Commit 02d85099 authored by Cresson Remi's avatar Cresson Remi

Merge branch 'develop'

parents e7c66cf3 9d890972
......@@ -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.
*
......
......@@ -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);
......
......@@ -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.
......
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])
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])
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])
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