mkocabas

import numpy as np
from pycocotools.coco import COCO
import os
import math
import keras
from keras.models import Model
from keras.layers import Dense, Input, Flatten, Reshape
from keras.utils import plot_model
from random import shuffle

mkocabas

name: "1546"

layer {
  name: "data"
  type: "ImageSegData"
  top: "data"
  top: "label"
  #top: "data_dim"
  include {
    phase: TRAIN

mkocabas

from keras.optimizers import Optimizer
from keras import backend as K
from keras.legacy import interfaces


class MultiSGD(Optimizer):
    """
    Modified SGD with added support for learning multiplier for kernels and biases
    as suggested in: https://github.com/fchollet/keras/issues/5920

mkocabas

def focal_loss(gamma=2, alpha=0.75):
	def focal_loss_fixed(y_true, y_pred): # compatible with tf backend
        pt_1 = tf.where(tf.equal(y_true, 1), y_pred, tf.ones_like(y_pred))
        pt_0 = tf.where(tf.equal(y_true, 0), y_pred, tf.zeros_like(y_pred))
        return -K.sum(alpha * K.pow(1. - pt_1, gamma) * K.log(pt_1))-K.sum((1-alpha) * K.pow( pt_0, gamma) * K.log(1. - pt_0))
    return focal_loss_fixed

mkocabas

tb = TensorBoard(
        log_dir='./logs/{}'.format(args.exp_dir),
        histogram_freq=0,
        batch_size=args.batch_size,
        write_graph=True,
        write_batch_performance=True,
        write_grads=False,
        write_images=False,
        embeddings_freq=0,
        embeddings_layer_names=None,

mkocabas

# coding: utf-8

# In[ ]:

"""
Copyright (c) 2017, by the Authors: Amir H. Abdi
This software is freely available under the MIT Public License. 
Please see the License file in the root for details.

mkocabas

from mininet.net import Mininet
from random import randint
from scipy import spatial
import numpy as np
import scipy as scp
import math  

## Mininet must run as ROOT !!!

# add function for nodes to move randomly when invoked

mkocabas

####################### retinanet.py
def default_null_model(
    pyramid_feature_size=256,
    name='null_submodel'
):
    options = {
        'kernel_size' : 3,
        'strides'     : 1,
        'padding'     : 'same',
    }

mkocabas

from __future__ import print_function, absolute_import

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

class FPN(nn.Module):
    def __init__(self,
                 backbone='resnet50',

mkocabas

import torch
import torch.nn.functional as F
from torch.nn.parameter import Parameter
import numpy as np


class SpatialSoftmax(torch.nn.Module):
	def __init__(self, height, width, channel, temperature=None, data_format='NCHW', unnorm=False):
		super(SpatialSoftmax, self).__init__()
		self.data_format = data_format