id int64 0 190k | prompt stringlengths 21 13.4M | docstring stringlengths 1 12k ⌀ |
|---|---|---|
6,428 | from importlib import import_module
from dzoedepth.models.depth_model import DepthModel
class DepthModel(nn.Module):
def __init__(self):
super().__init__()
self.device = 'cpu'
def to(self, device) -> nn.Module:
self.device = device
return super().to(device)
def for... | Builds a model from a config. The model is specified by the model name and version in the config. The model is then constructed using the build_from_config function of the model interface. This function should be used to construct models for training and evaluation. Args: config (dict): Config dict. Config is construct... |
6,429 | import json
import os
from dzoedepth.utils.easydict import EasyDict as edict
from dzoedepth.utils.arg_utils import infer_type
import pathlib
import platform
COMMON_CONFIG = {
"save_dir": os.path.expanduser("~/shortcuts/monodepth3_checkpoints"),
"project": "ZoeDepth",
"tags": '',
"notes": "",
"gpu": ... | Main entry point to get the config for the model. Args: model_name (str): name of the desired model. mode (str, optional): "train" or "infer". Defaults to 'train'. dataset (str, optional): If specified, the corresponding dataset configuration is loaded as well. Defaults to None. Keyword Args: key-value pairs of argumen... |
6,430 | import json
import os
from dzoedepth.utils.easydict import EasyDict as edict
from dzoedepth.utils.arg_utils import infer_type
import pathlib
import platform
DATASETS_CONFIG = {
"kitti": {
"dataset": "kitti",
"min_depth": 0.001,
"max_depth": 80,
"data_path": os.path.join(HOME_DIR, "sh... | null |
6,431 | from scipy import ndimage
import base64
import math
import re
from io import BytesIO
import matplotlib
import matplotlib.cm
import numpy as np
import requests
import torch
import torch.distributed as dist
import torch.nn
import torch.nn as nn
import torch.utils.data.distributed
from PIL import Image
from torchvision.tr... | Reverses the imagenet normalization applied to the input. Args: x (torch.Tensor - shape(N,3,H,W)): input tensor Returns: torch.Tensor - shape(N,3,H,W): Denormalized input |
6,432 | from scipy import ndimage
import base64
import math
import re
from io import BytesIO
import matplotlib
import matplotlib.cm
import numpy as np
import requests
import torch
import torch.distributed as dist
import torch.nn
import torch.nn as nn
import torch.utils.data.distributed
from PIL import Image
from torchvision.tr... | null |
6,433 | from scipy import ndimage
import base64
import math
import re
from io import BytesIO
import matplotlib
import matplotlib.cm
import numpy as np
import requests
import torch
import torch.distributed as dist
import torch.nn
import torch.nn as nn
import torch.utils.data.distributed
from PIL import Image
from torchvision.tr... | Compute metrics of predicted depth maps. Applies cropping and masking as necessary or specified via arguments. Refer to compute_errors for more details on metrics. |
6,434 | from scipy import ndimage
import base64
import math
import re
from io import BytesIO
import matplotlib
import matplotlib.cm
import numpy as np
import requests
import torch
import torch.distributed as dist
import torch.nn
import torch.nn as nn
import torch.utils.data.distributed
from PIL import Image
from torchvision.tr... | null |
6,435 | from scipy import ndimage
import base64
import math
import re
from io import BytesIO
import matplotlib
import matplotlib.cm
import numpy as np
import requests
import torch
import torch.distributed as dist
import torch.nn
import torch.nn as nn
import torch.utils.data.distributed
from PIL import Image
from torchvision.tr... | null |
6,436 | from scipy import ndimage
import base64
import math
import re
from io import BytesIO
import matplotlib
import matplotlib.cm
import numpy as np
import requests
import torch
import torch.distributed as dist
import torch.nn
import torch.nn as nn
import torch.utils.data.distributed
from PIL import Image
from torchvision.tr... | null |
6,437 | from scipy import ndimage
import base64
import math
import re
from io import BytesIO
import matplotlib
import matplotlib.cm
import numpy as np
import requests
import torch
import torch.distributed as dist
import torch.nn
import torch.nn as nn
import torch.utils.data.distributed
from PIL import Image
from torchvision.tr... | null |
6,438 | from scipy import ndimage
import base64
import math
import re
from io import BytesIO
import matplotlib
import matplotlib.cm
import numpy as np
import requests
import torch
import torch.distributed as dist
import torch.nn
import torch.nn as nn
import torch.utils.data.distributed
from PIL import Image
from torchvision.tr... | null |
6,439 | def infer_type(x): # hacky way to infer type from string args
if not isinstance(x, str):
return x
try:
x = int(x)
return x
except ValueError:
pass
try:
x = float(x)
return x
except ValueError:
pass
return x
def parse_unknown(unknown_args)... | null |
6,440 | import math
from PIL import Image
import numpy
def zoom_in_effect(clip, zoom_ratio=0.04):
def effect(get_frame, t):
img = Image.fromarray(get_frame(t))
base_size = img.size
new_size = [
math.ceil(img.size[0] * (1 + (zoom_ratio * t))),
math.ceil(img.size[1] * (1 + (z... | null |
6,441 | from generator.image.build import build_image_generator
from generator.video.build import build_video_generator
from generator.tts.build import build_tts_generator
from generator.text.build import build_text_generator
from generator.music.build import build_bgm_generator
from editor.chat_editor import Text2VideoEditor
... | null |
6,442 | import logging
logger = build_logger()
def build_logger():
logging.basicConfig(format='%(asctime)s - %(name)s - %(levelname)s - %(message)s', level=logging.INFO)
logger = logging.getLogger(__name__)
return logger | null |
6,443 | from bs4 import BeautifulSoup
import requests
import json
def get_paragraph_texts(url: str):
html: str = requests.get(url).text
soup = BeautifulSoup(html, "html.parser")
pes = soup.findAll('p')
texts: list[str] = []
for e in pes:
texts.append(e.get_text())
return texts | null |
6,444 | import openai
import os
import re
from comm.mylog import logger
from comm.url_parser import get_paragraph_texts
def is_all_chinese(strs):
for _char in strs:
if not '\u4e00' <= _char <= '\u9fa5':
return False
return True | null |
6,445 | import openai
import os
import re
from comm.mylog import logger
from comm.url_parser import get_paragraph_texts
def is_contains_chinese(strs):
for _char in strs:
if '\u4e00' <= _char <= '\u9fa5':
return True
return False | null |
6,446 | import urllib
import urllib.request
import io
import traceback
import os
from PIL import Image
from typing import List
from comm.mylog import logger
def download_image(url):
urllib_request = urllib.request.Request(
url,
data=None,
headers={"User-Agent": "Mozilla/5.0 (X11; Ubuntu; Linux x86_... | null |
6,447 | import clip
from multilingual_clip import pt_multilingual_clip
import transformers
import torch
import numpy as np
def build_clip_model(model_name = "Vit-L/14", device="cpu"):
model, preprocess = clip.load(model_name, device = device)
return model,preprocess, lambda t: clip.tokenize(t, truncate=True) | null |
6,448 | import clip
from multilingual_clip import pt_multilingual_clip
import transformers
import torch
import numpy as np
def build_mclip_model(model_name = "M-CLIP/XLM-Roberta-Large-Vit-L-14", device="cpu"):
model = MClip(model_name,device)
return model,None,model.get_tokenizer | null |
6,449 | import clip
from multilingual_clip import pt_multilingual_clip
import transformers
import torch
import numpy as np
def test_mclip():
model = MClip("M-CLIP/XLM-Roberta-Large-Vit-L-14","cpu")
text = ["hello world","你好"]
embed = model.get_text_embed(text)
print(embed.shape) | null |
6,450 | from generator.comm.media_generator import MediaGeneratorBase
import urllib
import urllib.request
import io
import traceback
import os
from PIL import Image
from typing import List
from comm.mylog import logger
def download_video(url):
urllib_request = urllib.request.Request(
url,
data=None,
... | null |
6,454 | from yacs.config import CfgNode as CN
_C = CN()
_C.video_editor = CN()
_C.video_editor.type = "Text2Video"
_C.video_editor.visual_gen.type = "image_by_retrieval" o_editor.visual_gen.image_by_retrieval = CN()
_C.video_editor.visual_gen.image_by_retrieval.model = "ViT-L/14"
_C.video_editor.visual_gen.image_by_retrieval.... | Get a yacs CfgNode object with default values for my_project. |
6,455 | import gradio as gr
import argparse
import sys
import os
from editor.build import build_editor
from configs.config import get_cfg_defaults
from comm.mylog import logger
def get_args():
parser = argparse.ArgumentParser(description='config for open chat editor')
parser.add_argument('--cfg', type=str, required=... | null |
6,456 | import gradio as gr
import argparse
import sys
import os
from editor.build import build_editor
from configs.config import get_cfg_defaults
from comm.mylog import logger
def run_Text2VideoEditor_ui():
gr.Interface(
run_Text2VideoEditor_logit,
[gr.inputs.Textbox(placeholder="... | null |
6,457 | import gradio as gr
import argparse
import sys
import os
from editor.build import build_editor
from configs.config import get_cfg_defaults
from comm.mylog import logger
def run_URL2VideoEditor_ui():
gr.Interface(
run_URL2VideoEditor_logit,
[gr.inputs.Textbox(placeholder="En... | null |
6,458 | from pathlib import Path
from fastdup.sentry import v1_sentry_handler
from fastdup.engine import Fastdup
from typing import Union
import fastdup.fastdup_controller as FD
class Fastdup(FastdupController):
"""
This class provides all fastdup capabilities as a single class.
Usage example
=============
... | Create fastdup analyzer instance. Usage example ============= ``` import pandas as pd import fastsup annotation_csv = '/path/to/annotation.csv' data_dir = '/path/to/images/' output_dir = '/path/to/fastdup_analysis' import fastdup fd = fastdup.create(work_dir='work_dir', input_dir='images') fd.run(annotations=pd.read_cs... |
6,459 | from fastdup import definitions
import os
from tqdm import tqdm
import os
os.environ["QT_QPA_PLATFORM"] ="offscreen"
os.environ['FASTDUP_LOCAL_DIR'] = LOCAL_DIR
if not os.path.exists(so_file):
print("Failed to find shared object", so_file);
print("Current init file is on", __file__);
sys.exit(... | Function to merge multiple image lists obtained from webdataset format by running fastdup with run_mode=1 into a single list. The following files will be created under work_dir: * atrain_features.dat.csv - list of all filenames * atrain_stats.csv - list of all image statistics (optional) * atrain_labels.csv - list of a... |
6,460 | from fastdup import definitions
import os
from tqdm import tqdm
import os
os.environ["QT_QPA_PLATFORM"] ="offscreen"
os.environ['FASTDUP_LOCAL_DIR'] = LOCAL_DIR
if not os.path.exists(so_file):
print("Failed to find shared object", so_file);
print("Current init file is on", __file__);
sys.exit(... | One fastdup runs and creates similarity.csv file, select a subset of similaritis > threshold and put them in the out_file Arguments: work_dir (str): fastdup working dir where the file similarity/csv is found, or a full path pointing to similarity.csv file out_file (str): name of output similarity file threshold: 0->1 t... |
6,461 | import os
import csv
import numpy as np
import pandas as pd
from PIL import Image
import cv2
from fastdup.image import my_resize
def register_embedding(embedding_tensor_name, meta_data_fname, log_dir, sprite_path, with_images=True):
from tensorboard.plugins import projector
config = projector.ProjectorConfig()
... | null |
6,462 | import glob
import random
import platform
from fastdup.definitions import *
from datetime import datetime
from fastdup.sentry import fastdup_capture_exception
import warnings
import itertools
import pathlib
import subprocess
import time
import os
import requests
import tqdm.auto as tqdm
import tarfile
from multiprocess... | null |
6,463 | import glob
import random
import platform
from fastdup.definitions import *
from datetime import datetime
from fastdup.sentry import fastdup_capture_exception
import warnings
import itertools
import pathlib
import subprocess
import time
import os
import requests
import tqdm.auto as tqdm
import tarfile
from multiprocess... | Download files from S3 to local disk (called only in case of turi_param='sync_s3_to_local=1') Note: we assume there is enough local disk space otherwise the download may fail input_dir: input directory on s3 or minio work_dir: local working directory verbose: if verbose show progress is_test: If this is a test folder s... |
6,464 | import glob
import random
import platform
from fastdup.definitions import *
from datetime import datetime
from fastdup.sentry import fastdup_capture_exception
import warnings
import itertools
import pathlib
import subprocess
import time
import os
import requests
import tqdm.auto as tqdm
import tarfile
from multiprocess... | null |
6,465 | import glob
import random
import platform
from fastdup.definitions import *
from datetime import datetime
from fastdup.sentry import fastdup_capture_exception
import warnings
import itertools
import pathlib
import subprocess
import time
import os
import requests
import tqdm.auto as tqdm
import tarfile
from multiprocess... | null |
6,466 | import glob
import random
import platform
from fastdup.definitions import *
from datetime import datetime
from fastdup.sentry import fastdup_capture_exception
import warnings
import itertools
import pathlib
import subprocess
import time
import os
import requests
import tqdm.auto as tqdm
import tarfile
from multiprocess... | null |
6,467 | import glob
import random
import platform
from fastdup.definitions import *
from datetime import datetime
from fastdup.sentry import fastdup_capture_exception
import warnings
import itertools
import pathlib
import subprocess
import time
import os
import requests
import tqdm.auto as tqdm
import tarfile
from multiprocess... | null |
6,468 | import glob
import random
import platform
from fastdup.definitions import *
from datetime import datetime
from fastdup.sentry import fastdup_capture_exception
import warnings
import itertools
import pathlib
import subprocess
import time
import os
import requests
import tqdm.auto as tqdm
import tarfile
from multiprocess... | null |
6,469 | import glob
import random
import platform
from fastdup.definitions import *
from datetime import datetime
from fastdup.sentry import fastdup_capture_exception
import warnings
import itertools
import pathlib
import subprocess
import time
import os
import requests
import tqdm.auto as tqdm
import tarfile
from multiprocess... | null |
6,470 | import glob
import random
import platform
from fastdup.definitions import *
from datetime import datetime
from fastdup.sentry import fastdup_capture_exception
import warnings
import itertools
import pathlib
import subprocess
import time
import os
import requests
import tqdm.auto as tqdm
import tarfile
from multiprocess... | null |
6,471 | import glob
import random
import platform
from fastdup.definitions import *
from datetime import datetime
from fastdup.sentry import fastdup_capture_exception
import warnings
import itertools
import pathlib
import subprocess
import time
import os
import requests
import tqdm.auto as tqdm
import tarfile
from multiprocess... | null |
6,472 | import glob
import random
import platform
from fastdup.definitions import *
from datetime import datetime
from fastdup.sentry import fastdup_capture_exception
import warnings
import itertools
import pathlib
import subprocess
import time
import os
import requests
import tqdm.auto as tqdm
import tarfile
from multiprocess... | null |
6,473 | import glob
import random
import platform
from fastdup.definitions import *
from datetime import datetime
from fastdup.sentry import fastdup_capture_exception
import warnings
import itertools
import pathlib
import subprocess
import time
import os
import requests
import tqdm.auto as tqdm
import tarfile
from multiprocess... | null |
6,474 | import glob
import random
import platform
from fastdup.definitions import *
from datetime import datetime
from fastdup.sentry import fastdup_capture_exception
import warnings
import itertools
import pathlib
import subprocess
import time
import os
import requests
import tqdm.auto as tqdm
import tarfile
from multiprocess... | Convert dictionary in COCO format object annotations to a Fastdup DF :param coco_dict: :return: a Dataframe in the expected format for fastdup bboxes. |
6,475 | import glob
import random
import platform
from fastdup.definitions import *
from datetime import datetime
from fastdup.sentry import fastdup_capture_exception
import warnings
import itertools
import pathlib
import subprocess
import time
import os
import requests
import tqdm.auto as tqdm
import tarfile
from multiprocess... | null |
6,476 | import glob
import random
import platform
from fastdup.definitions import *
from datetime import datetime
from fastdup.sentry import fastdup_capture_exception
import warnings
import itertools
import pathlib
import subprocess
import time
import os
import requests
import tqdm.auto as tqdm
import tarfile
from multiprocess... | null |
6,477 | import glob
import random
import platform
from fastdup.definitions import *
from datetime import datetime
from fastdup.sentry import fastdup_capture_exception
import warnings
import itertools
import pathlib
import subprocess
import time
import os
import requests
import tqdm.auto as tqdm
import tarfile
from multiprocess... | Package a recusive folder of images as webdataset Args: input_dir: the input directory with images work_dir: temp working dir output_dir: the output dir for the packaged tars _images_per_tar: how many images per tar (default 10000) image_suffix: list of images suffix to package for example ['.jpg', '.png'] num_threads:... |
6,478 | from enum import Enum
import os
import sys
import tempfile
import os
os.environ["QT_QPA_PLATFORM"] ="offscreen"
os.environ['FASTDUP_LOCAL_DIR'] = LOCAL_DIR
if not os.path.exists(so_file):
print("Failed to find shared object", so_file);
print("Current init file is on", __file__);
sys.exit(1)
... | null |
6,479 | import os
from fastdup.definitions import S3_TEMP_FOLDER, S3_TEST_TEMP_FOLDER
from datetime import datetime
from fastdup.sentry import fastdup_capture_exception
import os
os.environ["QT_QPA_PLATFORM"] ="offscreen"
os.environ['FASTDUP_LOCAL_DIR'] = LOCAL_DIR
if not os.path.exists(so_file):
print("Faile... | Download files from S3 to local disk (called only in case of turi_param='sync_s3_to_local=1') Note: we assume there is enough local disk space otherwise the download may fail input_dir: input directory on s3 or minio work_dir: local working directory verbose: if verbose show progress is_test: If this is a test folder s... |
6,480 | import os
from fastdup.definitions import S3_TEMP_FOLDER, S3_TEST_TEMP_FOLDER
from datetime import datetime
from fastdup.sentry import fastdup_capture_exception
def fastdup_capture_exception(section, e, warn_only=False, extra=""):
if not warn_only:
traceback.print_exc()
if 'SENTRY_OPT_OUT' not in os.en... | null |
6,481 | import os
from fastdup.definitions import S3_TEMP_FOLDER, S3_TEST_TEMP_FOLDER
from datetime import datetime
from fastdup.sentry import fastdup_capture_exception
def fastdup_capture_exception(section, e, warn_only=False, extra=""):
if not warn_only:
traceback.print_exc()
if 'SENTRY_OPT_OUT' not in os.en... | null |
6,482 | import json
import os
import tempfile
import warnings
from typing import List, Union, Tuple
import numpy as np
import pandas as pd
from pathlib import Path
import fastdup
from pandas.errors import EmptyDataError
import shutil
import fastdup.definitions as FD
from fastdup.sentry import v1_sentry_handler, fastdup_capture... | null |
6,483 | import json
import os
import tempfile
import warnings
from typing import List, Union, Tuple
import numpy as np
import pandas as pd
from pathlib import Path
import fastdup
from pandas.errors import EmptyDataError
import shutil
import fastdup.definitions as FD
from fastdup.sentry import v1_sentry_handler, fastdup_capture... | null |
6,484 | import json
import os
import tempfile
import warnings
from typing import List, Union, Tuple
import numpy as np
import pandas as pd
from pathlib import Path
import fastdup
from pandas.errors import EmptyDataError
import shutil
import fastdup.definitions as FD
from fastdup.sentry import v1_sentry_handler, fastdup_capture... | override default arguments in fastdup args with users-input :param input_kwargs:iunput kwargs to init function :return: updated dict |
6,485 | import json
import os
import tempfile
import warnings
from typing import List, Union, Tuple
import numpy as np
import pandas as pd
from pathlib import Path
import fastdup
from pandas.errors import EmptyDataError
import shutil
import fastdup.definitions as FD
from fastdup.sentry import v1_sentry_handler, fastdup_capture... | null |
6,486 | import os
import shutil
import numpy as np
import pandas as pd
from PIL import Image
import tempfile
from pathlib import Path
import cv2
from fastdup.fastdup_controller import FastdupController
from webcolors import rgb_to_name
import fastdup.definitions as FD
import matplotlib.pyplot as plt
def gen_data(output_dir, n_... | null |
6,487 | from fastdup.sentry import fastdup_capture_exception
import os
cat = {0: u'__background__',
1: u'person',
2: u'bicycle',
3: u'car',
4: u'motorcycle',
5: u'airplane',
6: u'bus',
7: u'train',
8: u'truck',
9: u'boat',
10: u'traffic light',
11: u'fire hydrant',
12: u'stop sign',
13: u'parking meter',
14: u'be... | null |
6,488 | import warnings
import numpy as np
The provided code snippet includes necessary dependencies for implementing the `column_or_1d` function. Write a Python function `def column_or_1d(y, *, warn=False)` to solve the following problem:
Ravel column or 1d numpy array, else raises an error. Parameters ---------- y : array-l... | Ravel column or 1d numpy array, else raises an error. Parameters ---------- y : array-like Input data. warn : bool, default=False To control display of warnings. Returns ------- y : ndarray Output data. Raises ------ ValueError If `y` is not a 1D array or a 2D array with a single row or column. |
6,489 | import warnings
import numpy as np
def unique_labels(truth, pred):
total = truth.copy()
total.extend(pred)
return np.array(sorted(np.unique(total)))
def precision_recall_fscore_support(
y_true,
y_pred,
*,
beta=1.0,
labels=None,
pos_label=1,
average=Non... | Build a text report showing the main classification metrics. Read more in the :ref:`User Guide <classification_report>`. Parameters ---------- y_true : 1d array-like, or label indicator array / sparse matrix Ground truth (correct) target values. y_pred : 1d array-like, or label indicator array / sparse matrix Estimated... |
6,490 | import os
import random
import json
import glob
import cv2
import shutil
import zipfile
from fastdup.image import get_shape
def create_annotations_file(files, labels, save_path):
def create_tasks_file(files, labels, save_path):
def create_cvat_index(index, save_path):
def create_cvat_manifest(files, save_path):
def ini... | null |
6,491 | import os
import shutil
import numpy as np
import pandas as pd
from PIL import Image
import tempfile
from pathlib import Path
from fastdup.fastdup_controller import FastdupController
from webcolors import rgb_to_name
import fastdup.definitions as FD
def gen_data(output_dir, n_valid, n_corrupted, n_duplicated, n_no_anno... | null |
6,492 | import os
import cv2
import numpy as np
from fastdup.image import get_shape
from fastdup.sentry import fastdup_capture_exception
def image_to_label_img_xml(img_path, cur_label, save_dir=None):
assert os.path.exists(img_path), '{} does not exist'.format(img_path)
if save_dir:
assert os.path.exists(save_d... | null |
6,493 | import random
ascii_arts = [
"""
:++. ...
;&&$+. ;x: ;;; ... | null |
6,494 | import sentry_sdk
from sentry_sdk import capture_exception
import time
import os
import sys
import traceback
import platform
import uuid
import hashlib
from fastdup.definitions import VERSION__
from functools import wraps
def traces_sampler(sampling_context):
# Examine provided context data (including parent decis... | null |
6,495 | import sentry_sdk
from sentry_sdk import capture_exception
import time
import os
import sys
import traceback
import platform
import uuid
import hashlib
from fastdup.definitions import VERSION__
from functools import wraps
token = hashlib.sha256(str(uuid.getnode()).encode()).hexdigest()
unit_test = None
def find_certifi... | null |
6,496 | import sentry_sdk
from sentry_sdk import capture_exception
import time
import os
import sys
import traceback
import platform
import uuid
import hashlib
from fastdup.definitions import VERSION__
from functools import wraps
import os
os.environ["QT_QPA_PLATFORM"] ="offscreen"
os.environ['FASTDUP_LOCAL_DIR'] = ... | null |
6,497 | import sentry_sdk
from sentry_sdk import capture_exception
import time
import os
import sys
import traceback
import platform
import uuid
import hashlib
from fastdup.definitions import VERSION__
from functools import wraps
def fastdup_capture_exception(section, e, warn_only=False, extra=""):
if not warn_only:
... | null |
6,498 | import os
import pandas as pd
import cv2
import time
import numpy as np
import traceback
import shutil
import pathlib
from fastdup.image import plot_bounding_box, my_resize, get_type, imageformatter, create_triplet_img, fastdup_imread, calc_image_path, clean_images, pad_image, enhance_image, fastdup_imwrite
from fastdu... | Function to create and display a gallery of images computed by the similarity metrics Parameters: similarity_file (str): csv file with the computed similarities by the fastdup tool, alternatively it can be a pandas dataframe with the computed similarities. save_path (str): output folder location for the visuals num_ima... |
6,499 | import os
import pandas as pd
import cv2
import time
import numpy as np
import traceback
import shutil
import pathlib
from fastdup.image import plot_bounding_box, my_resize, get_type, imageformatter, create_triplet_img, fastdup_imread, calc_image_path, clean_images, pad_image, enhance_image, fastdup_imwrite
from fastdu... | Function to create and display a gallery of images computed by the outliers metrics Parameters: outliers_file (str): csv file with the computed outliers by the fastdup tool. Altenriously, this can be a pandas dataframe with the computed outliers. save_path (str): output folder location for the visuals num_images(int): ... |
6,500 | import os
import pandas as pd
import cv2
import time
import numpy as np
import traceback
import shutil
import pathlib
from fastdup.image import plot_bounding_box, my_resize, get_type, imageformatter, create_triplet_img, fastdup_imread, calc_image_path, clean_images, pad_image, enhance_image, fastdup_imwrite
from fastdu... | Function to create and display a gallery of images for the largest graph components Parameters: work_dir (str): path to fastdup work_dir. Alternatively (for advanced users): * pd.DataFrame containing the content of connected_components.csv file. The file columns should contain: __id,component_id,min_distance. * or pd.D... |
6,501 | import os
import pandas as pd
import cv2
import time
import numpy as np
import traceback
import shutil
import pathlib
from fastdup.image import plot_bounding_box, my_resize, get_type, imageformatter, create_triplet_img, fastdup_imread, calc_image_path, clean_images, pad_image, enhance_image, fastdup_imwrite
from fastdu... | Function to create and display a gallery of images computed by the outliers metrics. Note that fastdup generates a histogram of all the encountred valued for the specific metric. The red dashed line on this plot resulting in the number of images displayed in the report. For example, assume you have unique image values ... |
6,502 | import os
import pandas as pd
import cv2
import time
import numpy as np
import traceback
import shutil
import pathlib
from fastdup.image import plot_bounding_box, my_resize, get_type, imageformatter, create_triplet_img, fastdup_imread, calc_image_path, clean_images, pad_image, enhance_image, fastdup_imwrite
from fastdu... | Function to create and display a gallery of images computed by the outliers metrics Parameters: similarity_file (str): csv file with the computed image statistics by the fastdup tool, alternatively a pandas dataframe can be passed in directly. save_path (str): output folder location for the visuals num_images(int): Max... |
6,503 | import os
import pandas as pd
import cv2
import time
import numpy as np
import traceback
import shutil
import pathlib
from fastdup.image import plot_bounding_box, my_resize, get_type, imageformatter, create_triplet_img, fastdup_imread, calc_image_path, clean_images, pad_image, enhance_image, fastdup_imwrite
from fastdu... | Create an html gallery of images with aspect ratio stats_file: save_path: get_label_func: max_width: num_images: slice: get_reformat_filename_func: Returns: |
6,504 | import cv2
from collections import defaultdict
import json
from PIL import Image
import numpy as np
def export_to_coco(df, bbox_col, label_col, json_filename):
# Initialize COCO formatted dictionary
coco_format = defaultdict(list)
# Initialize counters for unique IDs
image_id = 0
annotation_id = 0... | null |
6,505 | import cv2
from collections import defaultdict
import json
from PIL import Image
import numpy as np
def generate_colormap(labels, hue_start=0.1, hue_end=0.9, saturation=0.9, value=0.8):
"""
Generate a colormap for a set of unique labels while avoiding bright colors.
Parameters:
labels (iterable): An... | null |
6,506 | import cv2
from collections import defaultdict
import json
from PIL import Image
import numpy as np
def generate_colormap(labels, hue_start=0.1, hue_end=0.9, saturation=0.9, value=0.8):
"""
Generate a colormap for a set of unique labels while avoiding bright colors.
Parameters:
labels (iterable): An... | null |
6,507 | import torch
from fastdup.sentry import fastdup_capture_exception
from fastdup.definitions import MISSING_LABEL
from tqdm import tqdm
import nltk
device_to_captioner = {}
from nltk.corpus import stopwords
def format_captions(captions):
# Remove stop words
filtered_text = ' '.join(word for word in captions.split... | null |
6,508 | import shutil
import os
import pandas as pd
from fastdup.image import image_base64
from pathlib import Path
import numbers
from fastdup.sentry import fastdup_capture_exception
import os
os.environ["QT_QPA_PLATFORM"] ="offscreen"
os.environ['FASTDUP_LOCAL_DIR'] = LOCAL_DIR
if not os.path.exists(so_file):
... | null |
6,509 | import shutil
import os
import pandas as pd
from fastdup.image import image_base64
from pathlib import Path
import numbers
LOCAL_DIR = os.path.dirname(__file__)
from fastdup.sentry import fastdup_capture_exception
import os
os.environ["QT_QPA_PLATFORM"] ="offscreen"
os.environ['FASTDUP_LOCAL_DIR'] = LOCAL_DI... | null |
6,510 | import numpy as np
import pandas as pd
import torch
from torch.nn import Linear, Module, Parameter, ReLU, Sequential
from torch.nn.functional import cross_entropy
from torch.optim import Adam
from torch.utils.data import DataLoader, TensorDataset
from tqdm import tqdm
from ctgan.data_transformer import DataTransformer
... | null |
6,511 | import contextlib
import numpy as np
import torch
def set_random_states(random_state, set_model_random_state):
"""Context manager for managing the random state.
Args:
random_state (int or tuple):
The random seed or a tuple of (numpy.random.RandomState, torch.Generator).
set_model_ran... | Set the random state before calling the function. Args: function (Callable): The function to wrap around. |
6,512 | import argparse
from ctgan.data import read_csv, read_tsv, write_tsv
from ctgan.synthesizers.ctgan import CTGAN
def _parse_args():
parser = argparse.ArgumentParser(description='CTGAN Command Line Interface')
parser.add_argument('-e', '--epochs', default=300, type=int,
help='Number of tr... | null |
6,513 | import json
import numpy as np
import pandas as pd
The provided code snippet includes necessary dependencies for implementing the `read_csv` function. Write a Python function `def read_csv(csv_filename, meta_filename=None, header=True, discrete=None)` to solve the following problem:
Read a csv file.
Here is the funct... | Read a csv file. |
6,514 | import json
import numpy as np
import pandas as pd
The provided code snippet includes necessary dependencies for implementing the `read_tsv` function. Write a Python function `def read_tsv(data_filename, meta_filename)` to solve the following problem:
Read a tsv file.
Here is the function:
def read_tsv(data_filename... | Read a tsv file. |
6,515 | import json
import numpy as np
import pandas as pd
The provided code snippet includes necessary dependencies for implementing the `write_tsv` function. Write a Python function `def write_tsv(data, meta, output_filename)` to solve the following problem:
Write to a tsv file.
Here is the function:
def write_tsv(data, m... | Write to a tsv file. |
6,516 | import pandas as pd
DEMO_URL = 'http://ctgan-demo.s3.amazonaws.com/census.csv.gz'
The provided code snippet includes necessary dependencies for implementing the `load_demo` function. Write a Python function `def load_demo()` to solve the following problem:
Load the demo.
Here is the function:
def load_demo():
""... | Load the demo. |
6,517 | import inspect
import operator
import os
import shutil
import stat
import sys
from pathlib import Path
import tomli
from invoke import task
from packaging.requirements import Requirement
from packaging.version import Version
def readme(c):
test_path = Path('tests/readme_test')
if test_path.exists() and test_pa... | null |
6,518 | import inspect
import operator
import os
import shutil
import stat
import sys
from pathlib import Path
import tomli
from invoke import task
from packaging.requirements import Requirement
from packaging.version import Version
def check_dependencies(c):
c.run('python -m pip check')
def unit(c):
c.run('python -m p... | null |
6,519 | import inspect
import operator
import os
import shutil
import stat
import sys
from pathlib import Path
import tomli
from invoke import task
from packaging.requirements import Requirement
from packaging.version import Version
def check_dependencies(c):
c.run('python -m pip check')
def lint(c):
check_dependencie... | null |
6,520 | import inspect
import operator
import os
import shutil
import stat
import sys
from pathlib import Path
import tomli
from invoke import task
from packaging.requirements import Requirement
from packaging.version import Version
def remove_readonly(func, path, _):
"Clear the readonly bit and reattempt the removal"
... | null |
6,521 | from packaging.version import Version, parse
from types import ModuleType
import numpy as np
import torch
import os
import warnings
import shutil
from hummingbird.ml.exceptions import ConstantError
def onnx_ml_tools_installed():
"""
Checks that *ONNXMLTools* is available.
"""
try:
import onnxmlt... | null |
6,522 | from packaging.version import Version, parse
from types import ModuleType
import numpy as np
import torch
import os
import warnings
import shutil
from hummingbird.ml.exceptions import ConstantError
def onnx_runtime_installed():
def assert_onnx_runtime_installed():
assert onnx_runtime_installed() | null |
6,523 | from packaging.version import Version, parse
from types import ModuleType
import numpy as np
import torch
import os
import warnings
import shutil
from hummingbird.ml.exceptions import ConstantError
def sparkml_installed():
"""
Checks that *Spark ML/PySpark* is available.
"""
try:
import pyspark
... | null |
6,524 | from packaging.version import Version, parse
from types import ModuleType
import numpy as np
import torch
import os
import warnings
import shutil
from hummingbird.ml.exceptions import ConstantError
def sklearn_installed():
"""
Checks that *Sklearn* is available.
"""
try:
import sklearn
r... | null |
6,525 | from packaging.version import Version, parse
from types import ModuleType
import numpy as np
import torch
import os
import warnings
import shutil
from hummingbird.ml.exceptions import ConstantError
def tvm_installed():
"""
Checks that *TVM* is available.
"""
try:
import tvm
except ImportErro... | null |
6,526 | from packaging.version import Version, parse
from types import ModuleType
import numpy as np
import torch
import os
import warnings
import shutil
from hummingbird.ml.exceptions import ConstantError
def pandas_installed():
"""
Checks that *Pandas* is available.
"""
try:
import pandas
except I... | null |
6,527 | from packaging.version import Version, parse
from types import ModuleType
import numpy as np
import torch
import os
import warnings
import shutil
from hummingbird.ml.exceptions import ConstantError
def is_on_github_actions():
return ("CI" in os.environ) and ("GITHUB_RUN_ID" in os.environ) | null |
6,528 | from packaging.version import Version, parse
from types import ModuleType
import numpy as np
import torch
import os
import warnings
import shutil
from hummingbird.ml.exceptions import ConstantError
The provided code snippet includes necessary dependencies for implementing the `get_device` function. Write a Python func... | Convenient function used to get the runtime device for the model. |
6,529 | from packaging.version import Version, parse
from types import ModuleType
import numpy as np
import torch
import os
import warnings
import shutil
from hummingbird.ml.exceptions import ConstantError
The provided code snippet includes necessary dependencies for implementing the `dump_versions` function. Write a Python f... | Utility function used to generate a string containing the versions of the main modules used to convert a model. |
6,530 | from packaging.version import Version, parse
from types import ModuleType
import numpy as np
import torch
import os
import warnings
import shutil
from hummingbird.ml.exceptions import ConstantError
The provided code snippet includes necessary dependencies for implementing the `check_dumped_versions` function. Write a ... | When a model is loaded this function is used to check that the versions of the modules used at saving time match with the version at loading time. |
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