Commit 9d890972 authored by Cresson Remi's avatar Cresson Remi

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

parent 22981466
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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