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## rl-tutorial-1.ipynb

      
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                ruotianluo
                / rl-tutorial-1.ipynb
            
            
              Created
              June 16, 2016 04:19
                — forked from awjuliani/rl-tutorial-1.ipynb
            
              
                Reinforcement Learning Tutorial 1 (Two-armed bandit problem)
              
          
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## tmp.py
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

## test_roialign.py
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

## Detectron.pytorch_pth1_patch.diff
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 +

## cached_transformer.py
# 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

## train_cityscapes2.py
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

## train_cityscapes2.py
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

## train_cityscapes2.py
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

## test_dist.py
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

## gist:54c25460b2ca43a274f50e1a7daa409a
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
	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
	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
	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 +
	# 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
	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
	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
	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