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evaluation.py
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evaluation.py
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import tensorflow as tf
import numpy as np
import config
FLAGS = tf.app.flags.FLAGS
def simple_loss_calc(logits, labels):
"""
logits: tensor, float - [batch_size, width, height, num_classes].
labels: tensor, int32 - [batch_size, width, height, num_classes].
"""
labels = tf.cast(labels, tf.int32)
cross_entropy = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(
logits=logits, labels=labels))
tf.summary.scalar('loss', cross_entropy)
return cross_entropy
def ram_loss(logits, labels):
# binary_label = tf_binarize_targets(labels)
encoded_label = tf.one_hot(labels, FLAGS.num_class, axis=-1)
print('Labels', labels.shape)
print('Logits', logits.shape)
print('encoded_label', encoded_label.shape)
sfm_logits = tf.nn.softmax(logits)
print('Sfotmax', sfm_logits.shape)
predictions = tf.argmax(sfm_logits, 3)
print('predictions', predictions.shape)
# accuracy = tf.reduce_mean(tf.cast(tf.equal(predictions, tf.argmax(encoded_label, 1)),
# tf.float32))
flat_logits = tf.reshape(sfm_logits, [-1, FLAGS.num_class])
print('flat_logits', flat_logits.shape)
flat_labels = tf.reshape(encoded_label, [-1, FLAGS.num_class])
print('flat_labels', flat_labels.shape)
loss_map = tf.nn.softmax_cross_entropy_with_logits(logits=flat_logits,
labels=flat_labels)
cross_entropy_loss = tf.reduce_mean(loss_map)
return cross_entropy_loss
def loss_calc(logits, labels):
"""
logits: tensor, float - [batch_size, width, height, num_classes].
labels: tensor, int32 - [batch_size, width, height, num_classes].
"""
# # construct one-hot label array
# # print('Type of Labels : ', type(labels))
# # labels = np.asarray(labels, dtype=np.int32)
# labels = tf.cast(labels, tf.int32)
# # print('Type of Labels : ', type(labels))
# label_flat = tf.reshape(labels, (-1, 1))
# labels = tf.reshape(tf.one_hot(label_flat, depth=FLAGS.num_class), (-1, FLAGS.num_class))
# # This motif is needed to hook up the batch_norm updates to the training
# update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)
# with tf.control_dependencies(update_ops):
# cross_entropy = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=logits, labels=labels))
# # cross_entropy = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=logits, labels=labels))
# tf.summary.scalar('loss', cross_entropy)
# =================
# cross_entropy = tf.nn.sparse_softmax_cross_entropy_with_logits(logits=logits,
# labels=labels,
# name='Cross_Entropy')
# cross_entropy_mean = tf.reduce_mean(cross_entropy, name='xentropy_mean')
labels = tf.cast(labels, tf.int32)
cross_entropy = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(
logits=logits, labels=labels))
# tf.add_to_collection('losses', cross_entropy)
# tf.summary.scalar('loss', cross_entropy)
# loss = tf.add_n(tf.get_collection('losses'), name='total_loss')
return cross_entropy
def weighted_loss_calc(logits, labels):
class_weights = np.array([
FLAGS.balance_weight_0, # "Not building"
FLAGS.balance_weight_1 # "Building"
])
# cross_entropy = tf.nn.weighted_cross_entropy_with_logits(logits=logits, labels=labels, pos_weight=class_weights)
cross_entropy = tf.nn.weighted_cross_entropy_with_logits(logits, labels, class_weights)
loss = tf.reduce_mean(cross_entropy)
tf.summary.scalar('loss', loss)
return loss
def evaluation(logits, labels):
labels = tf.to_int64(labels)
correct_prediction = tf.equal(tf.argmax(logits, 3), labels)
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
tf.summary.scalar('accuracy', accuracy)
return accuracy
def per_class_acc(predictions, label_tensor):
labels = label_tensor
num_class = FLAGS.num_class
size = predictions.shape[0]
hist = np.zeros((num_class, num_class))
for i in range(size):
hist += fast_hist(labels[i].flatten(), predictions[i].argmax(2).flatten(), num_class)
acc_total = np.diag(hist).sum() / hist.sum()
print ('accuracy = %f' % np.nanmean(acc_total))
iu = np.diag(hist) / (hist.sum(1) + hist.sum(0) - np.diag(hist))
print ('mean IU = %f' % np.nanmean(iu))
for ii in range(num_class):
if float(hist.sum(1)[ii]) == 0:
acc = 0.0
else:
acc = np.diag(hist)[ii] / float(hist.sum(1)[ii])
print(" class # %d accuracy = %f " % (ii, acc))
def fast_hist(a, b, n):
k = (a >= 0) & (a < n)
return np.bincount(n * a[k].astype(int) + b[k], minlength=n**2).reshape(n, n)
def get_hist(predictions, labels):
num_class = predictions.shape[3] # becomes 2 for aerial - correct
batch_size = predictions.shape[0]
hist = np.zeros((num_class, num_class))
for i in range(batch_size):
hist += fast_hist(labels[i].flatten(), predictions[i].argmax(2).flatten(), num_class)
return hist
def print_hist_summery(hist):
acc_total = np.diag(hist).sum() / hist.sum()
print ('accuracy = %f' % np.nanmean(acc_total))
iu = np.diag(hist) / (hist.sum(1) + hist.sum(0) - np.diag(hist))
print ('mean IU = %f' % np.nanmean(iu))
for ii in range(hist.shape[0]):
if float(hist.sum(1)[ii]) == 0:
acc = 0.0
else:
acc = np.diag(hist)[ii] / float(hist.sum(1)[ii])
print(" class # %d accuracy = %f " % (ii, acc))