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Commit 0033b73a authored by Cresson Remi's avatar Cresson Remi
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REFAC: use argparse to generate the SavedModel + clean the code

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python/create_model_ienco-m3_patchbased.py
View file @ 0033b73a
......@@ -16,54 +16,30 @@
# limitations under the License.
#
#==========================================================================*/
import sys
import os
import numpy as np
import math
from operator import itemgetter, attrgetter, methodcaller
import tensorflow as tf
from tensorflow.contrib import rnn
import random
from sklearn.metrics import accuracy_score
from sklearn.metrics import precision_recall_fscore_support
from sklearn.metrics import f1_score
from sklearn.ensemble import RandomForestClassifier
from sklearn.utils import shuffle
from sklearn.metrics import confusion_matrix
from tricks import *
def checkTest(ts_data, vhsr_data, batchsz, label_test):
tot_pred = []
# gt_test = []
iterations = ts_data.shape[0] / batchsz
if ts_data.shape[0] % batchsz != 0:
iterations+=1
for ibatch in range(iterations):
batch_rnn_x, _ = getBatch(ts_data, label_test, ibatch, batchsz)
batch_cnn_x, batch_y = getBatch(vhsr_data, label_test, ibatch, batchsz)
pred_temp = sess.run(testPrediction,feed_dict={x_rnn:batch_rnn_x,
is_training_ph:True,
dropout:0.0,
x_cnn:batch_cnn_x})
for el in pred_temp:
tot_pred.append( el )
# Reference:
#
# Benedetti, P., Ienco, D., Gaetano, R., Ose, K., Pensa, R. G., & Dupuy, S. (2018)
# M3Fusion: A Deep Learning Architecture for Multiscale Multimodal Multitemporal
# Satellite Data Fusion. IEEE Journal of Selected Topics in Applied Earth
# Observations and Remote Sensing, 11(12), 4939-4949.
print_histo(np.asarray(tot_pred),"prediction distrib")
print_histo(label_test,"test distrib")
from tricks import *
from tensorflow.contrib import rnn
import argparse
# flatten the classes_test
label_test = flatten_nparray(label_test)
parser = argparse.ArgumentParser()
parser.add_argument("--nunits", type=int, default=1024, help="number of units")
parser.add_argument("--n_levels_lstm", type=int, default=1, help="number of lstm levels")
parser.add_argument("--hm_epochs", type=int, default=400, help="hm epochs")
parser.add_argument("--n_timestamps", type=int, default=37, help="number of images in timeseries")
parser.add_argument("--n_dims", type=int, default=16, help="number of channels in timeseries images")
parser.add_argument("--patch_window", type=int, default=25, help="patch size for the high-res image")
parser.add_argument("--n_channels", type=int, default=4, help="number of channels in the high-res image")
parser.add_argument("--nclasses", type=int, default=8, help="number of classes")
parser.add_argument("--outdir", help="Output directory for SavedModel", required=True)
params = parser.parse_args()
print "TEST F-Measure: %f" % f1_score(label_test, tot_pred, average='weighted')
print f1_score(label_test, tot_pred, average=None)
print "TEST Accuracy: %f" % accuracy_score(label_test, tot_pred)
sys.stdout.flush()
return accuracy_score(label_test, tot_pred)
def RnnAttention(x, nunits, nlayer, n_dims, n_timetamps, is_training_ph):
......@@ -169,13 +145,6 @@ def CNN(x, nunits):
tensor_shape = cnn.get_shape()
return cnn, tensor_shape[1].value
def getBatch(X, Y, i, batch_size):
start_id = i*batch_size
end_id = min( (i+1) * batch_size, X.shape[0])
batch_x = X[start_id:end_id]
batch_y = Y[start_id:end_id]
return batch_x, batch_y
def getPrediction(x_rnn, x_cnn, nunits, nlayer, nclasses, dropout, is_training, n_dims, n_timetamps):
features_learnt = None
......@@ -187,22 +156,16 @@ def getPrediction(x_rnn, x_cnn, nunits, nlayer, nclasses, dropout, is_training,
first_dim = cnn_dim + nunits
#Classifier1 #RNN Branch
print "RNN Features:"
print vec_rnn.get_shape()
outb1 = tf.Variable(tf.truncated_normal([nclasses]),name='B1')
outw1 = tf.Variable(tf.truncated_normal([nunits,nclasses]),name='W1')
pred_c1 = tf.matmul(vec_rnn,outw1)+outb1
#Classifier2 #CNN Branch
print "CNN Features:"
print vec_cnn.get_shape()
outb2 = tf.Variable(tf.truncated_normal([nclasses]),name='B2')
outw2 = tf.Variable(tf.truncated_normal([cnn_dim,nclasses]),name='W2')
pred_c2 = tf.matmul(vec_cnn,outw2)+outb2
#ClassifierFull
print "FULL features_learnt:"
print features_learnt.get_shape()
outb = tf.Variable(tf.truncated_normal([nclasses]),name='B')
outw = tf.Variable(tf.truncated_normal([first_dim,nclasses]),name='W')
pred_full = tf.matmul(features_learnt,outw)+outb
......@@ -211,112 +174,49 @@ def getPrediction(x_rnn, x_cnn, nunits, nlayer, nclasses, dropout, is_training,
###############################################################################
#Model parameters
nunits = 1024
batchsz = 64
hm_epochs = 400
n_levels_lstm = 1
#dropout = 0.2
#Data INformation
n_timestamps = 37
n_dims = 16
patch_window = 25
n_channels = 4
nclasses = 8
# check number of arguments
if len(sys.argv) != 8:
print("Usage : <ts_train> <vhs_train> <label_train> <ts_valid> <vhs_valid> <label_valid> <export_dir>")
sys.exit(1)
ts_train = read_samples(sys.argv[1])
vhsr_train = read_samples(sys.argv[2])
label_train = read_samples(sys.argv[3])
label_train = np.int32(label_train)
print_histo(label_train, "label_train")
ts_test = read_samples(sys.argv[4])
vhsr_test = read_samples(sys.argv[5])
label_test = read_samples(sys.argv[6])
label_test = np.int32(label_test)
print_histo(label_test, "label_test")
export_dir = read_samples(sys.argv[7])
x_rnn = tf.placeholder(tf.float32,[None, 1, 1, n_dims*n_timestamps],name="x_rnn")
x_cnn = tf.placeholder(tf.float32,[None, patch_window, patch_window, n_channels],name="x_cnn")
y = tf.placeholder(tf.int32,[None, 1, 1, 1],name="y")
learning_rate = tf.placeholder(tf.float32, shape=(), name="learning_rate")
is_training_ph = tf.placeholder(tf.bool, shape=(), name="is_training")
dropout = tf.placeholder(tf.float32, shape=(), name="drop_rate")
sess = tf.InteractiveSession()
""" Main """
pred_c1, pred_c2, pred_full, features_learnt = getPrediction(x_rnn,
x_cnn,
nunits,
n_levels_lstm,
nclasses,
dropout,
is_training_ph,
n_dims,
n_timestamps)
testPrediction = tf.argmax(pred_full, 1, name="prediction")
loss_full = tf.losses.sparse_softmax_cross_entropy(labels=tf.reshape(y, [-1, 1]), logits=tf.reshape(pred_full, [-1, nclasses]))
loss_c1 = tf.losses.sparse_softmax_cross_entropy(labels=tf.reshape(y, [-1, 1]), logits=tf.reshape(pred_c1, [-1, nclasses]))
loss_c2 = tf.losses.sparse_softmax_cross_entropy(labels=tf.reshape(y, [-1, 1]), logits=tf.reshape(pred_c2, [-1, nclasses]))
cost = loss_full + (0.3 * loss_c1) + (0.3 * loss_c2)
optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate, name="optimizer").minimize(cost)
correct = tf.equal(tf.argmax(pred_full,1),tf.argmax(y,1))
accuracy = tf.reduce_mean(tf.cast(correct,tf.float64))
tf.global_variables_initializer().run()
# Add ops to save and restore all the variables.
saver = tf.train.Saver()
iterations = ts_train.shape[0] / batchsz
if ts_train.shape[0] % batchsz != 0:
iterations+=1
best_loss = sys.float_info.max
for e in range(hm_epochs):
lossi = 0
accS = 0
ts_train, vhsr_train, label_train = shuffle(ts_train, vhsr_train, label_train, random_state=0)
print "shuffle DONE"
for ibatch in range(iterations):
#BATCH_X BATCH_Y: i-th batches of train_indices_x and train_y
batch_rnn_x, _ = getBatch(ts_train, label_train, ibatch, batchsz)
batch_cnn_x, batch_y = getBatch(vhsr_train, label_train, ibatch, batchsz)
acc,_,loss = sess.run([accuracy,optimizer,cost],feed_dict={x_rnn:batch_rnn_x,
x_cnn:batch_cnn_x,
y:batch_y,
is_training_ph:True,
dropout:0.2,
learning_rate:0.0002})
lossi+=loss
accS+=acc
# Create the TensorFlow graph
with tf.Graph().as_default():
print "Epoch:",e,"Train loss:",lossi/iterations,"| accuracy:",accS/iterations
c_loss = lossi/iterations
if c_loss < best_loss:
best_loss = c_loss
CreateSavedModel(sess, ["x_cnn:0","x_rnn:0","is_training:0"], ["prediction:0"], export_dir)
test_acc = checkTest(ts_test, vhsr_test, 1024, label_test)
\ No newline at end of file
x_rnn = tf.placeholder(tf.float32, [None, 1, 1, params.n_dims*params.n_timestamps], name="x_rnn")
x_cnn = tf.placeholder(tf.float32, [None, params.patch_window, params.patch_window, params.n_channels], name="x_cnn")
y = tf.placeholder(tf.int32, [None, 1, 1, 1], name="y")
learning_rate = tf.placeholder_with_default(tf.constant(0.0002, dtype=tf.float32, shape=[]), shape=[], name="learning_rate")
is_training_ph = tf.placeholder_with_default(tf.constant(False, dtype=tf.bool, shape=[]), shape=[], name="is_training")
dropout = tf.placeholder_with_default(tf.constant(0.5, dtype=tf.float32, shape=[]), shape=[], name="drop_rate")
pred_c1, pred_c2, pred_full, features_learnt = getPrediction(x_rnn,
x_cnn,
params.nunits,
params.n_levels_lstm,
params.nclasses,
dropout,
is_training_ph,
params.n_dims,
params.n_timestamps)
testPrediction = tf.argmax(pred_full, 1, name="prediction")
loss_full = tf.losses.sparse_softmax_cross_entropy(labels=tf.reshape(y, [-1, 1]),
logits=tf.reshape(pred_full, [-1, params.nclasses]))
loss_c1 = tf.losses.sparse_softmax_cross_entropy(labels=tf.reshape(y, [-1, 1]),
logits=tf.reshape(pred_c1, [-1, params.nclasses]))
loss_c2 = tf.losses.sparse_softmax_cross_entropy(labels=tf.reshape(y, [-1, 1]),
logits=tf.reshape(pred_c2, [-1, params.nclasses]))
cost = loss_full + (0.3 * loss_c1) + (0.3 * loss_c2)
optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate, name="optimizer").minimize(cost)
correct = tf.equal(tf.argmax(pred_full,1),tf.argmax(y,1))
accuracy = tf.reduce_mean(tf.cast(correct,tf.float64))
# Initializer, saver, session
init = tf.global_variables_initializer()
saver = tf.train.Saver( max_to_keep=20 )
sess = tf.Session()
sess.run(init)
CreateSavedModel(sess, ["x_cnn:0","x_rnn:0","y:0"], ["prediction:0"], params.outdir)
python/create_model_maggiori17_fullyconv.py
View file @ 0033b73a
......@@ -16,309 +16,109 @@
# limitations under the License.
#
#==========================================================================*/
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
# Imports
import numpy as np
import tensorflow as tf
import numpy as np
import random
import os
import shutil
import sys
import time
from sklearn.metrics import accuracy_score
from sklearn.metrics import precision_recall_fscore_support
from sklearn.metrics import f1_score
from sklearn.ensemble import RandomForestClassifier
from sklearn.utils import shuffle
from sklearn.metrics import confusion_matrix
# Reference:
#
# Maggiori, E., Tarabalka, Y., Charpiat, G., & Alliez, P. (2016).
# "Convolutional neural networks for large-scale remote-sensing image classification."
# IEEE Transactions on Geoscience and Remote Sensing, 55(2), 645-657.
from tricks import *
import argparse
tf.logging.set_verbosity(tf.logging.INFO)
def do_eval2(sess,
testPrediction,
xs_placeholder,
labels_placeholder,
istraining_placeholder,
ds_data_xs,
ds_labels,
batch_size):
"""Runs one evaluation against the full epoch of data.
"""
# total predictions
tot_pred = np.array([])
# And run one epoch of eval.
n_data = ds_data_xs.shape[0]
n_steps = int(n_data / batch_size)
for step in range(0, n_steps):
start_idx = batch_size * step
end_idx = start_idx + batch_size
if step == n_steps - 1:
end_idx = n_data
print(end_idx)
feed_dict = {
xs_placeholder: ds_data_xs[start_idx:end_idx,:],
labels_placeholder: ds_labels[start_idx:end_idx],
istraining_placeholder: False,
}
( predicted_y) = sess.run(testPrediction, feed_dict=feed_dict)
if tot_pred.size == 0:
tot_pred = predicted_y
else:
tot_pred = np.concatenate( (tot_pred, predicted_y), axis=0)
print(ds_labels.shape)
print(tot_pred.shape)
tot_pred = tot_pred.reshape((tot_pred.size))
ds_labels = ds_labels.reshape((ds_labels.size))
print(tot_pred.shape)
print("F-Measure: "+str(f1_score(ds_labels, tot_pred, average='weighted')))
print("F1 score: " + str(f1_score(ds_labels, tot_pred, average=None)))
print("Accuracy: "+str(accuracy_score(ds_labels, tot_pred)))
def main(unused_argv):
""" Main function
In this function we do:
1. Import a dataset
2. Build a model implementing a CNN
3. Perform training of the CNN
4. Export the model
"""
# Training params
n_epoch = 100
batch_size = 32
learning_rate = 0.0001
############################################################
# import a dataset
############################################################
parser = argparse.ArgumentParser()
parser.add_argument("--outdir", help="Output directory for SavedModel", required=True)
parser.add_argument("--n_channels", type=int, default=4, help="number of channels in the input image")
params = parser.parse_args()
# check number of arguments
if len(sys.argv) != 4:
print("Usage : <patches> <labels> <export_dir>")
sys.exit(1)
# Export dir
export_dir = sys.argv[3]
print("loading dataset")
# Create a dataset of size imp_size
imp_ds_patches = read_samples(sys.argv[1])
imp_ds_labels = read_samples(sys.argv[2])
# Shuffle the dataset
imp_ds_patches,imp_ds_labels = shuffle(imp_ds_patches,imp_ds_labels, random_state=0)
print("ok")
# Number of samples
if (imp_ds_patches.shape[0] != imp_ds_labels.shape[0]):
print("Number of samples should be the same as number of patches!")
sys.exit(1)
# Number of samples for training
n_data_train = int(imp_ds_patches.shape[0] / 2)
# Build the graph
with tf.Graph().as_default():
# Size of patches
nb_bands = 4
patch_size_xs = 80
patch_size_label = 16
# Divide the dataset in two subdatasets : training and validation
ds_data_train = imp_ds_patches[0:n_data_train,:]
ds_data_valid = imp_ds_patches[n_data_train:imp_ds_patches.shape[0],:]
ds_labels_train = imp_ds_labels[0:n_data_train,:]
ds_labels_valid = imp_ds_labels[n_data_train:imp_ds_patches.shape[0],:]
############################################################
# Build the graph
############################################################
with tf.Graph().as_default():
# placeholder for images and labels
istraining_placeholder = tf.placeholder(tf.bool, shape=(), name="istraining") # Used only for dropout...
xs_placeholder = tf.placeholder(tf.float32, shape=(None, patch_size_xs, patch_size_xs, nb_bands), name="x1")
labels_placeholder = tf.placeholder(tf.int32, shape=(None, patch_size_label, patch_size_label, 1), name="y1")
# placeholder for images and labels
istraining_placeholder = tf.placeholder_with_default(tf.constant(False , dtype=tf.bool, shape=[]), shape=[], name="is_training")
learning_rate = tf.placeholder_with_default(tf.constant(0.0002, dtype=tf.float32, shape=[]), shape=[], name="learning_rate")
xs_placeholder = tf.placeholder(tf.float32, shape=(None, patch_size_xs, patch_size_xs, params.n_channels), name="x")
labels_placeholder = tf.placeholder(tf.int32, shape=(None, patch_size_label, patch_size_label, 1), name="y")
print_tensor_info("xs_placeholder",xs_placeholder)
# Convolutional Layer #1
conv1 = tf.layers.conv2d(
inputs=xs_placeholder,
filters=64,
kernel_size=[12, 12],
padding="valid",
activation=tf.nn.crelu)
# Normalization of output of layer 1
norm1 = tf.layers.batch_normalization(conv1)
# pooling layer #1
pool1 = tf.layers.max_pooling2d(inputs=norm1, pool_size=[4, 4], strides=4)
# Convolutional Layer #2
conv2 = tf.layers.conv2d(
inputs=pool1,
filters=112,
kernel_size=[4, 4],
padding="valid",
activation=tf.nn.crelu)
# Normalization of output of layer 2
norm2 = tf.layers.batch_normalization(conv2)
# Convolutional Layer #1
conv1 = tf.layers.conv2d(
inputs=xs_placeholder,
filters=32,
kernel_size=[12, 12],
padding="valid",
activation=tf.nn.crelu)
print_tensor_info("conv1",conv1)
# Normalization of output of layer 1
norm1 = tf.layers.batch_normalization(conv1)
print_tensor_info("norm1",norm1)
# pooling layer #1
pool1 = tf.layers.max_pooling2d(inputs=norm1, pool_size=[4, 4], strides=4)
print_tensor_info("pool1",pool1)
# Convolutional Layer #2
conv2 = tf.layers.conv2d(
inputs=pool1,
filters=56,
kernel_size=[4, 4],
padding="valid",
activation=tf.nn.crelu)
print_tensor_info("conv2",conv2)
# Normalization of output of layer 2
norm2 = tf.layers.batch_normalization(conv2)
print_tensor_info("norm2",norm2)
# Convolutional Layer #3
conv3 = tf.layers.conv2d(
inputs=norm2,
filters=40,
kernel_size=[3, 3],
padding="valid",
activation=tf.nn.crelu)
print_tensor_info("conv3",conv3)
# Convolutional Layer #3
conv3 = tf.layers.conv2d(
inputs=norm2,
filters=80,
kernel_size=[3, 3],
padding="valid",
activation=tf.nn.crelu)
# Normalization of output of layer 3
norm3 = tf.layers.batch_normalization(conv3)
print_tensor_info("norm3",norm3)
# Normalization of output of layer 3
norm3 = tf.layers.batch_normalization(conv3)
# Convolutional Layer #4
conv4 = tf.layers.conv2d(
inputs=norm3,
filters=1,
kernel_size=[8, 8],
padding="valid",
activation=tf.nn.crelu)
print_tensor_info("conv4",conv4)
# Convolutional Layer #4
conv4 = tf.layers.conv2d(
inputs=norm3,
filters=1,
kernel_size=[8, 8],
padding="valid",
activation=tf.nn.crelu)
print_tensor_info("conv4",conv4)
# Deconv = conv on the padded/strided input, that is an (5+1)*4
deconv1 = tf.layers.conv2d_transpose(
inputs=conv4,
filters=1,
strides=(4,4),
kernel_size=[8, 8],
padding="valid",
activation=tf.nn.sigmoid)
print_tensor_info("deconv1",deconv1)
# Deconv = conv on the padded/strided input, that is an (5+1)*4
deconv1 = tf.layers.conv2d_transpose(
inputs=conv4,
filters=1,
strides=(4,4),
kernel_size=[8, 8],
padding="valid",
activation=tf.nn.sigmoid)
print_tensor_info("deconv1",deconv1)
numbatch = tf.shape(deconv1)[0]
szx = tf.shape(deconv1)[1]
szy = tf.shape(deconv1)[2]
estimated = tf.slice(deconv1, [0, 4, 4, 0], [numbatch, szx - 8, szy - 8, 1], "estimated")
print_tensor_info("estimated", estimated)
##################### Loss calculation ###################
estimated_resized = tf.reshape(estimated, [-1, patch_size_label*patch_size_label])
labels_resized = tf.reshape(labels_placeholder, [-1, patch_size_label*patch_size_label])
labels_resized = tf.cast(labels_resized, tf.float32)
testPrediction = tf.to_int32(estimated > 0.5)
loss = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(labels=labels_resized, logits=estimated_resized))
###################### Gradient descent ##################
train_op = tf.train.AdamOptimizer(learning_rate, name="optimizer").minimize(loss)
############### Variable initializer Op ##################
init = tf.global_variables_initializer()
######################### Saver ##########################
saver = tf.train.Saver()
#################### Create a session ####################
sess = tf.Session()
sess.run(init)
############## Here we start the training ################
for curr_epoch in range(n_epoch):
print("Epoch #" + str(curr_epoch))
ds_data_train,ds_labels_train = shuffle(ds_data_train,ds_labels_train, random_state=0)
# Start the training loop.
n_steps = int(n_data_train / batch_size)
for step in range(n_steps):