ruotianluo

import torch
import torch.nn as nn

class involution(torch.nn.Module):
    def __init__(self):
        super().__init__()
        self.K = K = 3
        self.C = C = 256
        self.r = r =  64
        self.G = G = 64

ruotianluo

# Find spot.
from bs4 import BeautifulSoup
import requests
import json

# change accordingly.
postal_code = '60615'

def find_spot():
    headers = {

ruotianluo

class InfiniteConcatDistributedSampler(DistributedSampler):

    def __init__(self, *args, **kwargs):
        """
        Args:
            global_batch_size: since infinite indices will wrap,
                so it is possible that same images in one batch.
                We apply drop_last here in the sampler.
            determistic: we always start the generator with seed 0,
                and then to restart from certain iteration, we just