ruotianluo

ruotianluo

import torch
import time

def timeit():
  torch.cuda.synchronize()
  start = time.time()
  x = torch.cuda.FloatTensor(10000,10000)
  torch.cuda.synchronize()
  print(time.time() - start)
  return x

ruotianluo

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.autograd import Variable

import numpy as np

ruotianluo

From 784c05a1ca663d88d68a644108ad2514b79310dc Mon Sep 17 00:00:00 2001
From: adityaarun1 <[email protected]>
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 +

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

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

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.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