fromtensorbayimportGAS fromtensorbay.datasetimportDataset importnumpyasnp fromPILimportImage importcv2 fromtqdmimporttqdm importos defcat_to_one_hot(y_cat): cat2ind = {'Affection':0,'Anger':1,'Annoyance':2,'Anticipation':3,'Aversion':4, 'Confidence':5,'Disapproval':6,'Disconnection':7,'Di...
from tqdm import tqdm from torch.utils.data import DataLoader from colpali_engine.utils.colpali_processing_utils import process_images from colpali_engine.utils.dataset_transformation import load_train_set train_set = load_train_set() COMPUTE_EMBEDDINGS = False COMPUTE_HARDNEGS = False if COMPUTE...
from tqdm import tqdm# from utils.utils.logger import Logger from utils.utils.logger import global_loggertextkey = 'Content'@@ -42,29 +42,20 @@ def __init__(self, sample_config: SampleConfig=None): self.if_sample_by_length = sample_config.get("if_sample_by_length", False) ...
from tqdm import tqdm ImportError: No module named tqdm $ sudo pip2.7 install tqdm DEPRECATION: Python 2.7 will reach the end of its life on January 1st, 2020. Please upgrade your Python as Python 2.7 won't be maintained after that date. A future version of pip will drop support for Pyt...
First off you will want to install tqdm using pip. pip install tqdm Then we will want to import tqdm. Now it’s the tqdm method we want to use to display the progress of data. The tqdm module can interpret each chunk and display the progress of the file. To incorporate the progress ...
transformsfromtorch.utils.dataimportDataLoaderfromtimm.utilsimportModelEmaV3fromtqdmimporttqdmimportmatplotlib.pyplotaspltimporttorch.optimasoptimimportnumpyasnpfromtorchvision.utilsimportmake_gridfromPILimportImageclassSinusoidalEmbeddings(nn.Module):def__init__(self,time_steps,embed_dim):super().__init__()...
import os,sys,time import numpy as np import pandas as pd import datetime from tqdm import tqdm import torch from torch import nn from accelerate import Accelerator from copy import deepcopy def printlog(info): nowtime = datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S') print("\n"...
For instance, let me download and load a dataset from the skin cancer detection tutorial:import requests from tqdm import tqdm import zipfile import os def get_file(url): response = requests.get(url, stream=True) total_size = int(response.headers.get('content-length', 0)) filename = ...
frompydubimportAudioSegmentfromtqdmimporttqdmimportosdefconcatenate_audio_pydub(audio_clip_paths,output_path,verbose=1):""" Concatenates two or more audio files into one audio file using PyDub library and save it to `output_path`. A lot of extensions are supported, more on PyDub's doc. ...
def get_cos_dis(df): tfidf_array0, tfidf_array1 = tfidf_array[:df["words0"].shape[0]], tfidf_array[df["words1"].shape[0]:] from tqdm import tqdm cos = [] for a,b in tqdm(zip(tfidf_array0, tfidf_array1)): cos.append(np.dot(a, b) / (np.linalg.norm(a) * np....