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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 |
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# Find spot. | |
from bs4 import BeautifulSoup | |
import requests | |
import json | |
# change accordingly. | |
postal_code = '60615' | |
def find_spot(): | |
headers = { |
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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 |
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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 +- | |
lib/model/csrc/ROIAlign.h | 46 + |
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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 |
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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 |
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# 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 |
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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 |
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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 |
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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 |
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