qubvel

import time
import torch
import requests
import transformers
import pandas as pd
from PIL import Image
from transformers import AutoProcessor, CLIPModel, CLIPTextModel, CLIPVisionModel
print("Transformers version:", transformers.__version__)

N_ITERATIONS = 100

qubvel

import os
import glob
import torch
from typing import Mapping

# get current file directory
file_dir = os.path.dirname(os.path.realpath(__file__))

branch_1 = "main"
branch_2 = "clean-up-do-reduce-labels"

qubvel

import torch
from collections import OrderedDict
from typing import List

checkpoints_weights_paths: List[str] = ...  # sorted in descending order by score
model: torch.nn.Module = ...


def average_weights(state_dicts: List[dict]):
    everage_dict = OrderedDict()

qubvel

class TestDataset(Dataset):

	def __init__(self, base_path, ids, transform):
		self.base_path = base_path
		self.ids = ids
		self.transform = transform

	def __getitem__(self, i):
		sample = self.get_sample(i)
		if self.transform is not None:

qubvel

import pickle
from keras.layers import Conv2D, BatchNormalization, Dense

# NOTE!
# It is supposed to be used with python 3.6+ as it is rely on ordered keys of dict

def get_name(name):
    """Parse name"""
    parts = name.split('/')[:-1]
    return '/'.join(parts)