Python nets.mobilenet.mobilenet.mobilenet() Examples

def training_scope(**kwargs):
  """Defines MobilenetV2 training scope.

  Usage:
     with tf.contrib.slim.arg_scope(mobilenet_v2.training_scope()):
       logits, endpoints = mobilenet_v2.mobilenet(input_tensor)

  with slim.

  Args:
    **kwargs: Passed to mobilenet.training_scope. The following parameters
    are supported:
      weight_decay- The weight decay to use for regularizing the model.
      stddev-  Standard deviation for initialization, if negative uses xavier.
      dropout_keep_prob- dropout keep probability
      bn_decay- decay for the batch norm moving averages.

  Returns:
    An `arg_scope` to use for the mobilenet v2 model.
  """
  return lib.training_scope(**kwargs) 

def training_scope(**kwargs):
    """Defines MobilenetV2 training scope.
    Usage:
       with tf.contrib.slim.arg_scope(mobilenet_v2.training_scope()):
         logits, endpoints = mobilenet_v2.mobilenet(input_tensor)
    with slim.
    Args:
      **kwargs: Passed to mobilenet.training_scope. The following parameters
      are supported:
        weight_decay- The weight decay to use for regularizing the model.
        stddev-  Standard deviation for initialization, if negative uses xavier.
        dropout_keep_prob- dropout keep probability
        bn_decay- decay for the batch norm moving averages.
    Returns:
      An `arg_scope` to use for the mobilenet v2 model.
    """
    return lib.training_scope(**kwargs) 

def training_scope(**kwargs):
  """Defines MobilenetV2 training scope.

  Usage:
     with tf.contrib.slim.arg_scope(mobilenet_v2.training_scope()):
       logits, endpoints = mobilenet_v2.mobilenet(input_tensor)

  with slim.

  Args:
    **kwargs: Passed to mobilenet.training_scope. The following parameters
    are supported:
      weight_decay- The weight decay to use for regularizing the model.
      stddev-  Standard deviation for initialization, if negative uses xavier.
      dropout_keep_prob- dropout keep probability
      bn_decay- decay for the batch norm moving averages.

  Returns:
    An `arg_scope` to use for the mobilenet v2 model.
  """
  return lib.training_scope(**kwargs) 

def training_scope(**kwargs):
  """Defines MobilenetV2 training scope.

  Usage:
     with tf.contrib.slim.arg_scope(mobilenet_v2.training_scope()):
       logits, endpoints = mobilenet_v2.mobilenet(input_tensor)

  with slim.

  Args:
    **kwargs: Passed to mobilenet.training_scope. The following parameters
    are supported:
      weight_decay- The weight decay to use for regularizing the model.
      stddev-  Standard deviation for initialization, if negative uses xavier.
      dropout_keep_prob- dropout keep probability
      bn_decay- decay for the batch norm moving averages.

  Returns:
    An `arg_scope` to use for the mobilenet v2 model.
  """
  return lib.training_scope(**kwargs) 

def testWithSplits(self):
    spec = copy.deepcopy(mobilenet_v2.V2_DEF)
    spec['overrides'] = {
        (ops.expanded_conv,): dict(split_expansion=2),
    }
    _, _ = mobilenet.mobilenet(
        tf.placeholder(tf.float32, (10, 224, 224, 16)), conv_defs=spec)
    num_convs = len(find_ops('Conv2D'))
    # All but 3 op has 3 conv operatore, the remainign 3 have one
    # and there is one unaccounted.
    self.assertEqual(num_convs, len(spec['spec']) * 3 - 5) 

def testCreationNoClasses(self):
    spec = copy.deepcopy(mobilenet_v2.V2_DEF)
    net, ep = mobilenet.mobilenet(
        tf.placeholder(tf.float32, (10, 224, 224, 16)), conv_defs=spec,
        num_classes=None)
    self.assertIs(net, ep['global_pool']) 

def testBatchNormScopeDoesNotHaveIsTrainingWhenItsSetToNone(self):
    sc = mobilenet.training_scope(is_training=None)
    self.assertNotIn('is_training', sc[slim.arg_scope_func_key(
        slim.batch_norm)]) 

def testWithOutputStride16AndExplicitPadding(self):
    tf.reset_default_graph()
    out, _ = mobilenet.mobilenet_base(
        tf.placeholder(tf.float32, (10, 224, 224, 16)),
        conv_defs=mobilenet_v2.V2_DEF,
        output_stride=16,
        use_explicit_padding=True)
    self.assertEqual(out.get_shape().as_list()[1:3], [14, 14]) 

def testWithOutputStride8AndExplicitPadding(self):
    tf.reset_default_graph()
    out, _ = mobilenet.mobilenet_base(
        tf.placeholder(tf.float32, (10, 224, 224, 16)),
        conv_defs=mobilenet_v2.V2_DEF,
        output_stride=8,
        use_explicit_padding=True,
        scope='MobilenetV2')
    self.assertEqual(out.get_shape().as_list()[1:3], [28, 28]) 

def testFineGrained(self):
    tf.reset_default_graph()
    # Verifies that depth_multiplier arg scope actually works
    # if no default min_depth is provided.

    mobilenet_v2.mobilenet(
        tf.placeholder(tf.float32, (10, 224, 224, 2)),
        conv_defs=mobilenet_v2.V2_DEF, depth_multiplier=0.01,
        finegrain_classification_mode=True)
    s = [op.outputs[0].get_shape().as_list()[-1] for op in find_ops('Conv2D')]
    s = set(s)
    # All convolutions will be 8->48, except for the last one.
    self.assertSameElements(s, [8, 48, 1001, 1280]) 

def testWithOutputStride16(self):
    tf.reset_default_graph()
    out, _ = mobilenet.mobilenet_base(
        tf.placeholder(tf.float32, (10, 224, 224, 16)),
        conv_defs=mobilenet_v2.V2_DEF,
        output_stride=16)
    self.assertEqual(out.get_shape().as_list()[1:3], [14, 14]) 

def testBatchNormScopeDoesHasIsTrainingWhenItsNotNone(self):
    sc = mobilenet.training_scope(is_training=False)
    self.assertIn('is_training', sc[slim.arg_scope_func_key(slim.batch_norm)])
    sc = mobilenet.training_scope(is_training=True)
    self.assertIn('is_training', sc[slim.arg_scope_func_key(slim.batch_norm)])
    sc = mobilenet.training_scope()
    self.assertIn('is_training', sc[slim.arg_scope_func_key(slim.batch_norm)]) 

def testBatchNormScopeDoesNotHaveIsTrainingWhenItsSetToNone(self):
    sc = mobilenet.training_scope(is_training=None)
    self.assertNotIn('is_training', sc[slim.arg_scope_func_key(
        slim.batch_norm)]) 

def testWithOutputStride16AndExplicitPadding(self):
    tf.reset_default_graph()
    out, _ = mobilenet.mobilenet_base(
        tf.placeholder(tf.float32, (10, 224, 224, 16)),
        conv_defs=mobilenet_v2.V2_DEF,
        output_stride=16,
        use_explicit_padding=True)
    self.assertEqual(out.get_shape().as_list()[1:3], [14, 14]) 

def testWithOutputStride8AndExplicitPadding(self):
    tf.reset_default_graph()
    out, _ = mobilenet.mobilenet_base(
        tf.placeholder(tf.float32, (10, 224, 224, 16)),
        conv_defs=mobilenet_v2.V2_DEF,
        output_stride=8,
        use_explicit_padding=True,
        scope='MobilenetV2')
    self.assertEqual(out.get_shape().as_list()[1:3], [28, 28]) 

def testMobilenetBase(self):
    tf.reset_default_graph()
    # Verifies that mobilenet_base returns pre-pooling layer.
    with slim.arg_scope((mobilenet.depth_multiplier,), min_depth=32):
      net, _ = mobilenet_v2.mobilenet_base(
          tf.placeholder(tf.float32, (10, 224, 224, 16)),
          conv_defs=mobilenet_v2.V2_DEF, depth_multiplier=0.1)
      self.assertEqual(net.get_shape().as_list(), [10, 7, 7, 128]) 

def testImageSizes(self):
    for input_size, output_size in [(224, 7), (192, 6), (160, 5),
                                    (128, 4), (96, 3)]:
      tf.reset_default_graph()
      _, ep = mobilenet_v2.mobilenet(
          tf.placeholder(tf.float32, (10, input_size, input_size, 3)))

      self.assertEqual(ep['layer_18/output'].get_shape().as_list()[1:3],
                       [output_size] * 2) 

def wrapped_partial(func, *args, **kwargs):
  partial_func = functools.partial(func, *args, **kwargs)
  functools.update_wrapper(partial_func, func)
  return partial_func


# Wrappers for mobilenet v2 with depth-multipliers. Be noticed that
# 'finegrain_classification_mode' is set to True, which means the embedding
# layer will not be shrinked when given a depth-multiplier < 1.0. 

def testDivisibleByWithArgScope(self):
    tf.reset_default_graph()
    # Verifies that depth_multiplier arg scope actually works
    # if no default min_depth is provided.
    with slim.arg_scope((mobilenet.depth_multiplier,), min_depth=32):
      mobilenet_v2.mobilenet(
          tf.placeholder(tf.float32, (10, 224, 224, 2)),
          conv_defs=mobilenet_v2.V2_DEF, depth_multiplier=0.1)
      s = [op.outputs[0].get_shape().as_list()[-1] for op in find_ops('Conv2D')]
      s = set(s)
      self.assertSameElements(s, [32, 192, 128, 1001]) 

def testDivisibleBy(self):
    tf.reset_default_graph()
    mobilenet_v2.mobilenet(
        tf.placeholder(tf.float32, (10, 224, 224, 16)),
        conv_defs=mobilenet_v2.V2_DEF,
        divisible_by=16,
        min_depth=32)
    s = [op.outputs[0].get_shape().as_list()[-1] for op in find_ops('Conv2D')]
    s = set(s)
    self.assertSameElements([32, 64, 96, 160, 192, 320, 384, 576, 960, 1280,
                             1001], s) 

def testWithOutputStride8(self):
    out, _ = mobilenet.mobilenet_base(
        tf.placeholder(tf.float32, (10, 224, 224, 16)),
        conv_defs=mobilenet_v2.V2_DEF,
        output_stride=8,
        scope='MobilenetV2')
    self.assertEqual(out.get_shape().as_list()[1:3], [28, 28]) 

def testImageSizes(self):
    for input_size, output_size in [(224, 7), (192, 6), (160, 5),
                                    (128, 4), (96, 3)]:
      tf.reset_default_graph()
      _, ep = mobilenet_v2.mobilenet(
          tf.placeholder(tf.float32, (10, input_size, input_size, 3)))

      self.assertEqual(ep['layer_18/output'].get_shape().as_list()[1:3],
                       [output_size] * 2) 

def testCreationNoClasses(self):
    spec = copy.deepcopy(mobilenet_v2.V2_DEF)
    net, ep = mobilenet.mobilenet(
        tf.placeholder(tf.float32, (10, 224, 224, 16)), conv_defs=spec,
        num_classes=None)
    self.assertIs(net, ep['global_pool']) 

def testCreation(self):
    spec = dict(mobilenet_v2.V2_DEF)
    _, ep = mobilenet.mobilenet(
        tf.placeholder(tf.float32, (10, 224, 224, 16)), conv_defs=spec)
    num_convs = len(find_ops('Conv2D'))

    # This is mostly a sanity test. No deep reason for these particular
    # constants.
    #
    # All but first 2 and last one have  two convolutions, and there is one
    # extra conv that is not in the spec. (logits)
    self.assertEqual(num_convs, len(spec['spec']) * 2 - 2)
    # Check that depthwise are exposed.
    for i in range(2, 17):
      self.assertIn('layer_%d/depthwise_output' % i, ep) 

def mobilenet_base(input_tensor, depth_multiplier=1.0, **kwargs):
  """Creates base of the mobilenet (no pooling and no logits) ."""
  return mobilenet(input_tensor,
                   depth_multiplier=depth_multiplier,
                   base_only=True, **kwargs) 

def testWithOutputStride8AndExplicitPadding(self):
    tf.reset_default_graph()
    out, _ = mobilenet.mobilenet_base(
        tf.placeholder(tf.float32, (10, 224, 224, 16)),
        conv_defs=mobilenet_v2.V2_DEF,
        output_stride=8,
        use_explicit_padding=True,
        scope='MobilenetV2')
    self.assertEqual(out.get_shape().as_list()[1:3], [28, 28]) 

def testWithOutputStride16(self):
    tf.reset_default_graph()
    out, _ = mobilenet.mobilenet_base(
        tf.placeholder(tf.float32, (10, 224, 224, 16)),
        conv_defs=mobilenet_v2.V2_DEF,
        output_stride=16)
    self.assertEqual(out.get_shape().as_list()[1:3], [14, 14]) 

def testMobilenetBase(self):
    tf.reset_default_graph()
    # Verifies that mobilenet_base returns pre-pooling layer.
    with slim.arg_scope((mobilenet.depth_multiplier,), min_depth=32):
      net, _ = mobilenet_v2.mobilenet_base(
          tf.placeholder(tf.float32, (10, 224, 224, 16)),
          conv_defs=mobilenet_v2.V2_DEF, depth_multiplier=0.1)
      self.assertEqual(net.get_shape().as_list(), [10, 7, 7, 128]) 

def testFineGrained(self):
    tf.reset_default_graph()
    # Verifies that depth_multiplier arg scope actually works
    # if no default min_depth is provided.

    mobilenet_v2.mobilenet(
        tf.placeholder(tf.float32, (10, 224, 224, 2)),
        conv_defs=mobilenet_v2.V2_DEF, depth_multiplier=0.01,
        finegrain_classification_mode=True)
    s = [op.outputs[0].get_shape().as_list()[-1] for op in find_ops('Conv2D')]
    s = set(s)
    # All convolutions will be 8->48, except for the last one.
    self.assertSameElements(s, [8, 48, 1001, 1280]) 

def testDivisibleByWithArgScope(self):
    tf.reset_default_graph()
    # Verifies that depth_multiplier arg scope actually works
    # if no default min_depth is provided.
    with slim.arg_scope((mobilenet.depth_multiplier,), min_depth=32):
      mobilenet_v2.mobilenet(
          tf.placeholder(tf.float32, (10, 224, 224, 2)),
          conv_defs=mobilenet_v2.V2_DEF, depth_multiplier=0.1)
      s = [op.outputs[0].get_shape().as_list()[-1] for op in find_ops('Conv2D')]
      s = set(s)
      self.assertSameElements(s, [32, 192, 128, 1001])