ADD: new models: 1 simple CNN, 1 simple FCN, gaetano and ienco's 2-branch CNN

python/create_savedmodel_pxs_fcn.py

+from tricks import *
+import sys
+import os
+
+nclasses=8
+
+def myModel(x1,x2):
+  
+  # The XS branch (input patches: 8x8x4)
+  conv1_x1 = tf.layers.conv2d(inputs=x1, filters=16, kernel_size=[5,5], padding="valid",
+                              activation=tf.nn.relu) # out size: 4x4x16
+  conv2_x1 = tf.layers.conv2d(inputs=conv1_x1, filters=32, kernel_size=[3,3], padding="valid",
+                              activation=tf.nn.relu) # out size: 2x2x32
+  conv3_x1 = tf.layers.conv2d(inputs=conv2_x1, filters=64, kernel_size=[2,2], padding="valid",
+                              activation=tf.nn.relu) # out size: 1x1x64
+  
+  # The PAN branch (input patches: 32x32x1)
+  conv1_x2 = tf.layers.conv2d(inputs=x2, filters=16, kernel_size=[5,5], padding="valid",
+                              activation=tf.nn.relu) # out size: 28x28x16
+  pool1_x2 = tf.layers.max_pooling2d(inputs=conv1_x2, pool_size=[2, 2], 
+                              strides=2) # out size: 14x14x16
+  conv2_x2 = tf.layers.conv2d(inputs=pool1_x2, filters=32, kernel_size=[5,5], padding="valid",
+                              activation=tf.nn.relu) # out size: 10x10x32
+  pool2_x2 = tf.layers.max_pooling2d(inputs=conv2_x2, pool_size=[2, 2],
+                              strides=2) # out size: 5x5x32
+  conv3_x2 = tf.layers.conv2d(inputs=pool2_x2, filters=64, kernel_size=[3,3], padding="valid",
+                              activation=tf.nn.relu) # out size: 3x3x64
+  conv4_x2 = tf.layers.conv2d(inputs=conv3_x2, filters=64, kernel_size=[3,3], padding="valid",
+                              activation=tf.nn.relu) # out size: 1x1x64
+  
+  # Stack features
+  features = tf.reshape(tf.stack([conv3_x1, conv4_x2], axis=3), 
+                        shape=[-1, 128], name="features")
+  
+  # 8 neurons for 8 classes
+  estimated = tf.layers.dense(inputs=features, units=nclasses, activation=None)
+  estimated_label = tf.argmax(estimated, 1, name="prediction")
+  
+  return estimated, estimated_label
+ 
+""" Main """
+# check number of arguments
+if len(sys.argv) != 2:
+  print("Usage : <output directory for SavedModel>")
+  sys.exit(1)
+
+# Create the graph
+with tf.Graph().as_default():
+  
+  # Placeholders
+  x1 = tf.placeholder(tf.float32, [None, None, None, 4], name="x1")
+  x2 = tf.placeholder(tf.float32, [None, None, None, 1], name="x2")
+  y  = tf.placeholder(tf.int32  , [None, None, None, 1], name="y")
+  lr = tf.placeholder_with_default(tf.constant(0.0002, dtype=tf.float32, shape=[]), 
+                                   shape=[], name="lr")
+  
+  # Output
+  y_estimated, y_label = myModel(x1,x2)
+  
+  # Loss function
+  cost = tf.losses.sparse_softmax_cross_entropy(labels=tf.reshape(y, [-1, 1]), 
+                                                logits=tf.reshape(y_estimated, [-1, nclasses]))
+  
+  # Optimizer
+  optimizer = tf.train.AdamOptimizer(learning_rate=lr, name="optimizer").minimize(cost)
+  
+  # Initializer, saver, session
+  init = tf.global_variables_initializer()
+  saver = tf.train.Saver( max_to_keep=20 )
+  sess = tf.Session()
+  sess.run(init)
+
+  # Create a SavedModel
+  CreateSavedModel(sess, ["x1:0", "x2:0", "y:0"], ["features:0", "prediction:0"], sys.argv[1])

python/create_savedmodel_simple_cnn.py

+from tricks import *
+import sys
+import os
+
+nclasses=8
+
+def myModel(x):
+  
+  # input patches: 16x16x4
+  conv1 = tf.layers.conv2d(inputs=x, filters=16, kernel_size=[5,5], padding="valid", 
+                           activation=tf.nn.relu) # out size: 12x12x16
+  pool1 = tf.layers.max_pooling2d(inputs=conv1, pool_size=[2, 2], strides=2) # out: 6x6x16
+  conv2 = tf.layers.conv2d(inputs=pool1, filters=16, kernel_size=[3,3], padding="valid", 
+                           activation=tf.nn.relu) # out size: 4x4x16
+  pool2 = tf.layers.max_pooling2d(inputs=conv2, pool_size=[2, 2], strides=2) # out: 2x2x16
+  conv3 = tf.layers.conv2d(inputs=pool2, filters=32, kernel_size=[2,2], padding="valid",
+                           activation=tf.nn.relu) # out size: 1x1x32
+  
+  # Features
+  features = tf.reshape(conv3, shape=[-1, 32], name="features")
+  
+  # 8 neurons for 8 classes
+  estimated = tf.layers.dense(inputs=features, units=nclasses, activation=None)
+  estimated_label = tf.argmax(estimated, 1, name="prediction")
+
+  return estimated, estimated_label
+ 
+""" Main """
+if len(sys.argv) != 2:
+  print("Usage : <output directory for SavedModel>")
+  sys.exit(1)
+
+# Create the TensorFlow graph
+with tf.Graph().as_default():
+  
+  # Placeholders
+  x = tf.placeholder(tf.float32, [None, None, None, 4], name="x")
+  y = tf.placeholder(tf.int32  , [None, None, None, 1], name="y")
+  lr = tf.placeholder_with_default(tf.constant(0.0002, dtype=tf.float32, shape=[]),
+                                   shape=[], name="lr")
+  
+  # Output
+  y_estimated, y_label = myModel(x)
+  
+  # Loss function
+  cost = tf.losses.sparse_softmax_cross_entropy(labels=tf.reshape(y, [-1, 1]), 
+                                                logits=tf.reshape(y_estimated, [-1, nclasses]))
+  
+  # Optimizer
+  optimizer = tf.train.AdamOptimizer(learning_rate=lr, name="optimizer").minimize(cost)
+  
+  # Initializer, saver, session
+  init = tf.global_variables_initializer()
+  saver = tf.train.Saver( max_to_keep=20 )
+  sess = tf.Session()
+  sess.run(init)
+
+  # Create a SavedModel
+  CreateSavedModel(sess, ["x:0", "y:0"], ["features:0", "prediction:0"], sys.argv[1])

python/create_savedmodel_simple_fcn.py

+from tricks import *
+import sys
+import os
+
+nclasses=8
+
+def myModel(x):
+  
+  # input patches: 16x16x4
+  conv1 = tf.layers.conv2d(inputs=x, filters=16, kernel_size=[5,5], padding="valid", 
+                           activation=tf.nn.relu) # out size: 12x12x16
+  conv2 = tf.layers.conv2d(inputs=conv1, filters=16, kernel_size=[5,5], padding="valid", 
+                           activation=tf.nn.relu) # out size: 8x8x16
+  conv3 = tf.layers.conv2d(inputs=conv2, filters=32, kernel_size=[5,5], padding="valid",
+                           activation=tf.nn.relu) # out size: 4x4x32
+  conv4 = tf.layers.conv2d(inputs=conv3, filters=32, kernel_size=[4,4], padding="valid",
+                           activation=tf.nn.relu) # out size: 1x1x32
+  
+  # Features
+  features = tf.reshape(conv4, shape=[-1, 32], name="features")
+  
+  # 8 neurons for 8 classes
+  estimated = tf.layers.dense(inputs=features, units=nclasses, activation=None)
+  estimated_label = tf.argmax(estimated, 1, name="prediction")
+
+  return estimated, estimated_label
+
+""" Main """
+if len(sys.argv) != 2:
+  print("Usage : <output directory for SavedModel>")
+  sys.exit(1)
+
+# Create the TensorFlow graph
+with tf.Graph().as_default():
+  
+  # Placeholders
+  x = tf.placeholder(tf.float32, [None, None, None, 4], name="x")
+  y = tf.placeholder(tf.int32  , [None, None, None, 1], name="y")
+  lr = tf.placeholder_with_default(tf.constant(0.0002, dtype=tf.float32, shape=[]),
+                                   shape=[], name="lr")
+  
+  # Output
+  y_estimated, y_label = myModel(x)
+  
+  # Loss function
+  cost = tf.losses.sparse_softmax_cross_entropy(labels=tf.reshape(y, [-1, 1]), 
+                                                logits=tf.reshape(y_estimated, [-1, nclasses]))
+  
+  # Optimizer
+  optimizer = tf.train.AdamOptimizer(learning_rate=lr, name="optimizer").minimize(cost)
+  
+  # Initializer, saver, session
+  init = tf.global_variables_initializer()
+  saver = tf.train.Saver( max_to_keep=20 )
+  sess = tf.Session()
+  sess.run(init)
+
+  # Create a SavedModel
+  CreateSavedModel(sess, ["x:0", "y:0"], ["features:0", "prediction:0"], sys.argv[1])