InnovArul

from __future__ import print_function
import torch.nn.functional as F
# from torch.autograd import Variable
# import copy
import torch
import torch.nn as nn
import torch.optim as optim
from torch.utils.data import DataLoader
from torchvision import datasets, transforms

InnovArul

# taken and improvised from : https://github.com/jiyanggao/Video-Person-ReID/issues/6

import numpy as np
from scipy.io import loadmat

q = loadmat('./mars/info/query_IDX.mat')['query_IDX'][0]
t = loadmat('./mars/info/tracks_test_info.mat')['track_test_info']

query_inds = q - 1 # to get 0 based predefined array indices for query instances 

InnovArul

running install
running bdist_egg
running egg_info
creating lltm_cuda.egg-info
writing lltm_cuda.egg-info/PKG-INFO
writing dependency_links to lltm_cuda.egg-info/dependency_links.txt
writing top-level names to lltm_cuda.egg-info/top_level.txt
writing manifest file 'lltm_cuda.egg-info/SOURCES.txt'
reading manifest file 'lltm_cuda.egg-info/SOURCES.txt'
writing manifest file 'lltm_cuda.egg-info/SOURCES.txt'

InnovArul

import torch
import torch.nn as nn


class SubModule(nn.Module):
    def __init__(self, embedding):
        super(SubModule, self).__init__()
        self.embedding = embedding
        self.fc = nn.Linear(200, 200)

InnovArul

sudo apt-get install zsh
sudo apt-get install git

sudo add-apt-repository ppa:webupd8team/sublime-text-3
sudo apt-get update
sudo apt-get install sublime-text-installer

dropbox
teamviewer
google-chrome

InnovArul

import torch, torch.nn as nn, torch.nn.functional as F
import numpy as np
import torch.optim as optim

# tied autoencoder using off the shelf nn modules
class TiedAutoEncoderOffTheShelf(nn.Module):
    def __init__(self, inp, out, weight):
        super().__init__()
        self.encoder = nn.Linear(inp, out, bias=False)
        self.decoder = nn.Linear(out, inp, bias=False)

InnovArul

import torch
import torch.nn as nn
import torch.nn.functional as F

class model(nn.Module):
    def __init__(self):
        super(model, self).__init__()
        self.conv1 = nn.Conv1d(9, 18, kernel_size=3) #9 input channels, 18 output channels
        self.conv2 = nn.Conv1d(18, 36, kernel_size=3) #18 input channels from previous Conv. layer, 36 out
        self.conv2_drop = nn.Dropout2d() #dropout

InnovArul

running install
running build_deps
+ USE_CUDA=0
+ USE_ROCM=0
+ USE_NNPACK=0
+ USE_MKLDNN=0
+ USE_GLOO_IBVERBS=0
+ USE_DISTRIBUTED_MW=0
+ FULL_CAFFE2=0
+ [[ 10 -gt 0 ]]

InnovArul

# @Author: arul
# @Date:   2017-11-13T21:45:01+05:30
# @Last modified by:   arul
# @Last modified time: 2017-11-13T22:56:47+05:30



# Your init script
#
# Atom will evaluate this file each time a new window is opened. It is run

InnovArul

    if(opt.traintype == 'finetuning' and epoch == 1) then
        -- find the number of elements for which the learning rates to be reduced
        totalParams = 0;
        isAfterCrossNeighbor = 0;
        dontFreeze = 0;

        for index, node in ipairs(model.modules) do

            --currParams = node:getParameters()