mirror of
https://github.com/ArthurDanjou/handson-ml3.git
synced 2026-02-01 04:27:50 +01:00
Add TF models slim nets
This commit is contained in:
Aurélien Geron
455
nets/vgg_test.py
Normal file
455
nets/vgg_test.py
Normal file
@@ -0,0 +1,455 @@
|
||||
# Copyright 2016 The TensorFlow Authors. All Rights Reserved.
|
||||
#
|
||||
# Licensed under the Apache License, Version 2.0 (the "License");
|
||||
# you may not use this file except in compliance with the License.
|
||||
# You may obtain a copy of the License at
|
||||
#
|
||||
# http://www.apache.org/licenses/LICENSE-2.0
|
||||
#
|
||||
# Unless required by applicable law or agreed to in writing, software
|
||||
# distributed under the License is distributed on an "AS IS" BASIS,
|
||||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
# See the License for the specific language governing permissions and
|
||||
# limitations under the License.
|
||||
# ==============================================================================
|
||||
"""Tests for slim.nets.vgg."""
|
||||
from __future__ import absolute_import
|
||||
from __future__ import division
|
||||
from __future__ import print_function
|
||||
|
||||
import tensorflow as tf
|
||||
|
||||
from nets import vgg
|
||||
|
||||
slim = tf.contrib.slim
|
||||
|
||||
|
||||
class VGGATest(tf.test.TestCase):
|
||||
|
||||
def testBuild(self):
|
||||
batch_size = 5
|
||||
height, width = 224, 224
|
||||
num_classes = 1000
|
||||
with self.test_session():
|
||||
inputs = tf.random_uniform((batch_size, height, width, 3))
|
||||
logits, _ = vgg.vgg_a(inputs, num_classes)
|
||||
self.assertEquals(logits.op.name, 'vgg_a/fc8/squeezed')
|
||||
self.assertListEqual(logits.get_shape().as_list(),
|
||||
[batch_size, num_classes])
|
||||
|
||||
def testFullyConvolutional(self):
|
||||
batch_size = 1
|
||||
height, width = 256, 256
|
||||
num_classes = 1000
|
||||
with self.test_session():
|
||||
inputs = tf.random_uniform((batch_size, height, width, 3))
|
||||
logits, _ = vgg.vgg_a(inputs, num_classes, spatial_squeeze=False)
|
||||
self.assertEquals(logits.op.name, 'vgg_a/fc8/BiasAdd')
|
||||
self.assertListEqual(logits.get_shape().as_list(),
|
||||
[batch_size, 2, 2, num_classes])
|
||||
|
||||
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_a(inputs, num_classes)
|
||||
expected_names = ['vgg_a/conv1/conv1_1',
|
||||
'vgg_a/pool1',
|
||||
'vgg_a/conv2/conv2_1',
|
||||
'vgg_a/pool2',
|
||||
'vgg_a/conv3/conv3_1',
|
||||
'vgg_a/conv3/conv3_2',
|
||||
'vgg_a/pool3',
|
||||
'vgg_a/conv4/conv4_1',
|
||||
'vgg_a/conv4/conv4_2',
|
||||
'vgg_a/pool4',
|
||||
'vgg_a/conv5/conv5_1',
|
||||
'vgg_a/conv5/conv5_2',
|
||||
'vgg_a/pool5',
|
||||
'vgg_a/fc6',
|
||||
'vgg_a/fc7',
|
||||
'vgg_a/fc8'
|
||||
]
|
||||
self.assertSetEqual(set(end_points.keys()), set(expected_names))
|
||||
|
||||
def testModelVariables(self):
|
||||
batch_size = 5
|
||||
height, width = 224, 224
|
||||
num_classes = 1000
|
||||
with self.test_session():
|
||||
inputs = tf.random_uniform((batch_size, height, width, 3))
|
||||
vgg.vgg_a(inputs, num_classes)
|
||||
expected_names = ['vgg_a/conv1/conv1_1/weights',
|
||||
'vgg_a/conv1/conv1_1/biases',
|
||||
'vgg_a/conv2/conv2_1/weights',
|
||||
'vgg_a/conv2/conv2_1/biases',
|
||||
'vgg_a/conv3/conv3_1/weights',
|
||||
'vgg_a/conv3/conv3_1/biases',
|
||||
'vgg_a/conv3/conv3_2/weights',
|
||||
'vgg_a/conv3/conv3_2/biases',
|
||||
'vgg_a/conv4/conv4_1/weights',
|
||||
'vgg_a/conv4/conv4_1/biases',
|
||||
'vgg_a/conv4/conv4_2/weights',
|
||||
'vgg_a/conv4/conv4_2/biases',
|
||||
'vgg_a/conv5/conv5_1/weights',
|
||||
'vgg_a/conv5/conv5_1/biases',
|
||||
'vgg_a/conv5/conv5_2/weights',
|
||||
'vgg_a/conv5/conv5_2/biases',
|
||||
'vgg_a/fc6/weights',
|
||||
'vgg_a/fc6/biases',
|
||||
'vgg_a/fc7/weights',
|
||||
'vgg_a/fc7/biases',
|
||||
'vgg_a/fc8/weights',
|
||||
'vgg_a/fc8/biases',
|
||||
]
|
||||
model_variables = [v.op.name for v in slim.get_model_variables()]
|
||||
self.assertSetEqual(set(model_variables), set(expected_names))
|
||||
|
||||
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_a(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 = tf.random_uniform(
|
||||
(train_batch_size, train_height, train_width, 3))
|
||||
logits, _ = vgg.vgg_a(train_inputs)
|
||||
self.assertListEqual(logits.get_shape().as_list(),
|
||||
[train_batch_size, num_classes])
|
||||
tf.get_variable_scope().reuse_variables()
|
||||
eval_inputs = tf.random_uniform(
|
||||
(eval_batch_size, eval_height, eval_width, 3))
|
||||
logits, _ = vgg.vgg_a(eval_inputs, is_training=False,
|
||||
spatial_squeeze=False)
|
||||
self.assertListEqual(logits.get_shape().as_list(),
|
||||
[eval_batch_size, 2, 2, num_classes])
|
||||
logits = tf.reduce_mean(logits, [1, 2])
|
||||
predictions = tf.argmax(logits, 1)
|
||||
self.assertEquals(predictions.get_shape().as_list(), [eval_batch_size])
|
||||
|
||||
def testForward(self):
|
||||
batch_size = 1
|
||||
height, width = 224, 224
|
||||
with self.test_session() as sess:
|
||||
inputs = tf.random_uniform((batch_size, height, width, 3))
|
||||
logits, _ = vgg.vgg_a(inputs)
|
||||
sess.run(tf.initialize_all_variables())
|
||||
output = sess.run(logits)
|
||||
self.assertTrue(output.any())
|
||||
|
||||
|
||||
class VGG16Test(tf.test.TestCase):
|
||||
|
||||
def testBuild(self):
|
||||
batch_size = 5
|
||||
height, width = 224, 224
|
||||
num_classes = 1000
|
||||
with self.test_session():
|
||||
inputs = tf.random_uniform((batch_size, height, width, 3))
|
||||
logits, _ = vgg.vgg_16(inputs, num_classes)
|
||||
self.assertEquals(logits.op.name, 'vgg_16/fc8/squeezed')
|
||||
self.assertListEqual(logits.get_shape().as_list(),
|
||||
[batch_size, num_classes])
|
||||
|
||||
def testFullyConvolutional(self):
|
||||
batch_size = 1
|
||||
height, width = 256, 256
|
||||
num_classes = 1000
|
||||
with self.test_session():
|
||||
inputs = tf.random_uniform((batch_size, height, width, 3))
|
||||
logits, _ = vgg.vgg_16(inputs, num_classes, spatial_squeeze=False)
|
||||
self.assertEquals(logits.op.name, 'vgg_16/fc8/BiasAdd')
|
||||
self.assertListEqual(logits.get_shape().as_list(),
|
||||
[batch_size, 2, 2, num_classes])
|
||||
|
||||
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_16(inputs, num_classes)
|
||||
expected_names = ['vgg_16/conv1/conv1_1',
|
||||
'vgg_16/conv1/conv1_2',
|
||||
'vgg_16/pool1',
|
||||
'vgg_16/conv2/conv2_1',
|
||||
'vgg_16/conv2/conv2_2',
|
||||
'vgg_16/pool2',
|
||||
'vgg_16/conv3/conv3_1',
|
||||
'vgg_16/conv3/conv3_2',
|
||||
'vgg_16/conv3/conv3_3',
|
||||
'vgg_16/pool3',
|
||||
'vgg_16/conv4/conv4_1',
|
||||
'vgg_16/conv4/conv4_2',
|
||||
'vgg_16/conv4/conv4_3',
|
||||
'vgg_16/pool4',
|
||||
'vgg_16/conv5/conv5_1',
|
||||
'vgg_16/conv5/conv5_2',
|
||||
'vgg_16/conv5/conv5_3',
|
||||
'vgg_16/pool5',
|
||||
'vgg_16/fc6',
|
||||
'vgg_16/fc7',
|
||||
'vgg_16/fc8'
|
||||
]
|
||||
self.assertSetEqual(set(end_points.keys()), set(expected_names))
|
||||
|
||||
def testModelVariables(self):
|
||||
batch_size = 5
|
||||
height, width = 224, 224
|
||||
num_classes = 1000
|
||||
with self.test_session():
|
||||
inputs = tf.random_uniform((batch_size, height, width, 3))
|
||||
vgg.vgg_16(inputs, num_classes)
|
||||
expected_names = ['vgg_16/conv1/conv1_1/weights',
|
||||
'vgg_16/conv1/conv1_1/biases',
|
||||
'vgg_16/conv1/conv1_2/weights',
|
||||
'vgg_16/conv1/conv1_2/biases',
|
||||
'vgg_16/conv2/conv2_1/weights',
|
||||
'vgg_16/conv2/conv2_1/biases',
|
||||
'vgg_16/conv2/conv2_2/weights',
|
||||
'vgg_16/conv2/conv2_2/biases',
|
||||
'vgg_16/conv3/conv3_1/weights',
|
||||
'vgg_16/conv3/conv3_1/biases',
|
||||
'vgg_16/conv3/conv3_2/weights',
|
||||
'vgg_16/conv3/conv3_2/biases',
|
||||
'vgg_16/conv3/conv3_3/weights',
|
||||
'vgg_16/conv3/conv3_3/biases',
|
||||
'vgg_16/conv4/conv4_1/weights',
|
||||
'vgg_16/conv4/conv4_1/biases',
|
||||
'vgg_16/conv4/conv4_2/weights',
|
||||
'vgg_16/conv4/conv4_2/biases',
|
||||
'vgg_16/conv4/conv4_3/weights',
|
||||
'vgg_16/conv4/conv4_3/biases',
|
||||
'vgg_16/conv5/conv5_1/weights',
|
||||
'vgg_16/conv5/conv5_1/biases',
|
||||
'vgg_16/conv5/conv5_2/weights',
|
||||
'vgg_16/conv5/conv5_2/biases',
|
||||
'vgg_16/conv5/conv5_3/weights',
|
||||
'vgg_16/conv5/conv5_3/biases',
|
||||
'vgg_16/fc6/weights',
|
||||
'vgg_16/fc6/biases',
|
||||
'vgg_16/fc7/weights',
|
||||
'vgg_16/fc7/biases',
|
||||
'vgg_16/fc8/weights',
|
||||
'vgg_16/fc8/biases',
|
||||
]
|
||||
model_variables = [v.op.name for v in slim.get_model_variables()]
|
||||
self.assertSetEqual(set(model_variables), set(expected_names))
|
||||
|
||||
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_16(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 = tf.random_uniform(
|
||||
(train_batch_size, train_height, train_width, 3))
|
||||
logits, _ = vgg.vgg_16(train_inputs)
|
||||
self.assertListEqual(logits.get_shape().as_list(),
|
||||
[train_batch_size, num_classes])
|
||||
tf.get_variable_scope().reuse_variables()
|
||||
eval_inputs = tf.random_uniform(
|
||||
(eval_batch_size, eval_height, eval_width, 3))
|
||||
logits, _ = vgg.vgg_16(eval_inputs, is_training=False,
|
||||
spatial_squeeze=False)
|
||||
self.assertListEqual(logits.get_shape().as_list(),
|
||||
[eval_batch_size, 2, 2, num_classes])
|
||||
logits = tf.reduce_mean(logits, [1, 2])
|
||||
predictions = tf.argmax(logits, 1)
|
||||
self.assertEquals(predictions.get_shape().as_list(), [eval_batch_size])
|
||||
|
||||
def testForward(self):
|
||||
batch_size = 1
|
||||
height, width = 224, 224
|
||||
with self.test_session() as sess:
|
||||
inputs = tf.random_uniform((batch_size, height, width, 3))
|
||||
logits, _ = vgg.vgg_16(inputs)
|
||||
sess.run(tf.initialize_all_variables())
|
||||
output = sess.run(logits)
|
||||
self.assertTrue(output.any())
|
||||
|
||||
|
||||
class VGG19Test(tf.test.TestCase):
|
||||
|
||||
def testBuild(self):
|
||||
batch_size = 5
|
||||
height, width = 224, 224
|
||||
num_classes = 1000
|
||||
with self.test_session():
|
||||
inputs = tf.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 testFullyConvolutional(self):
|
||||
batch_size = 1
|
||||
height, width = 256, 256
|
||||
num_classes = 1000
|
||||
with self.test_session():
|
||||
inputs = tf.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 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))
|
||||
|
||||
def testModelVariables(self):
|
||||
batch_size = 5
|
||||
height, width = 224, 224
|
||||
num_classes = 1000
|
||||
with self.test_session():
|
||||
inputs = tf.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 slim.get_model_variables()]
|
||||
self.assertSetEqual(set(model_variables), set(expected_names))
|
||||
|
||||
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 = tf.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])
|
||||
tf.get_variable_scope().reuse_variables()
|
||||
eval_inputs = tf.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 = tf.reduce_mean(logits, [1, 2])
|
||||
predictions = tf.argmax(logits, 1)
|
||||
self.assertEquals(predictions.get_shape().as_list(), [eval_batch_size])
|
||||
|
||||
def testForward(self):
|
||||
batch_size = 1
|
||||
height, width = 224, 224
|
||||
with self.test_session() as sess:
|
||||
inputs = tf.random_uniform((batch_size, height, width, 3))
|
||||
logits, _ = vgg.vgg_19(inputs)
|
||||
sess.run(tf.initialize_all_variables())
|
||||
output = sess.run(logits)
|
||||
self.assertTrue(output.any())
|
||||
|
||||
if __name__ == '__main__':
|
||||
tf.test.main()
|
||||
Reference in New Issue
Block a user