Python nets.inception.inception_v3() Examples

The following are 30 code examples of nets.inception.inception_v3(). You can vote up the ones you like or vote down the ones you don't like, and go to the original project or source file by following the links above each example. You may also want to check out all available functions/classes of the module nets.inception , or try the search function .
Example #1
Source File: inception_v3_test.py    From R3Det_Tensorflow with MIT License 6 votes vote down vote up 
def testUnknownImageShape(self):
    tf.reset_default_graph()
    batch_size = 2
    height, width = 299, 299
    num_classes = 1000
    input_np = np.random.uniform(0, 1, (batch_size, height, width, 3))
    with self.test_session() as sess:
      inputs = tf.placeholder(tf.float32, shape=(batch_size, None, None, 3))
      logits, end_points = inception.inception_v3(inputs, num_classes)
      self.assertListEqual(logits.get_shape().as_list(),
                           [batch_size, num_classes])
      pre_pool = end_points['Mixed_7c']
      feed_dict = {inputs: input_np}
      tf.global_variables_initializer().run()
      pre_pool_out = sess.run(pre_pool, feed_dict=feed_dict)
      self.assertListEqual(list(pre_pool_out.shape), [batch_size, 8, 8, 2048])
Example #2
Source File: inception_v3_test.py    From R3Det_Tensorflow with MIT License 6 votes vote down vote up 
def testBuildEndPointsWithDepthMultiplierGreaterThanOne(self):
    batch_size = 5
    height, width = 299, 299
    num_classes = 1000

    inputs = tf.random_uniform((batch_size, height, width, 3))
    _, end_points = inception.inception_v3(inputs, num_classes)

    endpoint_keys = [key for key in end_points.keys()
                     if key.startswith('Mixed') or key.startswith('Conv')]

    _, end_points_with_multiplier = inception.inception_v3(
        inputs, num_classes, scope='depth_multiplied_net',
        depth_multiplier=2.0)

    for key in endpoint_keys:
      original_depth = end_points[key].get_shape().as_list()[3]
      new_depth = end_points_with_multiplier[key].get_shape().as_list()[3]
      self.assertEqual(2.0 * original_depth, new_depth)
Example #3
Source File: inception_v3_test.py    From R3Det_Tensorflow with MIT License 6 votes vote down vote up 
def testBuildEndPointsWithDepthMultiplierLessThanOne(self):
    batch_size = 5
    height, width = 299, 299
    num_classes = 1000

    inputs = tf.random_uniform((batch_size, height, width, 3))
    _, end_points = inception.inception_v3(inputs, num_classes)

    endpoint_keys = [key for key in end_points.keys()
                     if key.startswith('Mixed') or key.startswith('Conv')]

    _, end_points_with_multiplier = inception.inception_v3(
        inputs, num_classes, scope='depth_multiplied_net',
        depth_multiplier=0.5)

    for key in endpoint_keys:
      original_depth = end_points[key].get_shape().as_list()[3]
      new_depth = end_points_with_multiplier[key].get_shape().as_list()[3]
      self.assertEqual(0.5 * original_depth, new_depth)
Example #4
Source File: inception_v3_test.py    From DOTA_models with Apache License 2.0 6 votes vote down vote up 
def testBuildEndPoints(self):
    batch_size = 5
    height, width = 299, 299
    num_classes = 1000

    inputs = tf.random_uniform((batch_size, height, width, 3))
    _, end_points = inception.inception_v3(inputs, num_classes)
    self.assertTrue('Logits' in end_points)
    logits = end_points['Logits']
    self.assertListEqual(logits.get_shape().as_list(),
                         [batch_size, num_classes])
    self.assertTrue('AuxLogits' in end_points)
    aux_logits = end_points['AuxLogits']
    self.assertListEqual(aux_logits.get_shape().as_list(),
                         [batch_size, num_classes])
    self.assertTrue('Mixed_7c' in end_points)
    pre_pool = end_points['Mixed_7c']
    self.assertListEqual(pre_pool.get_shape().as_list(),
                         [batch_size, 8, 8, 2048])
    self.assertTrue('PreLogits' in end_points)
    pre_logits = end_points['PreLogits']
    self.assertListEqual(pre_logits.get_shape().as_list(),
                         [batch_size, 1, 1, 2048])
Example #5
Source File: inception_v3_test.py    From R2CNN-Plus-Plus_Tensorflow with MIT License 6 votes vote down vote up 
def testTrainEvalWithReuse(self):
    train_batch_size = 5
    eval_batch_size = 2
    height, width = 150, 150
    num_classes = 1000

    train_inputs = tf.random_uniform((train_batch_size, height, width, 3))
    inception.inception_v3(train_inputs, num_classes)
    eval_inputs = tf.random_uniform((eval_batch_size, height, width, 3))
    logits, _ = inception.inception_v3(eval_inputs, num_classes,
                                       is_training=False, reuse=True)
    predictions = tf.argmax(logits, 1)

    with self.test_session() as sess:
      sess.run(tf.global_variables_initializer())
      output = sess.run(predictions)
      self.assertEquals(output.shape, (eval_batch_size,))
Example #6
Source File: inception_v3_test.py    From R2CNN-Plus-Plus_Tensorflow with MIT License 6 votes vote down vote up 
def testUnknowBatchSize(self):
    batch_size = 1
    height, width = 299, 299
    num_classes = 1000

    inputs = tf.placeholder(tf.float32, (None, height, width, 3))
    logits, _ = inception.inception_v3(inputs, num_classes)
    self.assertTrue(logits.op.name.startswith('InceptionV3/Logits'))
    self.assertListEqual(logits.get_shape().as_list(),
                         [None, num_classes])
    images = tf.random_uniform((batch_size, height, width, 3))

    with self.test_session() as sess:
      sess.run(tf.global_variables_initializer())
      output = sess.run(logits, {inputs: images.eval()})
      self.assertEquals(output.shape, (batch_size, num_classes))
Example #7
Source File: inception_v3_test.py    From R2CNN-Plus-Plus_Tensorflow with MIT License 6 votes vote down vote up 
def testUnknownImageShape(self):
    tf.reset_default_graph()
    batch_size = 2
    height, width = 299, 299
    num_classes = 1000
    input_np = np.random.uniform(0, 1, (batch_size, height, width, 3))
    with self.test_session() as sess:
      inputs = tf.placeholder(tf.float32, shape=(batch_size, None, None, 3))
      logits, end_points = inception.inception_v3(inputs, num_classes)
      self.assertListEqual(logits.get_shape().as_list(),
                           [batch_size, num_classes])
      pre_pool = end_points['Mixed_7c']
      feed_dict = {inputs: input_np}
      tf.global_variables_initializer().run()
      pre_pool_out = sess.run(pre_pool, feed_dict=feed_dict)
      self.assertListEqual(list(pre_pool_out.shape), [batch_size, 8, 8, 2048])
Example #8
Source File: inception_v3_test.py    From R3Det_Tensorflow with MIT License 6 votes vote down vote up 
def testBuildEndPoints(self):
    batch_size = 5
    height, width = 299, 299
    num_classes = 1000

    inputs = tf.random_uniform((batch_size, height, width, 3))
    _, end_points = inception.inception_v3(inputs, num_classes)
    self.assertTrue('Logits' in end_points)
    logits = end_points['Logits']
    self.assertListEqual(logits.get_shape().as_list(),
                         [batch_size, num_classes])
    self.assertTrue('AuxLogits' in end_points)
    aux_logits = end_points['AuxLogits']
    self.assertListEqual(aux_logits.get_shape().as_list(),
                         [batch_size, num_classes])
    self.assertTrue('Mixed_7c' in end_points)
    pre_pool = end_points['Mixed_7c']
    self.assertListEqual(pre_pool.get_shape().as_list(),
                         [batch_size, 8, 8, 2048])
    self.assertTrue('PreLogits' in end_points)
    pre_logits = end_points['PreLogits']
    self.assertListEqual(pre_logits.get_shape().as_list(),
                         [batch_size, 1, 1, 2048])
Example #9
Source File: inception_v3_test.py    From R3Det_Tensorflow with MIT License 6 votes vote down vote up 
def testUnknowBatchSize(self):
    batch_size = 1
    height, width = 299, 299
    num_classes = 1000

    inputs = tf.placeholder(tf.float32, (None, height, width, 3))
    logits, _ = inception.inception_v3(inputs, num_classes)
    self.assertTrue(logits.op.name.startswith('InceptionV3/Logits'))
    self.assertListEqual(logits.get_shape().as_list(),
                         [None, num_classes])
    images = tf.random_uniform((batch_size, height, width, 3))

    with self.test_session() as sess:
      sess.run(tf.global_variables_initializer())
      output = sess.run(logits, {inputs: images.eval()})
      self.assertEquals(output.shape, (batch_size, num_classes))
Example #10
Source File: inception_v3_test.py    From R3Det_Tensorflow with MIT License 6 votes vote down vote up 
def testTrainEvalWithReuse(self):
    train_batch_size = 5
    eval_batch_size = 2
    height, width = 150, 150
    num_classes = 1000

    train_inputs = tf.random_uniform((train_batch_size, height, width, 3))
    inception.inception_v3(train_inputs, num_classes)
    eval_inputs = tf.random_uniform((eval_batch_size, height, width, 3))
    logits, _ = inception.inception_v3(eval_inputs, num_classes,
                                       is_training=False, reuse=True)
    predictions = tf.argmax(logits, 1)

    with self.test_session() as sess:
      sess.run(tf.global_variables_initializer())
      output = sess.run(predictions)
      self.assertEquals(output.shape, (eval_batch_size,))
Example #11
Source File: inception_v3_test.py    From CVTron with Apache License 2.0 6 votes vote down vote up 
def testBuildEndPoints(self):
    batch_size = 5
    height, width = 299, 299
    num_classes = 1000

    inputs = tf.random_uniform((batch_size, height, width, 3))
    _, end_points = inception.inception_v3(inputs, num_classes)
    self.assertTrue('Logits' in end_points)
    logits = end_points['Logits']
    self.assertListEqual(logits.get_shape().as_list(),
                         [batch_size, num_classes])
    self.assertTrue('AuxLogits' in end_points)
    aux_logits = end_points['AuxLogits']
    self.assertListEqual(aux_logits.get_shape().as_list(),
                         [batch_size, num_classes])
    self.assertTrue('Mixed_7c' in end_points)
    pre_pool = end_points['Mixed_7c']
    self.assertListEqual(pre_pool.get_shape().as_list(),
                         [batch_size, 8, 8, 2048])
    self.assertTrue('PreLogits' in end_points)
    pre_logits = end_points['PreLogits']
    self.assertListEqual(pre_logits.get_shape().as_list(),
                         [batch_size, 1, 1, 2048])
Example #12
Source File: inception_v3_test.py    From CVTron with Apache License 2.0 6 votes vote down vote up 
def testBuildEndPointsWithDepthMultiplierLessThanOne(self):
    batch_size = 5
    height, width = 299, 299
    num_classes = 1000

    inputs = tf.random_uniform((batch_size, height, width, 3))
    _, end_points = inception.inception_v3(inputs, num_classes)

    endpoint_keys = [key for key in end_points.keys()
                     if key.startswith('Mixed') or key.startswith('Conv')]

    _, end_points_with_multiplier = inception.inception_v3(
        inputs, num_classes, scope='depth_multiplied_net',
        depth_multiplier=0.5)

    for key in endpoint_keys:
      original_depth = end_points[key].get_shape().as_list()[3]
      new_depth = end_points_with_multiplier[key].get_shape().as_list()[3]
      self.assertEqual(0.5 * original_depth, new_depth)
Example #13
Source File: inception_v3_test.py    From CVTron with Apache License 2.0 6 votes vote down vote up 
def testBuildEndPointsWithDepthMultiplierGreaterThanOne(self):
    batch_size = 5
    height, width = 299, 299
    num_classes = 1000

    inputs = tf.random_uniform((batch_size, height, width, 3))
    _, end_points = inception.inception_v3(inputs, num_classes)

    endpoint_keys = [key for key in end_points.keys()
                     if key.startswith('Mixed') or key.startswith('Conv')]

    _, end_points_with_multiplier = inception.inception_v3(
        inputs, num_classes, scope='depth_multiplied_net',
        depth_multiplier=2.0)

    for key in endpoint_keys:
      original_depth = end_points[key].get_shape().as_list()[3]
      new_depth = end_points_with_multiplier[key].get_shape().as_list()[3]
      self.assertEqual(2.0 * original_depth, new_depth)
Example #14
Source File: inception_v3_test.py    From CVTron with Apache License 2.0 6 votes vote down vote up 
def testUnknownImageShape(self):
    tf.reset_default_graph()
    batch_size = 2
    height, width = 299, 299
    num_classes = 1000
    input_np = np.random.uniform(0, 1, (batch_size, height, width, 3))
    with self.test_session() as sess:
      inputs = tf.placeholder(tf.float32, shape=(batch_size, None, None, 3))
      logits, end_points = inception.inception_v3(inputs, num_classes)
      self.assertListEqual(logits.get_shape().as_list(),
                           [batch_size, num_classes])
      pre_pool = end_points['Mixed_7c']
      feed_dict = {inputs: input_np}
      tf.global_variables_initializer().run()
      pre_pool_out = sess.run(pre_pool, feed_dict=feed_dict)
      self.assertListEqual(list(pre_pool_out.shape), [batch_size, 8, 8, 2048])
Example #15
Source File: inception_v3_test.py    From CVTron with Apache License 2.0 6 votes vote down vote up 
def testGlobalPoolUnknownImageShape(self):
    tf.reset_default_graph()
    batch_size = 1
    height, width = 330, 400
    num_classes = 1000
    input_np = np.random.uniform(0, 1, (batch_size, height, width, 3))
    with self.test_session() as sess:
      inputs = tf.placeholder(tf.float32, shape=(batch_size, None, None, 3))
      logits, end_points = inception.inception_v3(inputs, num_classes,
                                                  global_pool=True)
      self.assertListEqual(logits.get_shape().as_list(),
                           [batch_size, num_classes])
      pre_pool = end_points['Mixed_7c']
      feed_dict = {inputs: input_np}
      tf.global_variables_initializer().run()
      pre_pool_out = sess.run(pre_pool, feed_dict=feed_dict)
      self.assertListEqual(list(pre_pool_out.shape), [batch_size, 8, 11, 2048])
Example #16
Source File: inception_v3_test.py    From R2CNN-Plus-Plus_Tensorflow with MIT License 6 votes vote down vote up 
def testBuildEndPointsWithDepthMultiplierGreaterThanOne(self):
    batch_size = 5
    height, width = 299, 299
    num_classes = 1000

    inputs = tf.random_uniform((batch_size, height, width, 3))
    _, end_points = inception.inception_v3(inputs, num_classes)

    endpoint_keys = [key for key in end_points.keys()
                     if key.startswith('Mixed') or key.startswith('Conv')]

    _, end_points_with_multiplier = inception.inception_v3(
        inputs, num_classes, scope='depth_multiplied_net',
        depth_multiplier=2.0)

    for key in endpoint_keys:
      original_depth = end_points[key].get_shape().as_list()[3]
      new_depth = end_points_with_multiplier[key].get_shape().as_list()[3]
      self.assertEqual(2.0 * original_depth, new_depth)
Example #17
Source File: inception_v3_test.py    From R2CNN-Plus-Plus_Tensorflow with MIT License 6 votes vote down vote up 
def testBuildEndPointsWithDepthMultiplierLessThanOne(self):
    batch_size = 5
    height, width = 299, 299
    num_classes = 1000

    inputs = tf.random_uniform((batch_size, height, width, 3))
    _, end_points = inception.inception_v3(inputs, num_classes)

    endpoint_keys = [key for key in end_points.keys()
                     if key.startswith('Mixed') or key.startswith('Conv')]

    _, end_points_with_multiplier = inception.inception_v3(
        inputs, num_classes, scope='depth_multiplied_net',
        depth_multiplier=0.5)

    for key in endpoint_keys:
      original_depth = end_points[key].get_shape().as_list()[3]
      new_depth = end_points_with_multiplier[key].get_shape().as_list()[3]
      self.assertEqual(0.5 * original_depth, new_depth)
Example #18
Source File: inception_v3_test.py    From R2CNN-Plus-Plus_Tensorflow with MIT License 6 votes vote down vote up 
def testBuildEndPoints(self):
    batch_size = 5
    height, width = 299, 299
    num_classes = 1000

    inputs = tf.random_uniform((batch_size, height, width, 3))
    _, end_points = inception.inception_v3(inputs, num_classes)
    self.assertTrue('Logits' in end_points)
    logits = end_points['Logits']
    self.assertListEqual(logits.get_shape().as_list(),
                         [batch_size, num_classes])
    self.assertTrue('AuxLogits' in end_points)
    aux_logits = end_points['AuxLogits']
    self.assertListEqual(aux_logits.get_shape().as_list(),
                         [batch_size, num_classes])
    self.assertTrue('Mixed_7c' in end_points)
    pre_pool = end_points['Mixed_7c']
    self.assertListEqual(pre_pool.get_shape().as_list(),
                         [batch_size, 8, 8, 2048])
    self.assertTrue('PreLogits' in end_points)
    pre_logits = end_points['PreLogits']
    self.assertListEqual(pre_logits.get_shape().as_list(),
                         [batch_size, 1, 1, 2048])
Example #19
Source File: inception_v3_test.py    From tf-pose with Apache License 2.0 6 votes vote down vote up 
def testUnknowBatchSize(self):
    batch_size = 1
    height, width = 299, 299
    num_classes = 1000

    inputs = tf.placeholder(tf.float32, (None, height, width, 3))
    logits, _ = inception.inception_v3(inputs, num_classes)
    self.assertTrue(logits.op.name.startswith('InceptionV3/Logits'))
    self.assertListEqual(logits.get_shape().as_list(),
                         [None, num_classes])
    images = tf.random_uniform((batch_size, height, width, 3))

    with self.test_session() as sess:
      sess.run(tf.global_variables_initializer())
      output = sess.run(logits, {inputs: images.eval()})
      self.assertEquals(output.shape, (batch_size, num_classes))
Example #20
Source File: inception_v3_test.py    From tf-pose with Apache License 2.0 6 votes vote down vote up 
def testGlobalPoolUnknownImageShape(self):
    tf.reset_default_graph()
    batch_size = 2
    height, width = 400, 600
    num_classes = 1000
    input_np = np.random.uniform(0, 1, (batch_size, height, width, 3))
    with self.test_session() as sess:
      inputs = tf.placeholder(tf.float32, shape=(batch_size, None, None, 3))
      logits, end_points = inception.inception_v3(inputs, num_classes,
                                                  global_pool=True)
      self.assertListEqual(logits.get_shape().as_list(),
                           [batch_size, num_classes])
      pre_pool = end_points['Mixed_7c']
      feed_dict = {inputs: input_np}
      tf.global_variables_initializer().run()
      pre_pool_out = sess.run(pre_pool, feed_dict=feed_dict)
      self.assertListEqual(list(pre_pool_out.shape), [batch_size, 11, 17, 2048])
Example #21
Source File: inception_v3_test.py    From tf-pose with Apache License 2.0 6 votes vote down vote up 
def testUnknownImageShape(self):
    tf.reset_default_graph()
    batch_size = 2
    height, width = 299, 299
    num_classes = 1000
    input_np = np.random.uniform(0, 1, (batch_size, height, width, 3))
    with self.test_session() as sess:
      inputs = tf.placeholder(tf.float32, shape=(batch_size, None, None, 3))
      logits, end_points = inception.inception_v3(inputs, num_classes)
      self.assertListEqual(logits.get_shape().as_list(),
                           [batch_size, num_classes])
      pre_pool = end_points['Mixed_7c']
      feed_dict = {inputs: input_np}
      tf.global_variables_initializer().run()
      pre_pool_out = sess.run(pre_pool, feed_dict=feed_dict)
      self.assertListEqual(list(pre_pool_out.shape), [batch_size, 8, 8, 2048])
Example #22
Source File: inception_v3_test.py    From tf-pose with Apache License 2.0 6 votes vote down vote up 
def testBuildEndPointsWithDepthMultiplierGreaterThanOne(self):
    batch_size = 5
    height, width = 299, 299
    num_classes = 1000

    inputs = tf.random_uniform((batch_size, height, width, 3))
    _, end_points = inception.inception_v3(inputs, num_classes)

    endpoint_keys = [key for key in end_points.keys()
                     if key.startswith('Mixed') or key.startswith('Conv')]

    _, end_points_with_multiplier = inception.inception_v3(
        inputs, num_classes, scope='depth_multiplied_net',
        depth_multiplier=2.0)

    for key in endpoint_keys:
      original_depth = end_points[key].get_shape().as_list()[3]
      new_depth = end_points_with_multiplier[key].get_shape().as_list()[3]
      self.assertEqual(2.0 * original_depth, new_depth)
Example #23
Source File: inception_v3_test.py    From tf-pose with Apache License 2.0 6 votes vote down vote up 
def testBuildEndPointsWithDepthMultiplierLessThanOne(self):
    batch_size = 5
    height, width = 299, 299
    num_classes = 1000

    inputs = tf.random_uniform((batch_size, height, width, 3))
    _, end_points = inception.inception_v3(inputs, num_classes)

    endpoint_keys = [key for key in end_points.keys()
                     if key.startswith('Mixed') or key.startswith('Conv')]

    _, end_points_with_multiplier = inception.inception_v3(
        inputs, num_classes, scope='depth_multiplied_net',
        depth_multiplier=0.5)

    for key in endpoint_keys:
      original_depth = end_points[key].get_shape().as_list()[3]
      new_depth = end_points_with_multiplier[key].get_shape().as_list()[3]
      self.assertEqual(0.5 * original_depth, new_depth)
Example #24
Source File: inception_v3_test.py    From tf-pose with Apache License 2.0 6 votes vote down vote up 
def testBuildEndPoints(self):
    batch_size = 5
    height, width = 299, 299
    num_classes = 1000

    inputs = tf.random_uniform((batch_size, height, width, 3))
    _, end_points = inception.inception_v3(inputs, num_classes)
    self.assertTrue('Logits' in end_points)
    logits = end_points['Logits']
    self.assertListEqual(logits.get_shape().as_list(),
                         [batch_size, num_classes])
    self.assertTrue('AuxLogits' in end_points)
    aux_logits = end_points['AuxLogits']
    self.assertListEqual(aux_logits.get_shape().as_list(),
                         [batch_size, num_classes])
    self.assertTrue('Mixed_7c' in end_points)
    pre_pool = end_points['Mixed_7c']
    self.assertListEqual(pre_pool.get_shape().as_list(),
                         [batch_size, 8, 8, 2048])
    self.assertTrue('PreLogits' in end_points)
    pre_logits = end_points['PreLogits']
    self.assertListEqual(pre_logits.get_shape().as_list(),
                         [batch_size, 1, 1, 2048])
Example #25
Source File: inception_v3_test.py    From R2CNN_Faster-RCNN_Tensorflow with MIT License 6 votes vote down vote up 
def testTrainEvalWithReuse(self):
    train_batch_size = 5
    eval_batch_size = 2
    height, width = 150, 150
    num_classes = 1000

    train_inputs = tf.random_uniform((train_batch_size, height, width, 3))
    inception.inception_v3(train_inputs, num_classes)
    eval_inputs = tf.random_uniform((eval_batch_size, height, width, 3))
    logits, _ = inception.inception_v3(eval_inputs, num_classes,
                                       is_training=False, reuse=True)
    predictions = tf.argmax(logits, 1)

    with self.test_session() as sess:
      sess.run(tf.global_variables_initializer())
      output = sess.run(predictions)
      self.assertEquals(output.shape, (eval_batch_size,))
Example #26
Source File: inception_v3_test.py    From R2CNN_Faster-RCNN_Tensorflow with MIT License 6 votes vote down vote up 
def testUnknowBatchSize(self):
    batch_size = 1
    height, width = 299, 299
    num_classes = 1000

    inputs = tf.placeholder(tf.float32, (None, height, width, 3))
    logits, _ = inception.inception_v3(inputs, num_classes)
    self.assertTrue(logits.op.name.startswith('InceptionV3/Logits'))
    self.assertListEqual(logits.get_shape().as_list(),
                         [None, num_classes])
    images = tf.random_uniform((batch_size, height, width, 3))

    with self.test_session() as sess:
      sess.run(tf.global_variables_initializer())
      output = sess.run(logits, {inputs: images.eval()})
      self.assertEquals(output.shape, (batch_size, num_classes))
Example #27
Source File: inception_v3_test.py    From R2CNN_Faster-RCNN_Tensorflow with MIT License 6 votes vote down vote up 
def testUnknownImageShape(self):
    tf.reset_default_graph()
    batch_size = 2
    height, width = 299, 299
    num_classes = 1000
    input_np = np.random.uniform(0, 1, (batch_size, height, width, 3))
    with self.test_session() as sess:
      inputs = tf.placeholder(tf.float32, shape=(batch_size, None, None, 3))
      logits, end_points = inception.inception_v3(inputs, num_classes)
      self.assertListEqual(logits.get_shape().as_list(),
                           [batch_size, num_classes])
      pre_pool = end_points['Mixed_7c']
      feed_dict = {inputs: input_np}
      tf.global_variables_initializer().run()
      pre_pool_out = sess.run(pre_pool, feed_dict=feed_dict)
      self.assertListEqual(list(pre_pool_out.shape), [batch_size, 8, 8, 2048])
Example #28
Source File: inception_v3_test.py    From R2CNN_Faster-RCNN_Tensorflow with MIT License 6 votes vote down vote up 
def testBuildEndPointsWithDepthMultiplierGreaterThanOne(self):
    batch_size = 5
    height, width = 299, 299
    num_classes = 1000

    inputs = tf.random_uniform((batch_size, height, width, 3))
    _, end_points = inception.inception_v3(inputs, num_classes)

    endpoint_keys = [key for key in end_points.keys()
                     if key.startswith('Mixed') or key.startswith('Conv')]

    _, end_points_with_multiplier = inception.inception_v3(
        inputs, num_classes, scope='depth_multiplied_net',
        depth_multiplier=2.0)

    for key in endpoint_keys:
      original_depth = end_points[key].get_shape().as_list()[3]
      new_depth = end_points_with_multiplier[key].get_shape().as_list()[3]
      self.assertEqual(2.0 * original_depth, new_depth)
Example #29
Source File: inception_v3_test.py    From R2CNN_Faster-RCNN_Tensorflow with MIT License 6 votes vote down vote up 
def testBuildEndPointsWithDepthMultiplierLessThanOne(self):
    batch_size = 5
    height, width = 299, 299
    num_classes = 1000

    inputs = tf.random_uniform((batch_size, height, width, 3))
    _, end_points = inception.inception_v3(inputs, num_classes)

    endpoint_keys = [key for key in end_points.keys()
                     if key.startswith('Mixed') or key.startswith('Conv')]

    _, end_points_with_multiplier = inception.inception_v3(
        inputs, num_classes, scope='depth_multiplied_net',
        depth_multiplier=0.5)

    for key in endpoint_keys:
      original_depth = end_points[key].get_shape().as_list()[3]
      new_depth = end_points_with_multiplier[key].get_shape().as_list()[3]
      self.assertEqual(0.5 * original_depth, new_depth)
Example #30
Source File: inception_v3_test.py    From R2CNN_Faster-RCNN_Tensorflow with MIT License 6 votes vote down vote up 
def testBuildEndPoints(self):
    batch_size = 5
    height, width = 299, 299
    num_classes = 1000

    inputs = tf.random_uniform((batch_size, height, width, 3))
    _, end_points = inception.inception_v3(inputs, num_classes)
    self.assertTrue('Logits' in end_points)
    logits = end_points['Logits']
    self.assertListEqual(logits.get_shape().as_list(),
                         [batch_size, num_classes])
    self.assertTrue('AuxLogits' in end_points)
    aux_logits = end_points['AuxLogits']
    self.assertListEqual(aux_logits.get_shape().as_list(),
                         [batch_size, num_classes])
    self.assertTrue('Mixed_7c' in end_points)
    pre_pool = end_points['Mixed_7c']
    self.assertListEqual(pre_pool.get_shape().as_list(),
                         [batch_size, 8, 8, 2048])
    self.assertTrue('PreLogits' in end_points)
    pre_logits = end_points['PreLogits']
    self.assertListEqual(pre_logits.get_shape().as_list(),
                         [batch_size, 1, 1, 2048])