902 | 119 | py |
transformers | transformers-main/utils/get_github_job_time.py | import argparse
import math
import traceback
import dateutil.parser as date_parser
import requests
def extract_time_from_single_job(job):
"""Extract time info from a single job in a GitHub Actions workflow run"""
job_info = {}
start = job["started_at"]
end = job["completed_at"]
start_datetime ... | 2,190 | 29.430556 | 115 | py |
transformers | transformers-main/utils/check_inits.py | # coding=utf-8
# Copyright 2020 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... | 12,878 | 41.088235 | 117 | py |
transformers | transformers-main/utils/check_model_tester.py | # coding=utf-8
# Copyright 2023 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... | 2,598 | 39.609375 | 120 | py |
transformers | transformers-main/utils/sort_auto_mappings.py | # coding=utf-8
# Copyright 2022 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... | 3,313 | 35.822222 | 119 | py |
transformers | transformers-main/utils/past_ci_versions.py | import argparse
import os
past_versions_testing = {
"pytorch": {
"1.13": {
"torch": "1.13.1",
"torchvision": "0.14.1",
"torchaudio": "0.13.1",
"python": 3.9,
"cuda": "cu116",
"install": (
"python3 -m pip install --no-c... | 4,995 | 38.338583 | 114 | py |
transformers | transformers-main/utils/release.py | # coding=utf-8
# Copyright 2021 The HuggingFace Team. 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
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless requir... | 6,207 | 38.044025 | 115 | py |
transformers | transformers-main/utils/get_previous_daily_ci.py | import os
import zipfile
import requests
from get_ci_error_statistics import download_artifact, get_artifacts_links
def get_daily_ci_runs(token, num_runs=7):
"""Get the workflow runs of the scheduled (daily) CI.
This only selects the runs triggered by the `schedule` event on the `main` branch.
"""
h... | 2,763 | 37.929577 | 110 | py |
transformers | transformers-main/utils/get_test_info.py | # coding=utf-8
# Copyright 2023 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... | 7,048 | 35.905759 | 118 | py |
transformers | transformers-main/utils/download_glue_data.py | """ Script for downloading all GLUE data.
Original source: https://gist.github.com/W4ngatang/60c2bdb54d156a41194446737ce03e2e
Note: for legal reasons, we are unable to host MRPC.
You can either use the version hosted by the SentEval team, which is already tokenized,
or you can download the original data from (https://... | 8,285 | 51.443038 | 615 | py |
transformers | transformers-main/utils/extract_warnings.py | import argparse
import json
import os
import time
import zipfile
from get_ci_error_statistics import download_artifact, get_artifacts_links
from transformers import logging
logger = logging.get_logger(__name__)
def extract_warnings_from_single_artifact(artifact_path, targets):
"""Extract warnings from a downl... | 4,692 | 33.762963 | 112 | py |
transformers | transformers-main/utils/notification_service.py | # Copyright 2020 The HuggingFace Team. 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
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicabl... | 41,883 | 39.545983 | 119 | py |
transformers | transformers-main/utils/add_pipeline_model_mapping_to_test.py | # coding=utf-8
# Copyright 2023 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... | 13,870 | 40.038462 | 117 | py |
transformers | transformers-main/utils/check_repo.py | # coding=utf-8
# Copyright 2020 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... | 48,398 | 41.492537 | 128 | py |
transformers | transformers-main/utils/check_build.py | # coding=utf-8
# Copyright 2023 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... | 1,807 | 35.897959 | 117 | py |
transformers | transformers-main/utils/get_ci_error_statistics.py | import argparse
import json
import math
import os
import time
import traceback
import zipfile
from collections import Counter
import requests
def get_job_links(workflow_run_id, token=None):
"""Extract job names and their job links in a GitHub Actions workflow run"""
headers = None
if token is not None:
... | 10,403 | 36.42446 | 120 | py |
transformers | transformers-main/utils/check_config_attributes.py | # coding=utf-8
# Copyright 2023 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... | 12,599 | 42.298969 | 118 | py |
transformers | transformers-main/utils/update_metadata.py | # coding=utf-8
# Copyright 2021 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... | 12,459 | 42.114187 | 118 | py |
transformers | transformers-main/utils/check_dummies.py | # coding=utf-8
# Copyright 2020 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... | 6,221 | 35.816568 | 118 | py |
transformers | transformers-main/utils/check_table.py | # coding=utf-8
# Copyright 2020 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... | 7,490 | 40.159341 | 116 | py |
transformers | transformers-main/utils/test_module/custom_image_processing.py | from transformers import CLIPImageProcessor
class CustomImageProcessor(CLIPImageProcessor):
pass
| 103 | 16.333333 | 47 | py |
transformers | transformers-main/utils/test_module/custom_tokenization_fast.py | from transformers import BertTokenizerFast
from .custom_tokenization import CustomTokenizer
class CustomTokenizerFast(BertTokenizerFast):
slow_tokenizer_class = CustomTokenizer
pass
| 193 | 20.555556 | 48 | py |
transformers | transformers-main/utils/test_module/custom_pipeline.py | import numpy as np
from transformers import Pipeline
def softmax(outputs):
maxes = np.max(outputs, axis=-1, keepdims=True)
shifted_exp = np.exp(outputs - maxes)
return shifted_exp / shifted_exp.sum(axis=-1, keepdims=True)
class PairClassificationPipeline(Pipeline):
def _sanitize_parameters(self, **... | 1,110 | 31.676471 | 89 | py |
transformers | transformers-main/utils/test_module/custom_tokenization.py | from transformers import BertTokenizer
class CustomTokenizer(BertTokenizer):
pass
| 88 | 13.833333 | 38 | py |
transformers | transformers-main/utils/test_module/__init__.py | 0 | 0 | 0 | py | |
transformers | transformers-main/utils/test_module/custom_processing.py | from transformers import ProcessorMixin
class CustomProcessor(ProcessorMixin):
feature_extractor_class = "AutoFeatureExtractor"
tokenizer_class = "AutoTokenizer"
| 172 | 23.714286 | 52 | py |
transformers | transformers-main/utils/test_module/custom_feature_extraction.py | from transformers import Wav2Vec2FeatureExtractor
class CustomFeatureExtractor(Wav2Vec2FeatureExtractor):
pass
| 117 | 18.666667 | 55 | py |
transformers | transformers-main/utils/test_module/custom_modeling.py | import torch
from transformers import PreTrainedModel
from .custom_configuration import CustomConfig, NoSuperInitConfig
class CustomModel(PreTrainedModel):
config_class = CustomConfig
def __init__(self, config):
super().__init__(config)
self.linear = torch.nn.Linear(config.hidden_size, conf... | 772 | 21.735294 | 77 | py |
transformers | transformers-main/utils/test_module/custom_configuration.py | from transformers import PretrainedConfig
class CustomConfig(PretrainedConfig):
model_type = "custom"
def __init__(self, attribute=1, **kwargs):
self.attribute = attribute
super().__init__(**kwargs)
class NoSuperInitConfig(PretrainedConfig):
model_type = "custom"
def __init__(self,... | 380 | 21.411765 | 46 | py |
tiny-faces-pytorch | tiny-faces-pytorch-master/main.py | import argparse
import os
import os.path as osp
import torch
from torch import optim
from torchvision import transforms
import trainer
from datasets import get_dataloader
from models.loss import DetectionCriterion
from models.model import DetectionModel
def arguments():
parser = argparse.ArgumentParser()
p... | 3,338 | 33.78125 | 92 | py |
tiny-faces-pytorch | tiny-faces-pytorch-master/evaluate.py | import argparse
import json
import os
import os.path as osp
import numpy as np
import torch
from PIL import Image
from torch.utils import data
from torchvision import transforms
from tqdm import tqdm
import trainer
from datasets import get_dataloader
from datasets.wider_face import WIDERFace
from models.model import ... | 3,789 | 31.393162 | 83 | py |
tiny-faces-pytorch | tiny-faces-pytorch-master/trainer.py | from pathlib import Path
import numpy as np
import torch
from torch.nn import functional as nnfunc
from torchvision import transforms
from models.utils import get_bboxes
from utils.nms import nms
def print_state(idx, epoch, size, loss_cls, loss_reg):
if epoch >= 0:
message = "Epoch: [{0}][{1}/{2}]\t".fo... | 4,459 | 29.972222 | 89 | py |
tiny-faces-pytorch | tiny-faces-pytorch-master/models/loss.py | import numpy as np
import torch
from torch import nn
from .utils import balance_sampling
class AvgMeter:
def __init__(self):
self.average = 0
self.num_averaged = 0
def update(self, loss, size):
n = self.num_averaged
m = n + size
self.average = ((n * self.average) + fl... | 3,298 | 32.663265 | 91 | py |
tiny-faces-pytorch | tiny-faces-pytorch-master/models/utils.py | import numpy as np
def get_bboxes(score_cls, score_reg, prob_cls, templates, prob_thresh, rf, scale=1, refine=True):
"""
Convert model output tensor to a set of bounding boxes and their corresponding scores
"""
num_templates = templates.shape[0]
# template to evaluate at every scale (Type A templ... | 5,274 | 32.386076 | 97 | py |
tiny-faces-pytorch | tiny-faces-pytorch-master/models/model.py | import numpy as np
import torch
from torch import nn
from torchvision.models import resnet50, resnet101
class DetectionModel(nn.Module):
"""
Hybrid Model from Tiny Faces paper
"""
def __init__(self, base_model=resnet101, num_templates=1, num_objects=1):
super().__init__()
# 4 is for t... | 3,496 | 32.304762 | 97 | py |
tiny-faces-pytorch | tiny-faces-pytorch-master/models/__init__.py | 0 | 0 | 0 | py | |
tiny-faces-pytorch | tiny-faces-pytorch-master/datasets/processor.py | import numpy as np
from copy import deepcopy
from utils.visualize import draw_bounding_box, render_and_save_bboxes, visualize_bboxes
from utils.nms import nms
from utils.metrics import rect_dist
from utils.dense_overlap import compute_dense_overlap
import logging
logger = logging.getLogger("detector")
class DataPr... | 12,457 | 39.448052 | 115 | py |
tiny-faces-pytorch | tiny-faces-pytorch-master/datasets/__init__.py | import numpy as np
import os
import os.path as osp
import json
from utils.cluster import compute_kmedoids
from .wider_face import WIDERFace
from torch.utils import data
def get_dataloader(datapath, args, num_templates=25,
template_file="templates.json", img_transforms=None,
train... | 1,585 | 38.65 | 100 | py |
tiny-faces-pytorch | tiny-faces-pytorch-master/datasets/wider_face.py | from pathlib import Path
import numpy as np
import torch
from PIL import Image
from torch.utils.data import dataset
from torchvision import transforms
from utils import visualize
from .processor import DataProcessor
class WIDERFace(dataset.Dataset):
"""The WIDERFace dataset is generated using MATLAB,
so a ... | 8,229 | 34.938865 | 96 | py |
tiny-faces-pytorch | tiny-faces-pytorch-master/utils/nms.py | import numpy as np
def nms(dets, thresh):
"""
Courtesy of Ross Girshick
[https://github.com/rbgirshick/py-faster-rcnn/blob/master/lib/nms/py_cpu_nms.py]
"""
x1 = dets[:, 0]
y1 = dets[:, 1]
x2 = dets[:, 2]
y2 = dets[:, 3]
scores = dets[:, 4]
areas = (x2 - x1 + 1) * (y2 - y1 + 1... | 921 | 24.611111 | 84 | py |
tiny-faces-pytorch | tiny-faces-pytorch-master/utils/test_dense_overlap.py | from .dense_overlap import compute_dense_overlap
from scipy.io import loadmat
import numpy as np
d = loadmat("dense_overlap.mat")
ofx, ofy = d['ofx'][0, 0], d['ofy'][0, 0]
stx, sty = d['stx'][0, 0], d['sty'][0, 0]
vsx, vsy = d['vsx'][0, 0], d['vsy'][0, 0]
dx1, dy1, dx2, dy2 = d['dx1'], d['dy1'], d['dx2'], d['dy2']
d... | 1,076 | 29.771429 | 65 | py |
tiny-faces-pytorch | tiny-faces-pytorch-master/utils/dense_overlap.py | import numpy as np
def compute_dense_overlap(ofx, ofy, stx, sty, vsx, vsy,
dx1, dy1, dx2, dy2,
gx1, gy1, gx2, gy2, zmx=1, zmy=1):
"""
Compute the dense IoU
"""
num_templates = dx1.shape[0]
num_gt = gx1.shape[0]
ty, tx = (vsy - 1) * zmy + 1, ... | 1,997 | 31.754098 | 96 | py |
tiny-faces-pytorch | tiny-faces-pytorch-master/utils/cluster.py |
import argparse
from datetime import datetime
from pathlib import Path
import numpy as np
from PIL import Image, ImageDraw
from pyclust import KMedoids
from pyclustering.cluster.kmedoids import kmedoids
import joblib
from tqdm import tqdm
from .k_medoids import kMedoids
from .metrics import jaccard_index, rect_dist
... | 4,972 | 30.878205 | 117 | py |
tiny-faces-pytorch | tiny-faces-pytorch-master/utils/k_medoids.py | import numpy as np
import warnings
def kMedoids(distances, k):
"""
https://github.com/salspaugh/machine_learning/blob/master/clustering/kmedoids.py
:param distances:
:param k:
:return:
"""
n = distances.shape[0]
medoid_idxs = np.random.choice(n, size=k, replace=False)
old_medoids_... | 2,203 | 32.393939 | 93 | py |
tiny-faces-pytorch | tiny-faces-pytorch-master/utils/metrics.py | import json
import warnings
import numpy as np
from tqdm import tqdm
def jaccard_index(box_a, box_b, indices=[]):
"""
Compute the Jaccard Index (Intersection over Union) of 2 boxes. Each box is (x1, y1, x2, y2).
:param box_a:
:param box_b:
:param indices: The indices of box_a and box_b as [box_a_... | 5,812 | 27.777228 | 116 | py |
tiny-faces-pytorch | tiny-faces-pytorch-master/utils/__init__.py | """Utils module"""
from . import metrics
from . import nms
from . import dense_overlap
from . import k_medoids
| 112 | 15.142857 | 27 | py |
tiny-faces-pytorch | tiny-faces-pytorch-master/utils/test_metrics.py | import numpy as np
from scipy.io import loadmat
from .metrics import jaccard_index, rect_dist
def test_rect_dist(x, y, gt_dist):
d = rect_dist(x, y)
print("Is my rect_dist code correct?", np.array_equal(d, gt_dist))
def main():
truth = loadmat('rect_dist.mat')
gt_dist = truth['d'][:, 0]
x = trut... | 476 | 20.681818 | 70 | py |
tiny-faces-pytorch | tiny-faces-pytorch-master/utils/visualize.py | from PIL import Image, ImageDraw, ImageFont
import json
from pathlib import Path
import numpy as np
def draw_bounding_box(img, bbox, labels):
draw = ImageDraw.Draw(img)
font = ImageFont.load_default()
color = tuple(np.random.choice(range(100, 256), size=3))
draw.rectangle((bbox[0], bbox[1], bbox[2], ... | 1,696 | 27.762712 | 94 | py |
TSCP2 | TSCP2-main/src/main.py | import sys
import numpy as np
import argparse
import os
import tensorflow as tf
import matplotlib.pyplot as plt
# dataset
import TSCP2 as cp2
import losses as ls
from utils.DataHelper import load_dataset
from utils.estimate_CPD import estimate_CPs
parser = argparse.ArgumentParser(description='interface of running expe... | 6,984 | 43.775641 | 173 | py |
TSCP2 | TSCP2-main/src/losses.py | from tensorflow.keras import backend as K
import tensorflow as tf
import numpy as np
cosine_sim_1d = tf.keras.losses.CosineSimilarity(axis=1, reduction=tf.keras.losses.Reduction.NONE)
cosine_sim_2d = tf.keras.losses.CosineSimilarity(axis=2, reduction=tf.keras.losses.Reduction.NONE)
def _cosine_simililarity_dim1(x, y)... | 8,998 | 36.032922 | 161 | py |
TSCP2 | TSCP2-main/src/TSCP2.py | import os
from tensorflow.keras.layers import *
from tensorflow.keras.models import *
from tqdm import tqdm
import tensorflow as tf
from tcn import TCN
import numpy as np
import losses as ls
from utils.usc_ds_helper import ts_samples
#@tf.function
def train_step(xis, xjs, amodel, optimizer, criterion, temperature, s... | 4,665 | 41.036036 | 176 | py |
TSCP2 | TSCP2-main/src/helpers.py | import tensorflow as tf
import numpy as np
#from augmentation.gaussian_filter import GaussianBlur
def get_mask(batch_size):
# return a mask that removes the similarity score of equal/similar images.
# this function ensures that only distinct pair of images get their similarity scores
# passed as negative e... | 1,000 | 39.04 | 89 | py |
TSCP2 | TSCP2-main/src/utils/wsdm_ds_helper.py | import numpy as np
from pandas import read_csv
import pandas as pd
import csv
import os
def ts_samples(mbatch, win):
x = mbatch[:,1:win+1]
y = mbatch[:,-win:]
lbl = mbatch[:,0]
return x, y, lbl
def load_wsdm_ds(path, window, mode='train', part=1):
save_path = os.path.join("../data/slidingwindow"... | 3,412 | 30.601852 | 120 | py |
TSCP2 | TSCP2-main/src/utils/DataHelper.py | import numpy as np
import tensorflow as tf
from .hasc_helper import load_hasc_ds
from .usc_ds_helper import load_usc_ds
def load_dataset(path, ds_name, win, bs, mode="train"):
if ds_name == 'HASC':
trainx, trainlbl = load_hasc_ds(path, window = 2 * win, mode=mode)
elif ds_name == "USC":
trainx,... | 883 | 34.36 | 96 | py |
TSCP2 | TSCP2-main/src/utils/FastDTW.py | #!/usr/bin/env python
# coding: utf-8
# In[10]:
from __future__ import absolute_import, division
import numbers
import numpy as np
from collections import defaultdict
def __reduce_by_half(x):
return [(x[i] + x[1+i]) / 2 for i in range(0, len(x) - len(x) % 2, 2)]
# In[11]:
def __expand_window(path, len_x, le... | 5,152 | 25.425641 | 80 | py |
TSCP2 | TSCP2-main/src/utils/yahoo_ds_helper.py | from math import floor
import numpy as np
from pandas import read_csv
import pandas as pd
import csv
import os
def ts_samples(mbatch, win):
x = mbatch[:,1:win+1]
y = mbatch[:,-win:]
lbl = mbatch[:,0]
return x, y, lbl
def load_yahoo_ds(path, window, mode='train'):
save_path = os.path.join("../d... | 2,906 | 28.363636 | 120 | py |
TSCP2 | TSCP2-main/src/utils/analysis_helper.py | import csv
import os
import numpy as np
from numpy import mean
from numpy import std
import matplotlib.pyplot as plt
from scipy import signal
from sklearn.metrics import mean_squared_error
from utils.FastDTW import fastdtw
def getMSE(Yt, y_pred, sq = False):
y_hat = y_pred.reshape((Yt.shape[0], Yt.shape[1], Yt.... | 3,827 | 35.457143 | 171 | py |
TSCP2 | TSCP2-main/src/utils/usc_ds_helper.py | from math import floor
import numpy as np
import pandas as pd
import scipy.io as sio
import csv
def ts_samples(mbatch, win):
x = mbatch[:,1:win+1]
y = mbatch[:,-win:]
lbl = mbatch[:,0]
return x, y, lbl
def load_usc_ds(path, window, mode='train'):
X, lbl = extract_windows(path, window, mode)
... | 1,866 | 26.057971 | 113 | py |
TSCP2 | TSCP2-main/src/utils/logger.py | import logging
import logging.config
import yaml
import os
def get_logger(mod_name, log_dir):
if not os.path.exists(log_dir):
os.mkdir(log_dir)
config_filepath = os.path.join(os.path.realpath(os.path.dirname(__file__)), 'logger_config.yml')
if os.path.exists(config_filepath):
with open(co... | 721 | 31.818182 | 100 | py |
TSCP2 | TSCP2-main/src/utils/hasc_helper.py | from math import floor
import numpy as np
from pandas import read_csv
import pandas as pd
import csv
import os
def ts_samples(mbatch, win):
x = mbatch[:,1:win+1]
y = mbatch[:,-win:]
lbl = mbatch[:,0]
return x, y, lbl
def load_hasc_ds(path, window, mode='train'):
X, lbl = extract_windows(path,... | 1,878 | 24.739726 | 111 | py |
TSCP2 | TSCP2-main/src/utils/estimate_CPD.py | import numpy as np
from sklearn.metrics import confusion_matrix,f1_score
from matplotlib import pyplot as plt
from losses import _cosine_simililarity_dim1
def estimate_CPs(sim, gt, name, train_name, metric='cosine', threshold=0.5):
#if metric == "cosine":
# sim = _cosine_simililarity_dim1(h, f)
est_cp... | 1,741 | 30.672727 | 178 | py |
TSCP2 | TSCP2-main/src/utils/__init__.py | 0 | 0 | 0 | py | |
DG-Font | DG-Font-main/main.py | import argparse
import warnings
from datetime import datetime
from glob import glob
from shutil import copyfile
from collections import OrderedDict
import torch.nn
import torch.nn.parallel
import torch.backends.cudnn as cudnn
import torch.distributed as dist
import torch.optim
import torch.multiprocessing as mp
import... | 17,653 | 43.024938 | 1,912 | py |
DG-Font | DG-Font-main/font2img.py | from PIL import Image,ImageDraw,ImageFont
import matplotlib.pyplot as plt
import os
import numpy as np
import pathlib
import argparse
parser = argparse.ArgumentParser(description='Obtaining characters from .ttf')
parser.add_argument('--ttf_path', type=str, default='../ttf_folder',help='ttf directory')
parser.add_argu... | 2,167 | 37.714286 | 128 | py |
DG-Font | DG-Font-main/functions/modulated_deform_conv_func.py | #!/usr/bin/env python
from __future__ import absolute_import
from __future__ import print_function
from __future__ import division
import math
import torch
from torch import nn
from torch.autograd import Function
from torch.nn.modules.utils import _pair
from torch.autograd.function import once_differentiable
import D... | 2,484 | 42.596491 | 83 | py |
DG-Font | DG-Font-main/functions/__init__.py | from .modulated_deform_conv_func import ModulatedDeformConvFunction
| 68 | 33.5 | 67 | py |
DG-Font | DG-Font-main/tools/utils.py | import os
import torch
class Logger(object):
def __init__(self, log_dir):
self.last = None
def scalar_summary(self, tag, value, step):
if self.last and self.last['step'] != step:
print(self.last)
self.last = None
if self.last is None:
self.last = {'... | 1,968 | 25.608108 | 88 | py |
DG-Font | DG-Font-main/tools/ops.py | from torch import autograd
import torch
import torch.distributed as dist
from torch.nn import functional as F
def compute_grad_gp(d_out, x_in, is_patch=False):
batch_size = x_in.size(0)
grad_dout = autograd.grad(
outputs=d_out.sum() if not is_patch else d_out.mean(), inputs=x_in,
create_graph=... | 4,639 | 28.367089 | 97 | py |
DG-Font | DG-Font-main/modules/modulated_deform_conv.py | #!/usr/bin/env python
from __future__ import absolute_import
from __future__ import print_function
from __future__ import division
import torch
import math
from torch import nn
from torch.nn import init
from torch.nn.modules.utils import _pair
from functions.modulated_deform_conv_func import ModulatedDeformConvFuncti... | 5,186 | 44.902655 | 119 | py |
DG-Font | DG-Font-main/modules/__init__.py | from .modulated_deform_conv import ModulatedDeformConv, _ModulatedDeformConv, ModulatedDeformConvPack | 101 | 101 | 101 | py |
DG-Font | DG-Font-main/models/guidingNet.py | from torch import nn
import torch.nn.functional as F
try:
from models.blocks import Conv2dBlock, FRN
except:
from blocks import Conv2dBlock, FRN
cfg = {
'vgg11': [64, 'M', 128, 'M', 256, 256, 'M', 512, 512, 'M', 512, 512, 'M'],
'vgg13': [64, 64, 'M', 128, 128, 'M', 256, 256, 'M', 512, 512, 'M', 512, ... | 2,977 | 31.725275 | 120 | py |
DG-Font | DG-Font-main/models/discriminator.py | import torch
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
import torch.nn.init as init
import math
try:
from models.blocks import FRN, ActFirstResBlk
except:
from blocks import FRN, ActFirstResBlk
class Discriminator(nn.Module):
"""Discriminator: (image x, domain y) -> (logit... | 3,132 | 34.202247 | 87 | py |
DG-Font | DG-Font-main/models/inception.py | import torch
import torch.nn as nn
import torch.nn.functional as F
from torchvision import models
try:
from torchvision.models.utils import load_state_dict_from_url
except ImportError:
from torch.utils.model_zoo import load_url as load_state_dict_from_url
# Inception weights ported to Pytorch from
# http://do... | 11,623 | 36.376206 | 126 | py |
DG-Font | DG-Font-main/models/generator.py | from torch import nn
import torch
import torch.nn.functional as F
import torch.nn.init as init
import scipy.io as io
import math
import numpy as np
try:
from models.blocks import LinearBlock, Conv2dBlock, ResBlocks
except:
from blocks import LinearBlock, Conv2dBlock, ResBlocks
import sys
sys.path.append('..')... | 7,688 | 38.229592 | 168 | py |
DG-Font | DG-Font-main/models/blocks.py | import torch
import torch.nn.functional as F
from torch import nn
class ResBlocks(nn.Module):
def __init__(self, num_blocks, dim, norm, act, pad_type, use_sn=False):
super(ResBlocks, self).__init__()
self.model = nn.ModuleList()
for i in range(num_blocks):
self.model.append(Res... | 7,518 | 33.64977 | 118 | py |
DG-Font | DG-Font-main/datasets/custom_dataset.py | import torch.utils.data as data
from PIL import Image
import os
import os.path
import sys
def has_file_allowed_extension(filename, extensions):
"""Checks if a file is an allowed extension.
Args:
filename (string): path to a file
extensions (iterable of strings): extensions to consider (lowe... | 9,235 | 33.207407 | 131 | py |
DG-Font | DG-Font-main/datasets/datasetgetter.py | import torch
from torchvision.datasets import ImageFolder
import os
import torchvision.transforms as transforms
from datasets.custom_dataset import ImageFolerRemap, CrossdomainFolder
class Compose(object):
def __init__(self, tf):
self.tf = tf
def __call__(self, img):
for t in self.tf:
... | 2,435 | 27.325581 | 91 | py |
DG-Font | DG-Font-main/train/train.py | from tqdm import trange
import torch.nn
import torch.nn.parallel
import torch.optim
import torch.utils.data
import torch.utils.data.distributed
from tools.utils import *
from tools.ops import compute_grad_gp, update_average, copy_norm_params, queue_data, dequeue_data, \
average_gradients, calc_adv_loss, calc_contra... | 5,671 | 30.511111 | 158 | py |
DG-Font | DG-Font-main/validation/validation.py | import torch.nn
import torch.nn.parallel
import torch.optim
import torch.utils.data
import torch.utils.data.distributed
import torchvision.utils as vutils
import torch.nn.functional as F
import numpy as np
try:
from tqdm import tqdm
except ImportError:
# If not tqdm is not available, provide a mock version of... | 4,643 | 46.387755 | 161 | py |
FPConv | FPConv-master/tools/test_scannet.py | import torch
from torch.utils.data import DataLoader
import numpy as np
import argparse
import importlib
import os
import sys
import json
from utils.switchnorm import convert_sn
from datasets.scannet_dataset_rgb_test import ScannetDatasetWholeScene_evaluation
np.seterr(divide='ignore', invalid='ignore')
parser = arg... | 7,024 | 36.367021 | 139 | py |
FPConv | FPConv-master/tools/test_s3dis.py | import os, sys
import json
import numpy as np
import argparse
import importlib
import torch
from torch.utils.data import DataLoader
from datasets.s3dis_dataset_test import S3DISWholeScene_evaluation
np.seterr(divide='ignore', invalid='ignore')
parser = argparse.ArgumentParser(description="Arg parser")
parser.add_arg... | 5,981 | 35.036145 | 87 | py |
FPConv | FPConv-master/tools/train_scannet.py | import torch
import torch.nn as nn
from torch.utils.data import DataLoader
import torch.distributed as dist
import os, sys
import argparse
import importlib
import numpy as np
import json
import tensorboard_logger as tb_log
from datasets.scannet_dataset_rgb import ScannetDataset, ScannetDatasetWholeScene
from utils.sa... | 11,795 | 37.423453 | 137 | py |
FPConv | FPConv-master/tools/train_s3dis.py | import os, sys
import argparse
import importlib
import numpy as np
import json
import time
import tensorboard_logger as tb_log
import torch
import torch.nn as nn
from torch.utils.data import DataLoader
from datasets.s3dis_dataset import S3DIS
from utils.saver import Saver
np.seterr(divide='ignore', invalid='ignore')... | 12,945 | 35.162011 | 125 | py |
FPConv | FPConv-master/tools/vis_scannet.py | import numpy as np
import open3d as o3d
import os
import argparse
import sys
parser = argparse.ArgumentParser(description='Visualize point cloud')
parser.add_argument('--file_dir', type=str, help=None)
parser.add_argument('--scene_id', type=str, help=None)
args = parser.parse_args()
print(args)
color_map = [[ 0, 0... | 2,015 | 31.516129 | 84 | py |
FPConv | FPConv-master/models/fpcnn_scannet.py | import torch
import torch.nn as nn
from fpconv.pointnet2.pointnet2_modules import PointnetFPModule
import fpconv.pointnet2.pytorch_utils as pt_utils
from fpconv.base import AssemRes_BaseBlock
from fpconv.fpconv import FPConv4x4_BaseBlock, FPConv6x6_BaseBlock
NPOINT = 8192
NPOINTS = [NPOINT // 2, NPOINT // 8, NPOINT ... | 4,571 | 37.1 | 94 | py |
FPConv | FPConv-master/models/fpcnn_s3dis.py | import torch
import torch.nn as nn
from fpconv.pointnet2.pointnet2_modules import PointnetFPModule, PointnetSAModule
import fpconv.pointnet2.pytorch_utils as pt_utils
from fpconv.base import AssemRes_BaseBlock
from fpconv.fpconv import FPConv4x4_BaseBlock, FPConv6x6_BaseBlock
NPOINTS = [8192, 2048, 512, 128]
RADIUS =... | 4,378 | 37.412281 | 94 | py |
FPConv | FPConv-master/fpconv/base.py | import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.nn.parameter import Parameter
from fpconv.pointnet2 import pointnet2_utils
from fpconv.pointnet2 import pytorch_utils as pt_utils
relu_alpha = 0.2
class PointNet(nn.Module):
def __init__(self, mlp, pool='max', bn=True):
super()... | 8,204 | 40.649746 | 125 | py |
FPConv | FPConv-master/fpconv/fpconv.py | import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.nn.parameter import Parameter
from fpconv.pointnet2 import pointnet2_utils
from fpconv.pointnet2 import pytorch_utils as pt_utils
from fpconv import base
relu_alpha = 0.2
class FPConv4x4_BaseBlock(nn.Module):
def __init__(self, npoint,... | 8,087 | 41.793651 | 133 | py |
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