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#include <opencv2/core.hpp>

int main(int argc, char** argv) {

  double x[117][43] = {{-890.2075142966416, -91.49514585853208, -1519.470669123981, -4378.297874749891, 288.75, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0},{
 328.0247571268432, -1514.656408530106, -525.5454067319092, -4091.284249468227, 841.8749389648438, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0},{
 0.4572510884940519, -0.04269169026717295, -0.8883123671621016, -4.565511104557279, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0},{
 -774.7308902380352, -63.73590155633997, -1582.833433156421, -4813.378878037186, 0, 406.8749694824219, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0},{
 437.1122295425562, -1473.381008340528, -562.1952994931905, -4437.79235664477, 0, 931.874938964843

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%matplotlib inline
import matplotlib
import matplotlib.pyplot as plt
import seaborn as sns
import numpy as np
from PIL import Image
fname = '../../test-graf/img1.png'
img = Image.open(fname).convert('RGB')
img = np.array(img)

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import torch

class CompactBilinearPooling(torch.nn.Module):
    def __init__(self, input_dim1, input_dim2, output_dim, sum_pool = True):
        super(CompactBilinearPooling, self).__init__()
        self.output_dim = output_dim
        self.sum_pool = sum_pool
        generate_sketch_matrix = lambda rand_h, rand_s, input_dim, output_dim: torch.sparse.FloatTensor(torch.stack([torch.arange(input_dim, out = torch.LongTensor()), rand_h.long()]), rand_s.float(), [input_dim, output_dim]).to_dense()
        self.sketch_matrix1 = torch.nn.Parameter(generate_sketch_matrix(torch.randint(output_dim, size = (input_dim1,)), 2 * torch.randint(2, size = (input_dim1,)) - 1, input_dim1, output_dim))
        self.sketch_matrix2 = torch.nn.Parameter(generate_sketch_matrix(torch.randint(output_dim, size = (input_dim2,)), 2 * torch.randint(2, size = (input_dim2,)) - 1, input_dim2, output_dim))

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#!/bin/bash

DIR1=$(pwd)
MAINDIR=$(pwd)/3rdparty
mkdir ${MAINDIR}
cd ${MAINDIR}
conda create -y -n "NavAgents" python=3.6
source activate NavAgents
conda install opencv -y
conda install pytorch torchvision -c pytorch -y

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hb_setup();
%%
res = rproc.read('scoresroot', ...
  fullfile(hb_path, 'matlab', 'scores', 'default'));

norm_splits = {};
norms_path = fullfile(hb_path, 'matlab', 'data', 'best_normalizations.csv');
norms = readtable(norms_path, 'delimiter', ',');
norms.Properties.RowNames = norms.descriptor;

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

def batched_grid_apply(img, grid, batch_size):
    n_patches = len(grid)
    if n_patches > batch_size:
        bs = batch_size
        n_batches = n_patches / bs + 1
        for batch_idx in range(n_batches):

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layer {
  name: "data"
  type: "Input"
  top: "data"
  input_param { shape: { dim: 2 dim: 1 dim: 65 dim: 65 } }
}

 layer {
  name: "Gx"
  type: "Convolution"