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 def _save_pred(self, logits, filenames):

        # here need to extend from 1-dim to 3-dim in channel dimension

        invert_mask_mapping = {
            0: (255, 255, 255),   # impervious surfaces
            1: (0, 0, 255),       # Buildings
            2: (0, 255, 255),     # Low Vegetation
            3: (0, 255, 0),       # Tree
            4: (255, 255, 0),     # Car

mrluin

from collections import OrderedDict
import numpy as np

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

class ConvBnRelu(nn.Module):
    def __init__(self, inc, outc, ksize, stride, pad, dilation=1,

mrluin

import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.nn.init as init
import torchvision.models as models

from functools import partial
from opts import _nostride2dilation
from Models.layers import ASPP, _FCNHead, ConvBnRelu

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    def train_one_epoch(self, epoch, train_log_func):

        batch_time = AverageMeter()
        data_time = AverageMeter()
        losses = AverageMeter()
        accs = AverageMeter()
        mious = AverageMeter()
        fscores = AverageMeter()

        self.model.train()

mrluin

import cv2
import tqdm
import os
import glob
from Utils.tools import ensure_dir
from PIL import Image
from Configs.config import Configurations
import argparse

def image_cropper(configs, subset):

mrluin

import torch
import numpy as np
from torch.utils.data import Dataset
import torchvision.transforms.functional as TF
import random
import glob
import os
from PIL import Image

class TrainingData_Dataset(Dataset):

mrluin

from graphviz import Digraph
import torch
from torch.autograd import Variable, Function

def iter_graph(root, callback):
    queue = [root]
    seen = set()
    while queue:
        fn = queue.pop()
        if fn in seen:

mrluin

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


use_adam = False

class MyModel(nn.Module):
    def __init__(self):

mrluin

"""
A weighted version of categorical_crossentropy for keras (2.0.6). This lets you apply a weight to unbalanced classes.
@url: https://gist.github.com/wassname/ce364fddfc8a025bfab4348cf5de852d
@author: wassname
"""
from keras import backend as K
def weighted_categorical_crossentropy(weights):
    """
    A weighted version of keras.objectives.categorical_crossentropy