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  0 cs1=  0.2292 cs2=  1.3196 cs1_l=  0.2292 cs2_l=  1.3196 c1=  0.9407 c2=  0.6621
  1 cs1=  0.2308 cs2=  1.3286 cs1_l=  0.1636 cs2_l=  1.1018 c1=  0.9634 c2=  0.6934
  2 cs1=  0.2333 cs2=  1.3213 cs1_l=  0.1473 cs2_l=  1.0273 c1=  0.9688 c2=  0.7188
  3 cs1=  0.2343 cs2=  1.3243 cs1_l=  0.1395 cs2_l=  0.9930 c1=  0.9692 c2=  0.7258
  4 cs1=  0.2335 cs2=  1.3321 cs1_l=  0.1339 cs2_l=  0.9786 c1=  0.9734 c2=  0.7244
  5 cs1=  0.2314 cs2=  1.3320 cs1_l=  0.1292 cs2_l=  0.9649 c1=  0.9751 c2=  0.7263
  6 cs1=  0.2318 cs2=  1.3337 cs1_l=  0.1272 cs2_l=  0.9550 c1=  0.9763 c2=  0.7319
  7 cs1=  0.2313 cs2=  1.3411 cs1_l=  0.1246 cs2_l=  0.9546 c1=  0.9788 c2=  0.7273
  8 cs1=  0.2301 cs2=  1.3427 cs1_l=  0.1224 cs2_l=  0.9503 c1=  0.9790 c2=  0.7319
  9 cs1=  0.2287 cs2=  1.3394 cs1_l=  0.1203 cs2_l=  0.9418 c1=  0.9805 c2=  0.7341

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  0 cs1=  0.2207 cs2=  1.3196 c1=  0.9443 c2=  0.6621
  1 cs1=  0.2215 cs2=  1.3286 c1=  0.9683 c2=  0.6934
  2 cs1=  0.2242 cs2=  1.3213 c1=  0.9744 c2=  0.7188
  3 cs1=  0.2252 cs2=  1.3243 c1=  0.9749 c2=  0.7258
  4 cs1=  0.2258 cs2=  1.3321 c1=  0.9778 c2=  0.7244
  5 cs1=  0.2255 cs2=  1.3320 c1=  0.9788 c2=  0.7263
  6 cs1=  0.2247 cs2=  1.3337 c1=  0.9780 c2=  0.7319
  7 cs1=  0.2252 cs2=  1.3411 c1=  0.9788 c2=  0.7273
  8 cs1=  0.2257 cs2=  1.3427 c1=  0.9800 c2=  0.7319
  9 cs1=  0.2249 cs2=  1.3394 c1=  0.9802 c2=  0.7341

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import os

import pandas as pd
import numpy as np
import argparse

import torch
import torchvision

import models

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class AddCoords(nn.Module):

    def __init__(self, with_r=False):
        super().__init__()
        self.with_r = with_r

    def forward(self, input_tensor):
        batch_size, _, x_dim, y_dim = input_tensor.size()

        xx_channel = torch.arange(x_dim).repeat(1, y_dim, 1)

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class AddCoords(nn.Module):

    def __init__(self, with_r=False):
        super().__init__()
        self.with_r = with_r

    def forward(self, input_tensor):
        batch_size, _, x_dim, y_dim = input_tensor.size()

        xx_channel = torch.arange(x_dim).repeat(1, y_dim, 1)

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from PIL import Image
import glob

for p in glob.glob('*.png'):
    with open(p, 'rb') as f:
        img = Image.open(f)
        try:
            img.convert('RGB')
        except:
            print(p)

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import torch.utils
import torch.nn.utils
from torch.utils import data

from torchvision import transforms

from PIL import Image, ImageDraw
import numpy as np

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import torch.utils
import torch.nn.utils
from torch.utils import data

from torchvision import transforms

from PIL import Image, ImageDraw
import numpy as np

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20900 en_gen_0=    -2.006107 en_gen_1=    -2.008458 en_gen_d=    -0.002351
20900 en_pos=[   -4.6743] en_neg=[   -1.9059]
20900 en_pos_test=[   -4.3532] en_neg_test=[   -2.4173]
20900 en_pos=[   -4.6743    -4.6741     0.0001] en_neg=[   -1.9059    -1.9036     0.0023]
20900 loss= -2.0061
20900 neg_range=[    0.0000     0.9999] neg_abs_sum=  239.6976
x_pos_0 = 3 - 3 + 2 + 2      
x_pos_0 = 3 - 3 + 2 + 2      
x_neg_0 = ( 1 * ( 3 3 3      
x_neg_1 = ( ( z ( + 3 3 )    

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#!/bin/bash
#SBATCH -N 1
#SBATCH -p GPU-shared
#SBATCH --ntasks-per-node 4
#SBATCH --gres=gpu:p100:1
#SBATCH -t 47:00:00

module load python2/2.7.11_gcc
module load cuda/8.0
cd