Python vgg.vgg_16方法代码示例

# 需要导入模块: from tensorflow.contrib.slim.python.slim.nets import vgg [as 别名]
# 或者: from tensorflow.contrib.slim.python.slim.nets.vgg import vgg_16 [as 别名]
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_16(inputs, num_classes, is_training=is_training)
        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)) 

# 需要导入模块: from tensorflow.contrib.slim.python.slim.nets import vgg [as 别名]
# 或者: from tensorflow.contrib.slim.python.slim.nets.vgg import vgg_16 [as 别名]
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_16(inputs, num_classes)
      self.assertEquals(logits.op.name, 'vgg_16/fc8/squeezed')
      self.assertListEqual(logits.get_shape().as_list(),
                           [batch_size, num_classes]) 

# 需要导入模块: from tensorflow.contrib.slim.python.slim.nets import vgg [as 别名]
# 或者: from tensorflow.contrib.slim.python.slim.nets.vgg import vgg_16 [as 别名]
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_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]) 

# 需要导入模块: from tensorflow.contrib.slim.python.slim.nets import vgg [as 别名]
# 或者: from tensorflow.contrib.slim.python.slim.nets.vgg import vgg_16 [as 别名]
def testEvaluation(self):
    batch_size = 2
    height, width = 224, 224
    num_classes = 1000
    with self.test_session():
      eval_inputs = random_ops.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 = math_ops.argmax(logits, 1)
      self.assertListEqual(predictions.get_shape().as_list(), [batch_size]) 

# 需要导入模块: from tensorflow.contrib.slim.python.slim.nets import vgg [as 别名]
# 或者: from tensorflow.contrib.slim.python.slim.nets.vgg import vgg_16 [as 别名]
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_16(inputs)
      sess.run(variables.global_variables_initializer())
      output = sess.run(logits)
      self.assertTrue(output.any()) 

# 需要导入模块: from tensorflow.contrib.slim.python.slim.nets import vgg [as 别名]
# 或者: from tensorflow.contrib.slim.python.slim.nets.vgg import vgg_16 [as 别名]
def network_vgg_16():
    input_shape = [1, 224, 224, 3]
    input_ = tf.placeholder(dtype=tf.float32, name='input', shape=input_shape)
    net, _end_points = vgg_16(input_, num_classes=1000, is_training=False)
    return net 

# 需要导入模块: from tensorflow.contrib.slim.python.slim.nets import vgg [as 别名]
# 或者: from tensorflow.contrib.slim.python.slim.nets.vgg import vgg_16 [as 别名]
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_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 variables_lib.get_model_variables()]
      self.assertSetEqual(set(model_variables), set(expected_names)) 

# 需要导入模块: from tensorflow.contrib.slim.python.slim.nets import vgg [as 别名]
# 或者: from tensorflow.contrib.slim.python.slim.nets.vgg import vgg_16 [as 别名]
def encoder_vgg(x, enc_final_size, reuse=False, scope_prefix='', hparams=None,
                is_training=True):
  """VGG network to use as encoder without the top few layers.

  Can be pretrained.

  Args:
    x: The image to encode. In the range 0 to 1.
    enc_final_size: The desired size of the encoding.
    reuse: To reuse in variable scope or not.
    scope_prefix: The prefix before the scope name.
    hparams: The python hparams.
    is_training: boolean value indicating if training is happening.

  Returns:
    The generated image.
  """
  with tf.variable_scope(scope_prefix + 'encoder', reuse=reuse):

    # Preprocess input
    x *= 256
    x = x - COLOR_NORMALIZATION_VECTOR

    with arg_scope(vgg.vgg_arg_scope()):
      # Padding because vgg_16 accepts images of size at least VGG_IMAGE_SIZE.
      x = tf.pad(x, [[0, 0], [0, VGG_IMAGE_SIZE - IMG_WIDTH],
                     [0, VGG_IMAGE_SIZE - IMG_HEIGHT], [0, 0]])
      _, end_points = vgg.vgg_16(
          x,
          num_classes=enc_final_size,
          is_training=is_training)
      pool5_key = [key for key in end_points.keys() if 'pool5' in key]
      assert len(pool5_key) == 1
      enc = end_points[pool5_key[0]]
      # Undoing padding.
      enc = tf.slice(enc, [0, 0, 0, 0], [-1, 2, 2, -1])

    enc_shape = enc.get_shape().as_list()
    enc_shape[0] = -1
    enc_size = enc_shape[1] * enc_shape[2] * enc_shape[3]

    enc_flat = tf.reshape(enc, (-1, enc_size))
    enc_flat = tf.nn.dropout(enc_flat, hparams.enc_keep_prob)

    enc_flat = tf.layers.dense(
        enc_flat,
        enc_final_size,
        kernel_initializer=tf.truncated_normal_initializer(stddev=1e-4,))

    if hparams.enc_pred_use_l2norm:
      enc_flat = tf.nn.l2_normalize(enc_flat, 1)

  return enc_flat