Removed all nasnet refs

models/custom_model.py

@@ -3,6 +3,7 @@
 import tensorflow as tf
 import tensorflow.contrib.slim as slim
 import numpy as np
+import frontend_builder
 
 def conv_block(inputs, n_filters, filter_size=[3, 3], dropout_p=0.0):
 	"""
@@ -16,95 +17,36 @@ def conv_block(inputs, n_filters, filter_size=[3, 3], dropout_p=0.0):
 	  out = slim.dropout(out, keep_prob=(1.0-dropout_p))
 	return out
 
-def conv_transpose_block(inputs, n_filters, filter_size=[3, 3], dropout_p=0.0):
+def conv_transpose_block(inputs, n_filters, strides=2, filter_size=[3, 3], dropout_p=0.0):
 	"""
 	Basic conv transpose block for Encoder-Decoder upsampling
 	Apply successivly Transposed Convolution, BatchNormalization, ReLU nonlinearity
 	Dropout (if dropout_p > 0) on the inputs
 	"""
-	conv = slim.conv2d_transpose(inputs, n_filters, kernel_size=[3, 3], stride=[2, 2])
+	conv = slim.conv2d_transpose(inputs, n_filters, kernel_size=[3, 3], stride=[strides, strides])
 	out = tf.nn.relu(slim.batch_norm(conv, fused=True))
 	if dropout_p != 0.0:
 	  out = slim.dropout(out, keep_prob=(1.0-dropout_p))
 	return out
 
-def build_encoder_decoder_skip(inputs, num_classes, dropout_p=0.5, scope=None):
-	"""
-	Builds the Encoder-Decoder-Skip model. Inspired by SegNet with some modifications
-	Includes skip connections
-
-	Arguments:
-	  inputs: the input tensor
-	  n_classes: number of classes
-	  dropout_p: dropout rate applied after each convolution (0. for not using)
-
-	Returns:
-	  Encoder-Decoder model
-	"""
-
-	#####################
-	# Downsampling path #
-	#####################
-	net = conv_block(inputs, 64)
-	net = conv_block(net, 64)
-	net = slim.pool(net, [2, 2], stride=[2, 2], pooling_type='MAX')
-	skip_1 = net
-
-	net = conv_block(net, 128)
-	net = conv_block(net, 128)
-	net = slim.pool(net, [2, 2], stride=[2, 2], pooling_type='MAX')
-	skip_2 = net
-
-	net = conv_block(net, 256)
-	net = conv_block(net, 256)
-	net = conv_block(net, 256)
-	net = slim.pool(net, [2, 2], stride=[2, 2], pooling_type='MAX')
-	skip_3 = net
-
-	net = conv_block(net, 512)
-	net = conv_block(net, 512)
-	net = conv_block(net, 512)
-	net = slim.pool(net, [2, 2], stride=[2, 2], pooling_type='MAX')
-	skip_4 = net
-
-	net = conv_block(net, 512)
-	net = conv_block(net, 512)
-	net = conv_block(net, 512)
-	net = slim.pool(net, [2, 2], stride=[2, 2], pooling_type='MAX')
+def build_custom(inputs, num_classes, frontend="ResNet101", weight_decay=1e-5, is_training=True, pretrained_dir="models"):
+
 
+	logits, end_points, frontend_scope, init_fn  = frontend_builder.build_frontend(inputs, frontend, is_training=is_training)
 
-	#####################
-	# Upsampling path #
-	#####################
-	net = conv_transpose_block(net, 512)
-	net = conv_block(net, 512)
-	net = conv_block(net, 512)
-	net = conv_block(net, 512)
-	net = tf.add(net, skip_4)
+	up_1 = conv_transpose_block(end_points["pool2"], strides=4, n_filters=64)
+	up_2 = conv_transpose_block(end_points["pool3"], strides=8, n_filters=64)
+	up_3 = conv_transpose_block(end_points["pool4"], strides=16, n_filters=64)
+	up_4 = conv_transpose_block(end_points["pool5"], strides=32, n_filters=64)
 
-	net = conv_transpose_block(net, 512)
-	net = conv_block(net, 512)
-	net = conv_block(net, 512)
-	net = conv_block(net, 256)
-	net = tf.add(net, skip_3)
+	features = tf.concat([up_1, up_2, up_3, up_4], axis=-1)
 
-	net = conv_transpose_block(net, 256)
-	net = conv_block(net, 256)
-	net = conv_block(net, 256)
-	net = conv_block(net, 128)
-	net = tf.add(net, skip_2)
+	features = conv_block(inputs=features, n_filters=256, filter_size=[1, 1])
 
-	net = conv_transpose_block(net, 128)
-	net = conv_block(net, 128)
-	net = conv_block(net, 64)
-	net = tf.add(net, skip_1)
+	features = conv_block(inputs=features, n_filters=64, filter_size=[3, 3])
+	features = conv_block(inputs=features, n_filters=64, filter_size=[3, 3])
+	features = conv_block(inputs=features, n_filters=64, filter_size=[3, 3])
 
-	net = conv_transpose_block(net, 64)
-	net = conv_block(net, 64)
-	net = conv_block(net, 64)
 
-	#####################
-	#      Softmax      #
-	#####################
-	net = slim.conv2d(net, num_classes, [1, 1], scope='logits')
+	net = slim.conv2d(features, num_classes, [1, 1], scope='logits')
 	return net

models/frontend_builder.py

@@ -3,7 +3,6 @@
 import resnet_v2
 import mobilenet_v2
 import inception_v4
-import nasnet
 import os 
 
 

models/resnet_v2.py

@@ -199,6 +199,7 @@ def resnet_v2(inputs,
                               activation_fn=None, normalizer_fn=None):
             net = resnet_utils.conv2d_same(net, 64, 7, stride=2, scope='conv1')
           net = slim.max_pool2d(net, [3, 3], stride=2, scope='pool1')
+
           net = slim.utils.collect_named_outputs(end_points_collection, 'pool2', net)
         net = resnet_utils.stack_blocks_dense(net, blocks, output_stride)
         # This is needed because the pre-activation variant does not have batch