def testModelVariables(self):
batch_size = 5
height, width = 224, 224
num_classes = 1000
with self.test_session():
inputs = random_ops.random_uniform((batch_size, height, width, 3))
vgg.vgg_19(inputs, num_classes)
expected_names = [
'vgg_19/conv1/conv1_1/weights',
'vgg_19/conv1/conv1_1/biases',
'vgg_19/conv1/conv1_2/weights',
'vgg_19/conv1/conv1_2/biases',
'vgg_19/conv2/conv2_1/weights',
'vgg_19/conv2/conv2_1/biases',
'vgg_19/conv2/conv2_2/weights',
'vgg_19/conv2/conv2_2/biases',
'vgg_19/conv3/conv3_1/weights',
'vgg_19/conv3/conv3_1/biases',
'vgg_19/conv3/conv3_2/weights',
'vgg_19/conv3/conv3_2/biases',
'vgg_19/conv3/conv3_3/weights',
'vgg_19/conv3/conv3_3/biases',
'vgg_19/conv3/conv3_4/weights',
'vgg_19/conv3/conv3_4/biases',
'vgg_19/conv4/conv4_1/weights',
'vgg_19/conv4/conv4_1/biases',
'vgg_19/conv4/conv4_2/weights',
'vgg_19/conv4/conv4_2/biases',
'vgg_19/conv4/conv4_3/weights',
'vgg_19/conv4/conv4_3/biases',
'vgg_19/conv4/conv4_4/weights',
'vgg_19/conv4/conv4_4/biases',
'vgg_19/conv5/conv5_1/weights',
'vgg_19/conv5/conv5_1/biases',
'vgg_19/conv5/conv5_2/weights',
'vgg_19/conv5/conv5_2/biases',
'vgg_19/conv5/conv5_3/weights',
'vgg_19/conv5/conv5_3/biases',
'vgg_19/conv5/conv5_4/weights',
'vgg_19/conv5/conv5_4/biases',
'vgg_19/fc6/weights',
'vgg_19/fc6/biases',
'vgg_19/fc7/weights',
'vgg_19/fc7/biases',
'vgg_19/fc8/weights',
'vgg_19/fc8/biases',
]
model_variables = [
v.op.name for v in variables_lib.get_model_variables()
]
self.assertSetEqual(set(model_variables), set(expected_names))
def testEndPoints(self):
batch_size = 5
height, width = 224, 224
num_classes = 1000
for is_training in [True, False]:
with ops.Graph().as_default():
inputs = random_ops.random_uniform(
(batch_size, height, width, 3))
_, end_points = vgg.vgg_19(inputs,
num_classes,
is_training=is_training)
expected_names = [
'vgg_19/conv1/conv1_1', 'vgg_19/conv1/conv1_2',
'vgg_19/pool1', 'vgg_19/conv2/conv2_1',
'vgg_19/conv2/conv2_2', 'vgg_19/pool2',
'vgg_19/conv3/conv3_1', 'vgg_19/conv3/conv3_2',
'vgg_19/conv3/conv3_3', 'vgg_19/conv3/conv3_4',
'vgg_19/pool3', 'vgg_19/conv4/conv4_1',
'vgg_19/conv4/conv4_2', 'vgg_19/conv4/conv4_3',
'vgg_19/conv4/conv4_4', 'vgg_19/pool4',
'vgg_19/conv5/conv5_1', 'vgg_19/conv5/conv5_2',
'vgg_19/conv5/conv5_3', 'vgg_19/conv5/conv5_4',
'vgg_19/pool5', 'vgg_19/fc6', 'vgg_19/fc7', 'vgg_19/fc8'
]
self.assertSetEqual(set(end_points.keys()),
set(expected_names))
def testForward(self):
batch_size = 1
height, width = 224, 224
with self.test_session() as sess:
inputs = random_ops.random_uniform((batch_size, height, width, 3))
logits, _ = vgg.vgg_19(inputs)
sess.run(variables.global_variables_initializer())
output = sess.run(logits)
self.assertTrue(output.any())
def testFullyConvolutional(self):
batch_size = 1
height, width = 256, 256
num_classes = 1000
with self.test_session():
inputs = random_ops.random_uniform((batch_size, height, width, 3))
logits, _ = vgg.vgg_19(inputs, num_classes, spatial_squeeze=False)
self.assertEquals(logits.op.name, 'vgg_19/fc8/BiasAdd')
self.assertListEqual(logits.get_shape().as_list(),
[batch_size, 2, 2, num_classes])
def testBuild(self):
batch_size = 5
height, width = 224, 224
num_classes = 1000
with self.test_session():
inputs = random_ops.random_uniform((batch_size, height, width, 3))
logits, _ = vgg.vgg_19(inputs, num_classes)
self.assertEquals(logits.op.name, 'vgg_19/fc8/squeezed')
self.assertListEqual(logits.get_shape().as_list(),
[batch_size, num_classes])
def testEvaluation(self):
batch_size = 2
height, width = 224, 224
num_classes = 1000
with self.test_session():
eval_inputs = tf.random_uniform((batch_size, height, width, 3))
logits, _ = vgg.vgg_19(eval_inputs, is_training=False)
self.assertListEqual(logits.get_shape().as_list(),
[batch_size, num_classes])
predictions = tf.argmax(logits, 1)
self.assertListEqual(predictions.get_shape().as_list(),
[batch_size])
def testTrainEvalWithReuse(self):
train_batch_size = 2
eval_batch_size = 1
train_height, train_width = 224, 224
eval_height, eval_width = 256, 256
num_classes = 1000
with self.test_session():
train_inputs = random_ops.random_uniform(
(train_batch_size, train_height, train_width, 3))
logits, _ = vgg.vgg_19(train_inputs)
self.assertListEqual(logits.get_shape().as_list(),
[train_batch_size, num_classes])
variable_scope.get_variable_scope().reuse_variables()
eval_inputs = random_ops.random_uniform(
(eval_batch_size, eval_height, eval_width, 3))
logits, _ = vgg.vgg_19(eval_inputs,
is_training=False,
spatial_squeeze=False)
self.assertListEqual(logits.get_shape().as_list(),
[eval_batch_size, 2, 2, num_classes])
logits = math_ops.reduce_mean(logits, [1, 2])
predictions = math_ops.argmax(logits, 1)
self.assertEquals(predictions.get_shape().as_list(),
[eval_batch_size])
def testEndPoints(self):
batch_size = 5
height, width = 224, 224
num_classes = 1000
with self.test_session():
inputs = tf.random_uniform((batch_size, height, width, 3))
_, end_points = vgg.vgg_19(inputs, num_classes)
expected_names = [
'vgg_19/conv1/conv1_1', 'vgg_19/conv1/conv1_2', 'vgg_19/pool1',
'vgg_19/conv2/conv2_1', 'vgg_19/conv2/conv2_2', 'vgg_19/pool2',
'vgg_19/conv3/conv3_1', 'vgg_19/conv3/conv3_2',
'vgg_19/conv3/conv3_3', 'vgg_19/conv3/conv3_4', 'vgg_19/pool3',
'vgg_19/conv4/conv4_1', 'vgg_19/conv4/conv4_2',
'vgg_19/conv4/conv4_3', 'vgg_19/conv4/conv4_4', 'vgg_19/pool4',
'vgg_19/conv5/conv5_1', 'vgg_19/conv5/conv5_2',
'vgg_19/conv5/conv5_3', 'vgg_19/conv5/conv5_4', 'vgg_19/pool5',
'vgg_19/fc6', 'vgg_19/fc7', 'vgg_19/fc8'
]
self.assertSetEqual(set(end_points.keys()), set(expected_names))
import tensorflow as tf
import tensorflow.contrib.slim as slim
from slim.nets.vgg import vgg_19, vgg_arg_scope
from slim.nets.inception_resnet_v2 import inception_resnet_v2, inception_resnet_v2_arg_scope
from slim.nets.resnet_v2 import resnet_v2_152, resnet_v2, resnet_arg_scope
import numpy as np
height = 224
width = 224
channels = 3
image_size = vgg_19.default_image_size
X = tf.placeholder(tf.float32, shape=[None, image_size, image_size, channels])
with slim.arg_scope(vgg_arg_scope()):
logits, end_points = vgg_19(X, num_classes=1000, is_training=False)
print(end_points)
# print(end_points.shape, end_points.type)
predictions = end_points['vgg_19/fc8']
saver = tf.train.Saver()
X_test = np.ones((1, image_size, image_size, channels)) # a fake image
# Execute the graph
with tf.Session() as sess:
saver.restore(sess, '/home/duclong002/pretrained_model/vgg19/vgg_19.ckpt')
predictions_val = predictions.eval(feed_dict={X: X_test})
tf.train.write_graph(sess.graph_def,
'/home/duclong002/pretrained_model/vgg19/',
'vgg19.pbtxt', True)
import numpy as np
import tensorflow as tf
from tensorflow.contrib import slim
from slim.nets import vgg
import os
checkpoints_dir = '/home/long/pretrained_model/vgg19/'
summaries_dir = '/home/long/pretrained_model/summaries/vgg19'
image_size = vgg.vgg_19.default_image_size
with tf.Graph().as_default():
X = tf.placeholder(tf.float32, shape=[None, image_size, image_size, 3])
image = np.ones((1, image_size, image_size, 3), dtype=np.float32)
logits, _ = vgg.vgg_19(image, 1000, is_training=False)
init_fn = slim.assign_from_checkpoint_fn(
os.path.join(checkpoints_dir, 'vgg_19.ckpt'),
slim.get_model_variables('vgg_19'))
probabilities = tf.nn.softmax(logits)
with tf.Session() as sess:
init_fn(sess)
np_image, probabilities = sess.run([X, probabilities],
feed_dict={X: image})
# with tf.name_scope('summaries'):
# tf.summary.scalar(probabilities)
# probabilities = probabilities[0, 0:]
# sorted_inds = [i[0] for i in sorted(enumerate(-probabilities), key=lambda x: x[1])]
# names = imagenet.create_readable_names_for_imagenet_labels()
# for i in range(5):