diff --git a/include/otbTensorflowMultisourceModelBase.h b/include/otbTensorflowMultisourceModelBase.h
index 9c430e68a719c91d56774047db95d689a2b98536..dc025bcb66c48ddafa9fed8ed486c53762ddebe7 100644
--- a/include/otbTensorflowMultisourceModelBase.h
+++ b/include/otbTensorflowMultisourceModelBase.h
@@ -50,14 +50,14 @@ namespace otb
* Target nodes names of the TensorFlow graph that must be triggered can be set
* with the SetTargetNodesNames.
*
- * The OutputTensorNames consists in a strd::vector of std::string, and
+ * The OutputTensorNames consists in a std::vector of std::string, and
* corresponds to the names of tensors that will be computed during the session.
* As for input placeholders, output tensors field of expression
* (OutputExpressionFields, a std::vector of SizeType), i.e. the output
* space that the TensorFlow model will "generate", must be provided.
*
* Finally, a list of scalar placeholders can be fed in the form of std::vector
- * of std::string, each one expressing the assigment of a signle valued
+ * of std::string, each one expressing the assignment of a single valued
* placeholder, e.g. "drop_rate=0.5 learning_rate=0.002 toto=true".
* See otb::tf::ExpressionToTensor() to know more about syntax.
*
diff --git a/include/otbTensorflowMultisourceModelFilter.hxx b/include/otbTensorflowMultisourceModelFilter.hxx
index 77902bbdf6c045fe8fa2a9f2136919dd781ff54c..dbed34f753eca958289e41313c9cf34681773981 100644
--- a/include/otbTensorflowMultisourceModelFilter.hxx
+++ b/include/otbTensorflowMultisourceModelFilter.hxx
@@ -57,10 +57,9 @@ TensorflowMultisourceModelFilter<TInputImage, TOutputImage>
for(unsigned int dim = 0; dim<OutputImageType::ImageDimension; ++dim)
{
const SizeValueType psz = patchSize[dim];
- const SizeValueType rval = 0.5 * psz;
- const SizeValueType lval = psz - rval;
+ const SizeValueType lval = 0.5 * psz;
region.GetModifiableIndex()[dim] += lval;
- region.GetModifiableSize()[dim] -= psz;
+ region.GetModifiableSize()[dim] -= psz - 1;
}
}
@@ -327,8 +326,19 @@ TensorflowMultisourceModelFilter<TInputImage, TOutputImage>
// Compute the FOV-scale*FOE radius to pad
SizeType toPad(this->GetInputReceptiveFields().at(i));
- toPad[0] -= 1 + (this->GetOutputExpressionFields().at(0)[0] - 1) * m_OutputSpacingScale;
- toPad[1] -= 1 + (this->GetOutputExpressionFields().at(0)[1] - 1) * m_OutputSpacingScale;
+ for(unsigned int dim = 0; dim<ImageType::ImageDimension; ++dim)
+ {
+ int valToPad = 1 + (this->GetOutputExpressionFields().at(0)[dim] - 1) * m_OutputSpacingScale * this->GetInput(0)->GetSpacing()[dim] / this->GetInput(i)->GetSpacing()[dim] ;
+ if (valToPad > toPad[dim])
+ itkExceptionMacro("The input requested region of source #" << i << " is not consistent (dim "<< dim<< ")." <<
+ "Please check RF, EF, SF vs physical spacing of your image!" <<
+ "\nReceptive field: " << this->GetInputReceptiveFields().at(i)[dim] <<
+ "\nExpression field: " << this->GetOutputExpressionFields().at(0)[dim] <<
+ "\nScale factor: " << m_OutputSpacingScale <<
+ "\nReference image spacing: " << this->GetInput(0)->GetSpacing()[dim] <<
+ "\nImage " << i << " spacing: " << this->GetInput(i)->GetSpacing()[dim]);
+ toPad[dim] -= valToPad;
+ }
// Pad with radius
SmartPad(inRegion, toPad);
diff --git a/include/otbTensorflowMultisourceModelLearningBase.h b/include/otbTensorflowMultisourceModelLearningBase.h
index 930b8366083355a715cc9910b3068384867a2f88..f5ada7f25c2a8a1859818887e8cc0f7463924492 100644
--- a/include/otbTensorflowMultisourceModelLearningBase.h
+++ b/include/otbTensorflowMultisourceModelLearningBase.h
@@ -23,7 +23,15 @@ namespace otb
/**
* \class TensorflowMultisourceModelLearningBase
- * \brief This filter is the base class for all learning filters.
+ * \brief This filter is the base class for all filters that input patches images.
+ *
+ * One input patches image consist in an image of size (pszx, pszy*n, nbands) where:
+ * -pszx : is the width of one patch
+ * -pszy : is the height of one patch
+ * -n : is the number of patches in the patches image
+ * -nbands : is the number of channels in the patches image
+ *
+ * This filter verify that every patches images are consistent.
*
* The batch size can be set using the SetBatchSize() method.
* The streaming can be activated to allow the processing of huge datasets.
diff --git a/python/create_savedmodel_pxs_fcn.py b/python/create_savedmodel_pxs_fcn.py
new file mode 100755
index 0000000000000000000000000000000000000000..bb57c18eaface0435bd56237bd4af72246d1df0d
--- /dev/null
+++ b/python/create_savedmodel_pxs_fcn.py
@@ -0,0 +1,74 @@
+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])
diff --git a/python/create_savedmodel_simple_cnn.py b/python/create_savedmodel_simple_cnn.py
new file mode 100755
index 0000000000000000000000000000000000000000..2cd79f1884de4028f050c6e0ac2a657446f0ed38
--- /dev/null
+++ b/python/create_savedmodel_simple_cnn.py
@@ -0,0 +1,59 @@
+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])
diff --git a/python/create_savedmodel_simple_fcn.py b/python/create_savedmodel_simple_fcn.py
new file mode 100755
index 0000000000000000000000000000000000000000..53f38502ff0acaa2cd396ad174bb1ed83f334992
--- /dev/null
+++ b/python/create_savedmodel_simple_fcn.py
@@ -0,0 +1,59 @@
+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])