id int64 0 190k | prompt stringlengths 21 13.4M | docstring stringlengths 1 12k ⌀ |
|---|---|---|
31,724 | import re
from difflib import SequenceMatcher
import yaml
from profanityfilter import ProfanityFilter
def console_print(text, width=75):
last_newline = 0
i = 0
while i < len(text):
if text[i] == "\n":
last_newline = 0
elif last_newline > width and text[i] == " ":
tex... | null |
31,725 | import re
from difflib import SequenceMatcher
import yaml
from profanityfilter import ProfanityFilter
def get_similarity(a, b):
return SequenceMatcher(None, a, b).ratio() | null |
31,726 | import re
from difflib import SequenceMatcher
import yaml
from profanityfilter import ProfanityFilter
def get_num_options(num):
while True:
choice = input("Enter the number of your choice: ")
try:
result = int(choice)
if result >= 0 and result < num:
return ... | null |
31,727 | import re
from difflib import SequenceMatcher
import yaml
from profanityfilter import ProfanityFilter
The provided code snippet includes necessary dependencies for implementing the `player_died` function. Write a Python function `def player_died(text)` to solve the following problem:
TODO: Add in more sophisticated NL... | TODO: Add in more sophisticated NLP, maybe a custom classifier trained on hand-labelled data that classifies second-person statements as resulting in death or not. |
31,728 | import re
from difflib import SequenceMatcher
import yaml
from profanityfilter import ProfanityFilter
def player_won(text):
lower_text = text.lower()
won_phrases = [
"you ((\w* )*and |)live happily ever after",
"you ((\w* )*and |)live (forever|eternally|for eternity)",
"you ((\w* )*and ... | null |
31,729 | import re
from difflib import SequenceMatcher
import yaml
from profanityfilter import ProfanityFilter
pf = ProfanityFilter(custom_censor_list=censored_words)
def remove_profanity(text):
return pf.censor(text) | null |
31,730 | import re
from difflib import SequenceMatcher
import yaml
from profanityfilter import ProfanityFilter
def split_first_sentence(text):
first_period = text.find(".")
first_exclamation = text.find("!")
if first_exclamation < first_period and first_exclamation > 0:
split_point = first_exclamation + 1
... | null |
31,731 | import re
from difflib import SequenceMatcher
import yaml
from profanityfilter import ProfanityFilter
def cut_trailing_quotes(text):
num_quotes = text.count('"')
if num_quotes % 2 is 0:
return text
else:
final_ind = text.rfind('"')
return text[:final_ind]
def cut_trailing_action(text... | null |
31,732 | import re
from difflib import SequenceMatcher
import yaml
from profanityfilter import ProfanityFilter
def mapping_variation_pairs(mapping):
mapping_list = []
mapping_list.append((" " + mapping[0] + " ", " " + mapping[1] + " "))
mapping_list.append(
(" " + capitalize(mapping[0]) + " ", " " + capitali... | null |
31,733 | import re
from difflib import SequenceMatcher
import yaml
from profanityfilter import ProfanityFilter
def mapping_variation_pairs(mapping):
second_to_first_mappings = [
("you're", "I'm"),
("your", "my"),
("you are", "I am"),
("you were", "I was"),
("are you", "am I"),
("you", "I"),
("you", "... | null |
31,734 | import re
from difflib import SequenceMatcher
import yaml
from profanityfilter import ProfanityFilter
def replace_outside_quotes(text, current_word, repl_word):
def mapping_variation_pairs(mapping):
first_to_second_mappings = [
("I'm", "you're"),
("Im", "you're"),
("Ive", "you've"),
("I am", "you are"),... | null |
31,735 | import re
from difflib import SequenceMatcher
import yaml
from profanityfilter import ProfanityFilter
def replace_outside_quotes(text, current_word, repl_word):
text = standardize_punctuation(text)
reg_expr = re.compile(current_word + '(?=([^"]*"[^"]*")*[^"]*$)')
output = reg_expr.sub(repl_word, text)
r... | null |
31,736 | import json
import os
from story.utils import *
with open(output_file_path, "w") as output_file:
filenames = ["writingprompts/" + file for file in files]
cleaned_stories = []
for filename in filenames:
print("Processing file ", filename)
stories = load_stories(filename)
for story in ... | null |
31,737 | import json
import os
from story.utils import *
def modify_story(story):
text = story["body"]
if len(text) < 100:
return None
first_person = is_first_person(text)
second_person = is_second_person(text)
if first_person or second_person:
return first_to_second_person(text)
else:... | null |
31,738 | import json
import time
from selenium import webdriver
from selenium.webdriver.chrome.options import Options
def save_tree(tree, filename):
with open(filename, "w") as fp:
json.dump(tree, fp) | null |
31,739 | import csv
import json
from story.utils import *
def load_tree(filename):
with open(filename, "r") as fp:
tree = json.load(fp)
return tree
def make_stories(current_story, tree):
stories = []
action = first_to_second_person(tree["action"])
action_list = action.split(" ")
first_word = acti... | null |
31,740 | import csv
import json
import os
def data_to_forest(filename):
trees = []
rows = []
with open(filename, newline="") as f:
reader = csv.reader(f)
for row in reader:
rows.append(row)
for i in range(1, len(rows[0])):
tree = {}
tree["tree_id"] = rows[0][i]
... | null |
31,741 | import csv
import json
import os
def build_action_samples_helper(context, story_block, action_results, path, tree_id):
samples = []
for i, action_result in enumerate(action_results):
new_path = path[:]
new_path.append(i)
if (
len(action_result["action_results"]) is 0
... | null |
31,742 | import csv
import json
import os
def build_result_samples_helper(
context, story_block, parent_action_result, path, tree_id
):
samples = []
action_results = parent_action_result["action_results"]
for i, action_result in enumerate(action_results):
new_path = path[:]
new_path.append(i)
... | null |
31,743 | import csv
import json
import os
def save_tree(tree, filename):
def save_forest(forest, forest_name):
if not os.path.exists("./" + forest_name):
os.mkdir("./" + forest_name)
for tree in forest:
save_tree(tree, "./" + forest_name + "/" + tree["tree_id"] + ".json") | null |
31,744 | import csv
import json
import os
def load_tree(filename):
with open(filename, "r") as fp:
tree = json.load(fp)
return tree
def load_forest(forest_name):
files = os.listdir("./" + forest_name)
forest = []
for file in files:
forest.append(load_tree("./" + forest_name + "/" + file))
... | null |
31,745 | import csv
import json
import os
def load_tree(filename):
with open(filename, "r") as fp:
tree = json.load(fp)
return tree
def csv_to_dict(file):
update_dict = {}
field_names = []
with open(file, newline="") as f:
reader = csv.reader(f)
for row in reader:
if len(u... | null |
31,746 | import csv
import json
import os
def load_tree(filename):
with open(filename, "r") as fp:
tree = json.load(fp)
return tree
def csv_to_dict(file):
update_dict = {}
field_names = []
with open(file, newline="") as f:
reader = csv.reader(f)
for row in reader:
if len(u... | null |
31,747 | import csv
import json
import os
tree = data_to_forest("upwork.csv")
for i, story in enumerate(tree):
save_tree(story, "crowdsourcedstory" + str(i) + ".json")
def data_to_forest(filename):
trees = []
rows = []
with open(filename, newline="") as f:
reader = csv.reader(f)
for row in rea... | null |
31,748 | import csv
import json
import os
def build_action_samples_helper(context, story_block, action_results, path, tree_id):
samples = []
for i, action_result in enumerate(action_results):
new_path = path[:]
new_path.append(i)
if (
len(action_result["action_results"]) is 0
... | null |
31,749 | import csv
import json
import os
def build_result_samples_helper(
context, story_block, parent_action_result, path, tree_id
):
tree = data_to_forest("upwork.csv")
for i, story in enumerate(tree):
save_tree(story, "crowdsourcedstory" + str(i) + ".json")
def make_write_results_batch(forest, filename):
with ... | null |
31,750 | import csv
import json
import os
def save_tree(tree, filename):
with open(filename, "w") as fp:
json.dump(tree, fp)
tree = data_to_forest("upwork.csv")
def save_forest(forest, forest_name):
if not os.path.exists("./" + forest_name):
os.mkdir("./" + forest_name)
for tree in forest:
... | null |
31,752 | import csv
import json
import os
def load_tree(filename):
with open(filename, "r") as fp:
tree = json.load(fp)
return tree
def csv_to_dict(file):
update_dict = {}
field_names = []
with open(file, newline="") as f:
reader = csv.reader(f)
for row in reader:
if len(u... | null |
31,753 | import csv
import json
import os
def load_tree(filename):
with open(filename, "r") as fp:
tree = json.load(fp)
return tree
def csv_to_dict(file):
update_dict = {}
field_names = []
with open(file, newline="") as f:
reader = csv.reader(f)
for row in reader:
if len(u... | null |
31,754 | import os
import random
import sys
import time
import argparse
from generator.gpt2.gpt2_generator import *
from story import grammars
from story.story_manager import *
from story.utils import *
def splash():
print("0) New Game\n1) Load Game\n")
choice = get_num_options(2)
if choice == 1:
return "loa... | Entry/main function for starting AIDungeon 2 Arguments: args (namespace): Arguments returned by the ArgumentParser |
31,755 | import tensorflow as tf
from generator.gpt2.src import model
def penalize_used(logits, output):
# I want to change the indices of logits wherever the index is found in output
change_tensor = tf.zeros_like(logits, dtype=logits.dtype)
unique = tf.unique(output[0])[0]
ones = tf.ones_like(unique, dtype=uniq... | null |
31,756 | import numpy as np
import tensorflow as tf
from tensorflow.contrib.training import HParams
def default_hparams():
return HParams(n_vocab=0, n_ctx=1024, n_embd=768, n_head=12, n_layer=12,) | null |
31,757 | import json
import os
from functools import lru_cache
import regex as re
The provided code snippet includes necessary dependencies for implementing the `bytes_to_unicode` function. Write a Python function `def bytes_to_unicode()` to solve the following problem:
Returns list of utf-8 byte and a corresponding list of un... | Returns list of utf-8 byte and a corresponding list of unicode strings. The reversible bpe codes work on unicode strings. This means you need a large # of unicode characters in your vocab if you want to avoid UNKs. When you're at something like a 10B token dataset you end up needing around 5K for decent coverage. This ... |
31,758 | import json
import os
from functools import lru_cache
import regex as re
The provided code snippet includes necessary dependencies for implementing the `get_pairs` function. Write a Python function `def get_pairs(word)` to solve the following problem:
Return set of symbol pairs in a word. Word is represented as tuple ... | Return set of symbol pairs in a word. Word is represented as tuple of symbols (symbols being variable-length strings). |
31,759 | import json
import os
from functools import lru_cache
import regex as re
class Encoder:
def __init__(self, encoder, bpe_merges, errors="replace"):
self.encoder = encoder
self.decoder = {v: k for k, v in self.encoder.items()}
self.errors = errors # how to handle errors in decoding
se... | null |
31,760 | import os
import sys
import time
from generator.gpt2.gpt2_generator import *
from generator.human_dm import *
from play import *
from story.story_manager import *
from story.utils import *
class AIPlayer:
def __init__(self, generator):
self.generator = generator
def get_action(self, prompt):
ret... | null |
31,761 | import argparse
import os
import random
import numpy as np
import torch
import torch.backends.cudnn as cudnn
import minigpt4.tasks as tasks
from minigpt4.common.config import Config
from minigpt4.common.dist_utils import get_rank, init_distributed_mode
from minigpt4.common.logger import setup_logger
from minigpt4.commo... | null |
31,762 | import argparse
import os
import random
import numpy as np
import torch
import torch.backends.cudnn as cudnn
import minigpt4.tasks as tasks
from minigpt4.common.config import Config
from minigpt4.common.dist_utils import get_rank, init_distributed_mode
from minigpt4.common.logger import setup_logger
from minigpt4.commo... | null |
31,763 | import argparse
import os
import random
import numpy as np
import torch
import torch.backends.cudnn as cudnn
import minigpt4.tasks as tasks
from minigpt4.common.config import Config
from minigpt4.common.dist_utils import get_rank, init_distributed_mode
from minigpt4.common.logger import setup_logger
from minigpt4.commo... | Get runner class from config. Default to epoch-based runner. |
31,764 | import argparse
import os
import random
import numpy as np
import torch
import torch.backends.cudnn as cudnn
import gradio as gr
from minigpt4.common.config import Config
from minigpt4.common.dist_utils import get_rank
from minigpt4.common.registry import registry
from minigpt4.conversation.conversation_video import Ch... | null |
31,765 | import argparse
import os
import random
import numpy as np
import torch
import torch.backends.cudnn as cudnn
import gradio as gr
from minigpt4.common.config import Config
from minigpt4.common.dist_utils import get_rank
from minigpt4.common.registry import registry
from minigpt4.conversation.conversation_video import Ch... | null |
31,766 | import argparse
import os
import random
import numpy as np
import torch
import torch.backends.cudnn as cudnn
import gradio as gr
from minigpt4.common.config import Config
from minigpt4.common.dist_utils import get_rank
from minigpt4.common.registry import registry
from minigpt4.conversation.conversation_video import Ch... | null |
31,767 | import argparse
import os
import random
import numpy as np
import torch
import torch.backends.cudnn as cudnn
import gradio as gr
from minigpt4.common.config import Config
from minigpt4.common.dist_utils import get_rank
from minigpt4.common.registry import registry
from minigpt4.conversation.conversation_video import Ch... | null |
31,768 | import argparse
import os
import random
import numpy as np
import torch
import torch.backends.cudnn as cudnn
import gradio as gr
from minigpt4.common.config import Config
from minigpt4.common.dist_utils import get_rank
from minigpt4.common.registry import registry
from minigpt4.conversation.conversation_video import Ch... | null |
31,769 | import argparse
import os
import random
import numpy as np
import torch
import torch.backends.cudnn as cudnn
import gradio as gr
from minigpt4.common.config import Config
from minigpt4.common.dist_utils import get_rank
from minigpt4.common.registry import registry
from minigpt4.conversation.conversation_video import Ch... | null |
31,770 | import argparse
import os
import random
import numpy as np
import torch
import torch.backends.cudnn as cudnn
import gradio as gr
from minigpt4.common.config import Config
from minigpt4.common.dist_utils import get_rank
from minigpt4.common.registry import registry
from minigpt4.conversation.conversation import Chat, CO... | null |
31,771 | import argparse
import os
import random
import numpy as np
import torch
import torch.backends.cudnn as cudnn
import gradio as gr
from minigpt4.common.config import Config
from minigpt4.common.dist_utils import get_rank
from minigpt4.common.registry import registry
from minigpt4.conversation.conversation import Chat, CO... | null |
31,772 | import argparse
import os
import random
import numpy as np
import torch
import torch.backends.cudnn as cudnn
import gradio as gr
from minigpt4.common.config import Config
from minigpt4.common.dist_utils import get_rank
from minigpt4.common.registry import registry
from minigpt4.conversation.conversation import Chat, CO... | null |
31,773 | import argparse
import os
import random
import numpy as np
import torch
import torch.backends.cudnn as cudnn
import gradio as gr
from minigpt4.common.config import Config
from minigpt4.common.dist_utils import get_rank
from minigpt4.common.registry import registry
from minigpt4.conversation.conversation import Chat, CO... | null |
31,774 | import argparse
import os
import random
import numpy as np
import torch
import torch.backends.cudnn as cudnn
import gradio as gr
from minigpt4.common.config import Config
from minigpt4.common.dist_utils import get_rank
from minigpt4.common.registry import registry
from minigpt4.conversation.conversation import Chat, CO... | null |
31,775 | import argparse
import os
import random
import numpy as np
import torch
import torch.backends.cudnn as cudnn
import gradio as gr
from minigpt4.common.config import Config
from minigpt4.common.dist_utils import get_rank
from minigpt4.common.registry import registry
from minigpt4.conversation.conversation import Chat, CO... | null |
31,776 | import cv2
import numpy as np
import torch
def identity_func(img):
return img | null |
31,777 | import cv2
import numpy as np
import torch
The provided code snippet includes necessary dependencies for implementing the `autocontrast_func` function. Write a Python function `def autocontrast_func(img, cutoff=0)` to solve the following problem:
same output as PIL.ImageOps.autocontrast
Here is the function:
def aut... | same output as PIL.ImageOps.autocontrast |
31,778 | import cv2
import numpy as np
import torch
The provided code snippet includes necessary dependencies for implementing the `equalize_func` function. Write a Python function `def equalize_func(img)` to solve the following problem:
same output as PIL.ImageOps.equalize PIL's implementation is different from cv2.equalize
... | same output as PIL.ImageOps.equalize PIL's implementation is different from cv2.equalize |
31,779 | import cv2
import numpy as np
import torch
The provided code snippet includes necessary dependencies for implementing the `rotate_func` function. Write a Python function `def rotate_func(img, degree, fill=(0, 0, 0))` to solve the following problem:
like PIL, rotate by degree, not radians
Here is the function:
def ro... | like PIL, rotate by degree, not radians |
31,780 | import cv2
import numpy as np
import torch
The provided code snippet includes necessary dependencies for implementing the `solarize_func` function. Write a Python function `def solarize_func(img, thresh=128)` to solve the following problem:
same output as PIL.ImageOps.posterize
Here is the function:
def solarize_fun... | same output as PIL.ImageOps.posterize |
31,781 | import cv2
import numpy as np
import torch
The provided code snippet includes necessary dependencies for implementing the `color_func` function. Write a Python function `def color_func(img, factor)` to solve the following problem:
same output as PIL.ImageEnhance.Color
Here is the function:
def color_func(img, factor... | same output as PIL.ImageEnhance.Color |
31,782 | import cv2
import numpy as np
import torch
The provided code snippet includes necessary dependencies for implementing the `contrast_func` function. Write a Python function `def contrast_func(img, factor)` to solve the following problem:
same output as PIL.ImageEnhance.Contrast
Here is the function:
def contrast_func... | same output as PIL.ImageEnhance.Contrast |
31,783 | import cv2
import numpy as np
import torch
The provided code snippet includes necessary dependencies for implementing the `brightness_func` function. Write a Python function `def brightness_func(img, factor)` to solve the following problem:
same output as PIL.ImageEnhance.Contrast
Here is the function:
def brightnes... | same output as PIL.ImageEnhance.Contrast |
31,784 | import cv2
import numpy as np
import torch
The provided code snippet includes necessary dependencies for implementing the `sharpness_func` function. Write a Python function `def sharpness_func(img, factor)` to solve the following problem:
The differences the this result and PIL are all on the 4 boundaries, the center ... | The differences the this result and PIL are all on the 4 boundaries, the center areas are same |
31,785 | import cv2
import numpy as np
import torch
def shear_x_func(img, factor, fill=(0, 0, 0)):
H, W = img.shape[0], img.shape[1]
M = np.float32([[1, factor, 0], [0, 1, 0]])
out = cv2.warpAffine(
img, M, (W, H), borderValue=fill, flags=cv2.INTER_LINEAR
).astype(np.uint8)
return out | null |
31,786 | import cv2
import numpy as np
import torch
The provided code snippet includes necessary dependencies for implementing the `translate_x_func` function. Write a Python function `def translate_x_func(img, offset, fill=(0, 0, 0))` to solve the following problem:
same output as PIL.Image.transform
Here is the function:
d... | same output as PIL.Image.transform |
31,787 | import cv2
import numpy as np
import torch
The provided code snippet includes necessary dependencies for implementing the `translate_y_func` function. Write a Python function `def translate_y_func(img, offset, fill=(0, 0, 0))` to solve the following problem:
same output as PIL.Image.transform
Here is the function:
d... | same output as PIL.Image.transform |
31,788 | import cv2
import numpy as np
import torch
The provided code snippet includes necessary dependencies for implementing the `posterize_func` function. Write a Python function `def posterize_func(img, bits)` to solve the following problem:
same output as PIL.ImageOps.posterize
Here is the function:
def posterize_func(i... | same output as PIL.ImageOps.posterize |
31,789 | import cv2
import numpy as np
import torch
def shear_y_func(img, factor, fill=(0, 0, 0)):
H, W = img.shape[0], img.shape[1]
M = np.float32([[1, 0, 0], [factor, 1, 0]])
out = cv2.warpAffine(
img, M, (W, H), borderValue=fill, flags=cv2.INTER_LINEAR
).astype(np.uint8)
return out | null |
31,790 | import cv2
import numpy as np
import torch
def cutout_func(img, pad_size, replace=(0, 0, 0)):
replace = np.array(replace, dtype=np.uint8)
H, W = img.shape[0], img.shape[1]
rh, rw = np.random.random(2)
pad_size = pad_size // 2
ch, cw = int(rh * H), int(rw * W)
x1, x2 = max(ch - pad_size, 0), min... | null |
31,791 | import cv2
import numpy as np
import torch
def enhance_level_to_args(MAX_LEVEL):
def level_to_args(level):
return ((level / MAX_LEVEL) * 1.8 + 0.1,)
return level_to_args | null |
31,792 | import cv2
import numpy as np
import torch
def shear_level_to_args(MAX_LEVEL, replace_value):
def level_to_args(level):
level = (level / MAX_LEVEL) * 0.3
if np.random.random() > 0.5:
level = -level
return (level, replace_value)
return level_to_args | null |
31,793 | import cv2
import numpy as np
import torch
def translate_level_to_args(translate_const, MAX_LEVEL, replace_value):
def level_to_args(level):
level = (level / MAX_LEVEL) * float(translate_const)
if np.random.random() > 0.5:
level = -level
return (level, replace_value)
return... | null |
31,794 | import cv2
import numpy as np
import torch
def cutout_level_to_args(cutout_const, MAX_LEVEL, replace_value):
def level_to_args(level):
level = int((level / MAX_LEVEL) * cutout_const)
return (level, replace_value)
return level_to_args | null |
31,795 | import cv2
import numpy as np
import torch
def solarize_level_to_args(MAX_LEVEL):
def level_to_args(level):
level = int((level / MAX_LEVEL) * 256)
return (level,)
return level_to_args | null |
31,796 | import cv2
import numpy as np
import torch
def none_level_to_args(level):
return () | null |
31,797 | import cv2
import numpy as np
import torch
def posterize_level_to_args(MAX_LEVEL):
def level_to_args(level):
level = int((level / MAX_LEVEL) * 4)
return (level,)
return level_to_args | null |
31,798 | import cv2
import numpy as np
import torch
def rotate_level_to_args(MAX_LEVEL, replace_value):
def level_to_args(level):
level = (level / MAX_LEVEL) * 30
if np.random.random() < 0.5:
level = -level
return (level, replace_value)
return level_to_args | null |
31,799 | import gzip
import logging
import os
import random as rnd
import tarfile
import zipfile
import random
from typing import List
from tqdm import tqdm
import decord
from decord import VideoReader
import webdataset as wds
import numpy as np
import torch
from torch.utils.data.dataset import IterableDataset
from minigpt4.com... | null |
31,800 | import gzip
import logging
import os
import random as rnd
import tarfile
import zipfile
import random
from typing import List
from tqdm import tqdm
import decord
from decord import VideoReader
import webdataset as wds
import numpy as np
import torch
from torch.utils.data.dataset import IterableDataset
from minigpt4.com... | Organizes datasets by split. Args: datasets: dict of torch.utils.data.Dataset objects by name. Returns: Dict of datasets by split {split_name: List[Datasets]}. |
31,801 | import gzip
import logging
import os
import random as rnd
import tarfile
import zipfile
import random
from typing import List
from tqdm import tqdm
import decord
from decord import VideoReader
import webdataset as wds
import numpy as np
import torch
from torch.utils.data.dataset import IterableDataset
from minigpt4.com... | Concatenates multiple datasets into a single dataset. It supports may-style datasets and DataPipeline from WebDataset. Currently, does not support generic IterableDataset because it requires creating separate samplers. Now only supports conctenating training datasets and assuming validation and testing have only a sing... |
31,802 | import logging
import os
import shutil
import warnings
from omegaconf import OmegaConf
import torch.distributed as dist
from torchvision.datasets.utils import download_url
import minigpt4.common.utils as utils
from minigpt4.common.dist_utils import is_dist_avail_and_initialized, is_main_process
from minigpt4.common.reg... | null |
31,803 | import time
import random
import torch
from minigpt4.datasets.data_utils import move_to_cuda
from torch.utils.data import DataLoader
def record_cuda_stream(batch):
if isinstance(batch, torch.Tensor):
batch.record_stream(torch.cuda.current_stream())
elif isinstance(batch, list) or isinstance(batch, tupl... | null |
31,804 | import io
import json
import logging
import os
import pickle
import re
import shutil
import urllib
import urllib.error
import urllib.request
from typing import Optional
from urllib.parse import urlparse
import numpy as np
import pandas as pd
import yaml
from iopath.common.download import download
from iopath.common.fil... | null |
31,805 | import io
import json
import logging
import os
import pickle
import re
import shutil
import urllib
import urllib.error
import urllib.request
from typing import Optional
from urllib.parse import urlparse
import numpy as np
import pandas as pd
import yaml
from iopath.common.download import download
from iopath.common.fil... | null |
31,806 | import io
import json
import logging
import os
import pickle
import re
import shutil
import urllib
import urllib.error
import urllib.request
from typing import Optional
from urllib.parse import urlparse
import numpy as np
import pandas as pd
import yaml
from iopath.common.download import download
from iopath.common.fil... | null |
31,807 | import io
import json
import logging
import os
import pickle
import re
import shutil
import urllib
import urllib.error
import urllib.request
from typing import Optional
from urllib.parse import urlparse
import numpy as np
import pandas as pd
import yaml
from iopath.common.download import download
from iopath.common.fil... | null |
31,808 | import io
import json
import logging
import os
import pickle
import re
import shutil
import urllib
import urllib.error
import urllib.request
from typing import Optional
from urllib.parse import urlparse
import numpy as np
import pandas as pd
import yaml
from iopath.common.download import download
from iopath.common.fil... | null |
31,809 | import io
import json
import logging
import os
import pickle
import re
import shutil
import urllib
import urllib.error
import urllib.request
from typing import Optional
from urllib.parse import urlparse
import numpy as np
import pandas as pd
import yaml
from iopath.common.download import download
from iopath.common.fil... | Utility function to transform a view URL of google drive to a download URL for google drive Example input: https://drive.google.com/file/d/137RyRjvTBkBiIfeYBNZBtViDHQ6_Ewsp/view Example output: https://drive.google.com/uc?export=download&id=137RyRjvTBkBiIfeYBNZBtViDHQ6_Ewsp |
31,810 | import io
import json
import logging
import os
import pickle
import re
import shutil
import urllib
import urllib.error
import urllib.request
from typing import Optional
from urllib.parse import urlparse
import numpy as np
import pandas as pd
import yaml
from iopath.common.download import download
from iopath.common.fil... | Download a file from google drive Downloading an URL from google drive requires confirmation when the file of the size is too big (google drive notifies that anti-viral checks cannot be performed on such files) |
31,811 | import io
import json
import logging
import os
import pickle
import re
import shutil
import urllib
import urllib.error
import urllib.request
from typing import Optional
from urllib.parse import urlparse
import numpy as np
import pandas as pd
import yaml
from iopath.common.download import download
from iopath.common.fil... | null |
31,812 | import io
import json
import logging
import os
import pickle
import re
import shutil
import urllib
import urllib.error
import urllib.request
from typing import Optional
from urllib.parse import urlparse
import numpy as np
import pandas as pd
import yaml
from iopath.common.download import download
from iopath.common.fil... | This implementation downloads the remote resource and caches it locally. The resource will only be downloaded if not previously requested. |
31,813 | import io
import json
import logging
import os
import pickle
import re
import shutil
import urllib
import urllib.error
import urllib.request
from typing import Optional
from urllib.parse import urlparse
import numpy as np
import pandas as pd
import yaml
from iopath.common.download import download
from iopath.common.fil... | Simply create the symlinks for a given file1 to file2. Useful during model checkpointing to symlinks to the latest successful checkpoint. |
31,814 | import io
import json
import logging
import os
import pickle
import re
import shutil
import urllib
import urllib.error
import urllib.request
from typing import Optional
from urllib.parse import urlparse
import numpy as np
import pandas as pd
import yaml
from iopath.common.download import download
from iopath.common.fil... | Common i/o utility to handle saving data to various file formats. Supported: .pkl, .pickle, .npy, .json Specifically for .json, users have the option to either append (default) or rewrite by passing in Boolean value to append_to_json. |
31,815 | import io
import json
import logging
import os
import pickle
import re
import shutil
import urllib
import urllib.error
import urllib.request
from typing import Optional
from urllib.parse import urlparse
import numpy as np
import pandas as pd
import yaml
from iopath.common.download import download
from iopath.common.fil... | Common i/o utility to handle loading data from various file formats. Supported: .pkl, .pickle, .npy, .json For the npy files, we support reading the files in mmap_mode. If the mmap_mode of reading is not successful, we load data without the mmap_mode. |
31,816 | import io
import json
import logging
import os
import pickle
import re
import shutil
import urllib
import urllib.error
import urllib.request
from typing import Optional
from urllib.parse import urlparse
import numpy as np
import pandas as pd
import yaml
from iopath.common.download import download
from iopath.common.fil... | Make a path absolute, but take into account prefixes like "http://" or "manifold://" |
31,817 | import io
import json
import logging
import os
import pickle
import re
import shutil
import urllib
import urllib.error
import urllib.request
from typing import Optional
from urllib.parse import urlparse
import numpy as np
import pandas as pd
import yaml
from iopath.common.download import download
from iopath.common.fil... | Check if an input string is a url. look for http(s):// and ignoring the case |
31,818 | import io
import json
import logging
import os
import pickle
import re
import shutil
import urllib
import urllib.error
import urllib.request
from typing import Optional
from urllib.parse import urlparse
import numpy as np
import pandas as pd
import yaml
from iopath.common.download import download
from iopath.common.fil... | Utility for deleting a directory. Useful for cleaning the storage space that contains various training artifacts like checkpoints, data etc. |
31,819 | import io
import json
import logging
import os
import pickle
import re
import shutil
import urllib
import urllib.error
import urllib.request
from typing import Optional
from urllib.parse import urlparse
import numpy as np
import pandas as pd
import yaml
from iopath.common.download import download
from iopath.common.fil... | Given a file, get the size of file in MB |
31,820 | import numpy as np
from matplotlib import pyplot as plt
from scipy.ndimage import filters
from skimage import transform as skimage_transform
def getAttMap(img, attMap, blur=True, overlap=True):
attMap -= attMap.min()
if attMap.max() > 0:
attMap /= attMap.max()
attMap = skimage_transform.resize(attM... | null |
31,821 | import datetime
import functools
import os
import torch
import torch.distributed as dist
import timm.models.hub as timm_hub
def setup_for_distributed(is_master):
def init_distributed_mode(args):
if "RANK" in os.environ and "WORLD_SIZE" in os.environ:
args.rank = int(os.environ["RANK"])
args.world_s... | null |
31,822 | import datetime
import functools
import os
import torch
import torch.distributed as dist
import timm.models.hub as timm_hub
def get_dist_info():
if torch.__version__ < "1.0":
initialized = dist._initialized
else:
initialized = dist.is_initialized()
if initialized:
rank = dist.get_ran... | null |
31,823 | import datetime
import functools
import os
import torch
import torch.distributed as dist
import timm.models.hub as timm_hub
def is_dist_avail_and_initialized():
if not dist.is_available():
return False
if not dist.is_initialized():
return False
return True
def is_main_process():
return g... | Download a file from a URL and cache it locally. If the file already exists, it is not downloaded again. If distributed, only the main process downloads the file, and the other processes wait for the file to be downloaded. |
31,824 | import logging
import json
from typing import Dict
from omegaconf import OmegaConf
from minigpt4.common.registry import registry
def node_to_dict(node):
return OmegaConf.to_container(node) | null |
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