soumith

[WARNING]: No mapping options supplied. 'Naive' options will be used which might fail compilation
[WARNING]: Autotuning results won't be cached. 'cache' option is not specified
[WARNING]: Using naive options for autotuning
 
template<typename T> inline __device__ T floord(T n, T d) {
  return n < 0 ? - (-n + d - 1)/d : n / d;
}

// Halide type handling
typedef int int32;

soumith

import torch
import torch.nn as nn
from torch.autograd import Variable
import numpy as np

BATCH_SIZE = 1
INPUT_DIM = 1
OUTPUT_DIM = 1
DTYPE = np.float32

soumith

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

class TreeDecoder(nn.Module):
    NODE_DICT, NODE_LIST, NODE_STR = 0, 1, 2

    def __init__(self, input_size, max_key, max_ident, max_depth, max_length):
        super(TreeDecoder, self).__init__()

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soumith

import torch
import torch.nn as nn
import torch.nn.parallel

class DCGAN_D(nn.Container):
    def __init__(self, isize, nz, nc, ndf, ngpu, n_extra_layers=0):
        super(DCGAN_D, self).__init__()
        self.ngpu = ngpu
        assert isize % 16 == 0, "isize has to be a multiple of 16"

soumith

import torch.multiprocessing as mp
from torch.multiprocessing import Semaphore
import sys


if sys.version_info[0] == 3:
    Barrier = mp.Barrier
else: # version 2
    # from http://stackoverflow.com/a/26703365/117844
    class Barrier:

soumith

$ CUDA_VISIBLE_DEVICES=0 th -lcutorch -e "cutorch.test('sigmoid1')"
seed:   1471924845
Running 1 test
1/1 sigmoid1 ............................................................ [WAIT]torch.CudaTensor
input CPU:      0.31726333498955
input GPU:      0.31726333498955
Result CPU:     0.57865715026855
Result GPU:     0.5786572098732
torch.CudaDoubleTensor
input CPU:      0.31726332521066

soumith

[
   {
      "oC":32,
      "name":"conv",
      "strideH":2,
      "kW":3,
      "iC":3,
      "padW":0,
      "strideW":2,
      "padH":0,

soumith

-- multiple learning rates per network. Optimizes two copies of a model network and checks if the optimization steps (2) and (3) produce the same weights/parameters.

require 'torch'
require 'nn'
require 'optim'
torch.setdefaulttensortype('torch.FloatTensor')

-- (1) Define a model for this example.
local model = nn.Sequential()
model:add(nn.Linear(10,20))

soumith

-- Torch Android demo script
-- Script: main.lua
-- Copyright (C) 2013 Soumith Chintala

require 'torch'
require 'cunn'
require 'nnx'
require 'dok'
require 'image'