brunodoamaral

import tensorflow as tf
from pathlib import Path
import pandas as pd
from tqdm import tqdm
from collections import defaultdict

# Get all event* runs from logging_dir subdirectories
logging_dir = Path('.', 'logs')
event_paths = logging_dir.glob('**/event*')

brunodoamaral

cmake \
-D CMAKE_BUILD_TYPE=RELEASE \
-D CMAKE_INSTALL_PREFIX=/path/to/anaconda3/ \
-D BUILD_NEW_PYTHON_SUPPORT=ON \
-D BUILD_OPENCV_PYTHON3=OFF \
-D BUILD_OPENCV_PYTHON2=OFF \
-D INSTALL_C_EXAMPLES=OFF \
-D INSTALL_PYTHON_EXAMPLES=OFF \
-D BUILD_EXAMPLES=OFF \
-D BUILD_JPEG=ON \

brunodoamaral

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

# LSTM
hidden_size = 20
num_layers = 2
num_directions = 1

# Input

brunodoamaral

def dice(im1, im2, empty_score=1.0):
    """
    Computes the Dice coefficient, a measure of set similarity.
    Parameters
    ----------
    im1 : array-like, bool
        Any array of arbitrary size. If not boolean, will be converted.
    im2 : array-like, bool
        Any other array of identical size. If not boolean, will be converted.
    Returns

brunodoamaral

// Convert a float RGB to int RGB. Note: does not check for floats outside range (0..1)
public static int rgbFloatToInt(float r, float g, float b) {
	return ((int) (0xff * b)) | (((int) (0xff * g)) << 8 ) | (((int) (0xff * r)) << 16 ) ;
}

brunodoamaral

import java.util.HashMap;
import java.util.Map;
import java.util.Set;
import java.util.function.Consumer;

/**
 * Created by Bruno on 01/03/2015.
 * 
 * Table with type R for rows, C for columns and V for values
 *