with tf.name_scope('input'):
x = tf.placeholder(dtype=tf.float32,
shape=[
FLAGS.batch_size, FLAGS.crop_height,
FLAGS.crop_width, FLAGS.channels
],
name='x_input')
y = tf.placeholder(
dtype=tf.int32,
shape=[FLAGS.batch_size, FLAGS.crop_height, FLAGS.crop_width],
name='ground_truth')
auxi_logits, logits = pspnet.PSPNet(
x,
is_training=True,
output_stride=FLAGS.output_stride,
pre_trained_model=FLAGS.pretrained_model_path,
classes=FLAGS.classes)
with tf.name_scope('regularization'):
train_var_list = [
v for v in tf.trainable_variables()
if 'beta' not in v.name and 'gamma' not in v.name
]
# Add weight decay to the loss.
with tf.variable_scope("total_loss"):
l2_loss = FLAGS.weight_decay * tf.add_n(
[tf.nn.l2_loss(v) for v in train_var_list])
with tf.name_scope('loss'):
#reshaped_logits = tf.reshape(logits, [BATCH_SIZE, -1])
crop_width=FLAGS.crop_width,
classes=FLAGS.classes,
ignore_label=FLAGS.ignore_label,
scales=FLAGS.scales)
with tf.name_scope("input"):
x = tf.placeholder(tf.float32,
[FLAGS.batch_size, FLAGS.height, FLAGS.width, 3],
name='x_input')
y = tf.placeholder(tf.int32, [FLAGS.batch_size, FLAGS.height, FLAGS.width],
name='ground_truth')
_, logits = PSPNet.PSPNet(x,
is_training=False,
output_stride=FLAGS.output_stride,
pre_trained_model=FLAGS.pretrained_model_path,
classes=FLAGS.classes)
with tf.name_scope('prediction_and_miou'):
prediction = tf.argmax(logits, axis=-1, name='predictions')
def get_val_predictions():
with tf.Session() as sess:
sess.run(tf.local_variables_initializer())
sess.run(tf.global_variables_initializer())
saver = tf.train.Saver()
for i in range(height):
for j in range(width):
return_img[i, j, :] = cmap[image[i, j]]
return return_img
image_batch_0, image_batch, anno_batch, filename = input_data.read_batch(BATCH_SIZE, type=prediction_on)
with tf.name_scope("input"):
x = tf.placeholder(tf.float32, [BATCH_SIZE, HEIGHT, WIDTH, 3], name='x_input')
y = tf.placeholder(tf.int32, [BATCH_SIZE, HEIGHT, WIDTH], name='ground_truth')
_, logits = PSPNet.PSPNet(x, is_training=False, output_stride=8, pre_trained_model=PRETRAINED_MODEL_PATH)
with tf.name_scope('prediction_and_miou'):
prediction = tf.argmax(logits, axis=-1, name='predictions')
with tf.Session() as sess:
sess.run(tf.local_variables_initializer())
sess.run(tf.global_variables_initializer())
saver = tf.train.Saver()
#saver.restore(sess, './checkpoint/pspnet.model-2000')