melgor

melgor

melgor

import torchvision.models as models
import torch
import torch.nn as nn
import torch.nn.parallel
import torch.backends.cudnn as cudnn
import torch.optim
from torch.autograd import Variable
import time

cudnn.benchmark = True

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# Copyright 2015 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,

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#
input: "data"
input_dim: 1
input_dim: 3
input_dim: 1025
input_dim: 2049

layer {
  name: "data_sub1"
  type: "Scale"

melgor

import tensorflow as tf
from tensorflow.contrib.layers.python.layers import initializers
slim = tf.contrib.slim

'''
============================================================================
LinkNet: Exploiting Encoder Representations for Efficient Semantic Segmentation
============================================================================
Based on the paper: https://arxiv.org/pdf/1707.03718.pdf
'''

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{
  cudnn : "deterministic"
  LR : 0.04
  nThreads : 8
  nClasses : 0
  batchSize : 204
  testOnly : false
  netType : "face_res"
  save : "cas"
  depth : 18

melgor