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model_cutie_aspp.py
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model_cutie_aspp.py
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# written by Xiaohui Zhao
# 2019-03
# xiaohui.zhao@outlook.com
import tensorflow as tf
from model_cutie import CUTIE
class CUTIERes(CUTIE):
def __init__(self, num_vocabs, num_classes, params, trainable=True):
self.name = "CUTIE_atrousSPP" #
self.data_grid = tf.placeholder(tf.int32, shape=[None, None, None, 1], name='data_grid')
self.gt_classes = tf.placeholder(tf.int32, shape=[None, None, None], name='gt_classes')
self.data_image = tf.placeholder(tf.float32, shape=[None, None, None, 3], name='data_image') # not used in CUTIEv1
self.ps_1d_indices = tf.placeholder(tf.int32, shape=[None, None], name='ps_1d_indices') # not used in CUTIEv1
self.use_ghm = tf.equal(1, params.use_ghm) if hasattr(params, 'use_ghm') else tf.equal(1, 0) #params.use_ghm
self.activation = 'sigmoid' if (hasattr(params, 'use_ghm') and params.use_ghm) else 'relu'
self.dropout = params.data_augmentation_dropout if hasattr(params, 'data_augmentation_dropout') else 1
self.ghm_weights = tf.placeholder(tf.float32, shape=[None, None, None, num_classes], name='ghm_weights')
self.layers = dict({'data_grid': self.data_grid, 'gt_classes': self.gt_classes, 'ghm_weights':self.ghm_weights})
self.num_vocabs = num_vocabs
self.num_classes = num_classes
self.trainable = trainable
self.embedding_size = params.embedding_size
self.weight_decay = params.weight_decay if hasattr(params, 'weight_decay') else 0.0
self.hard_negative_ratio = params.hard_negative_ratio if hasattr(params, 'hard_negative_ratio') else 0.0
self.batch_size = params.batch_size if hasattr(params, 'batch_size') else 0
self.layer_inputs = []
self.setup()
def setup(self):
# input
(self.feed('data_grid')
.embed(self.num_vocabs, self.embedding_size, name='embedding', dropout=self.dropout))
# encoder
(self.feed('embedding')
.conv(3, 5, 256, 1, 1, name='encoder1_1')
.conv(3, 5, 256, 1, 1, name='encoder1_2')
.conv(3, 5, 256, 1, 1, name='encoder1_3')
.conv(3, 5, 256, 1, 1, name='encoder1_4')
.dilate_conv(3, 5, 256, 1, 1, 2, name='encoder1_5')
.dilate_conv(3, 5, 256, 1, 1, 4, name='encoder1_6')
.dilate_conv(3, 5, 256, 1, 1, 8, name='encoder1_7')
.dilate_conv(3, 5, 256, 1, 1, 16, name='encoder1_8'))
# Atrous Spatial Pyramid Pooling module
#(self.feed('encoder1_8')
# .conv(1, 1, 256, 1, 1, name='aspp_0'))
(self.feed('encoder1_8')
.dilate_conv(3, 5, 256, 1, 1, 4, name='aspp_1'))
(self.feed('encoder1_8')
.dilate_conv(3, 5, 256, 1, 1, 8, name='aspp_2'))
(self.feed('encoder1_8')
.dilate_conv(3, 5, 256, 1, 1, 16, name='aspp_3'))
(self.feed('encoder1_8')
.global_pool(name='aspp_4'))
(self.feed('aspp_1', 'aspp_2', 'aspp_3', 'aspp_4')
.concat(3, name='aspp_concat')
.conv(1, 1, 256, 1, 1, name='aspp_1x1'))
# combine low level features
(self.feed('encoder1_1', 'aspp_1x1')
.concat(3, name='concat1')
.conv(3, 5, 64, 1, 1, name='decoder1_1'))
# classification
(self.feed('decoder1_1')
.conv(1, 1, self.num_classes, 1, 1, activation=self.activation, name='cls_logits') # sigmoid for ghm
.softmax(name='softmax'))