qubvel

import sys
import time
import requests
import itertools
import numpy as np
import pandas as pd

from tqdm import tqdm
from PIL import Image

qubvel

import sys
import time
import requests
import itertools
import numpy as np
import pandas as pd

from tqdm import tqdm
from PIL import Image

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)