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

ruotianluo

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

# change accordingly.
postal_code = '60615'

def find_spot():
    headers = {

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

import torch
import torch.nn as nn
class X(nn.Module):
    def __init__(self):
        super().__init__()
        self.a = nn.Linear(3,4)
    def forward(self, x):
        x = next(self.parameters())

import torchvision

ruotianluo

import os
import tempfile
import torch
import torch.distributed as dist
import torch.nn as nn
import torch.optim as optim
import torch.multiprocessing as mp

from torch.nn.parallel import DistributedDataParallel as DDP

ruotianluo

import torch
import torch.multiprocessing as mp
import torch.distributed as dist
from torchvision import transforms
import random
from functools import partial
from easydict import EasyDict as edict
from albumentations import (
    Compose, HorizontalFlip, ShiftScaleRotate, PadIfNeeded, RandomCrop,
    RGBShift, RandomBrightness, RandomContrast

ruotianluo

import torch
import torch.multiprocessing as mp
import torch.distributed as dist
from torchvision import transforms
import random
from functools import partial
from easydict import EasyDict as edict
from albumentations import (
    Compose, HorizontalFlip, ShiftScaleRotate, PadIfNeeded, RandomCrop,
    RGBShift, RandomBrightness, RandomContrast

ruotianluo

import torch
import torch.multiprocessing as mp
import torch.distributed as dist
from torchvision import transforms
import random
from functools import partial
from easydict import EasyDict as edict
from albumentations import (
    Compose, HorizontalFlip, ShiftScaleRotate, PadIfNeeded, RandomCrop,
    RGBShift, RandomBrightness, RandomContrast

ruotianluo

# This file contains Transformer network
# Most of the code is copied from http://nlp.seas.harvard.edu/2018/04/03/attention.html

# The cfg name correspondance:
# N=num_layers
# d_model=input_encoding_size
# d_ff=rnn_size
# h is always 8

from __future__ import absolute_import

ruotianluo

From 784c05a1ca663d88d68a644108ad2514b79310dc Mon Sep 17 00:00:00 2001
From: adityaarun1 <adityaarun1@gmail.com>
Date: Wed, 16 Jan 2019 22:15:26 +0530
Subject: [PATCH] migrating to Pytorch-1.0

---
 .gitignore                                    |    2 +
 README.md                                     |   13 +-
 lib/make.sh                                   |   56 +-