Python nets.mobilenet.mobilenet_v2.V2_DEF Examples

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Example #1
Source File: mobilenet_v2_test.py    From edafa with MIT License 5 votes vote down vote up 
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])
Example #2
Source File: mobilenet_v2_test.py    From CBAM-tensorflow-slim with MIT License 5 votes vote down vote up 
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)
Example #3
Source File: mobilenet_v2_test.py    From RetinaNet_Tensorflow_Rotation with MIT License 5 votes vote down vote up 
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])
Example #4
Source File: mobilenet_v2_test.py    From CBAM-tensorflow-slim with MIT License 5 votes vote down vote up 
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])
Example #5
Source File: mobilenet_v2_test.py    From CBAM-tensorflow-slim with MIT License 5 votes vote down vote up 
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])
Example #6
Source File: mobilenet_v2_test.py    From CBAM-tensorflow-slim with MIT License 5 votes vote down vote up 
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])
Example #7
Source File: mobilenet_v2_test.py    From CBAM-tensorflow-slim with MIT License 5 votes vote down vote up 
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)
Example #8
Source File: mobilenet_v2_test.py    From CBAM-tensorflow-slim with MIT License 5 votes vote down vote up 
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'])
Example #9
Source File: mobilenet_v2_test.py    From CBAM-tensorflow-slim with MIT License 5 votes vote down vote up 
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)
Example #10
Source File: mobilenet_v2_test.py    From RetinaNet_Tensorflow_Rotation with MIT License 5 votes vote down vote up 
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])
Example #11
Source File: mobilenet_v2_test.py    From CBAM-tensorflow-slim with MIT License 5 votes vote down vote up 
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])
Example #12
Source File: mobilenet_v2_test.py    From edafa with MIT License 5 votes vote down vote up 
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])
Example #13
Source File: mobilenet_v2_test.py    From edafa with MIT License 5 votes vote down vote up 
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])
Example #14
Source File: mobilenet_v2_test.py    From edafa with MIT License 5 votes vote down vote up 
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])
Example #15
Source File: mobilenet_v2_test.py    From edafa with MIT License 5 votes vote down vote up 
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)
Example #16
Source File: mobilenet_v2_test.py    From edafa with MIT License 5 votes vote down vote up 
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])
Example #17
Source File: mobilenet_v2_test.py    From edafa with MIT License 5 votes vote down vote up 
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)
Example #18
Source File: mobilenet_v2_test.py    From edafa with MIT License 5 votes vote down vote up 
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'])
Example #19
Source File: mobilenet_v2_test.py    From edafa with MIT License 5 votes vote down vote up 
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)
Example #20
Source File: feature_extractor.py    From mobile-segmentation with Apache License 2.0 5 votes vote down vote up 
def _mobilenet_v2(net,
                  depth_multiplier,
                  output_stride,
                  reuse=None,
                  scope=None,
                  final_endpoint=None):
    """Auxiliary function to add support for 'reuse' to mobilenet_v2.

    Args:
      net: Input tensor of shape [batch_size, height, width, channels].
      depth_multiplier: Float multiplier for the depth (number of channels)
        for all convolution ops. The value must be greater than zero. Typical
        usage will be to set this value in (0, 1) to reduce the number of
        parameters or computation cost of the model.
      output_stride: An integer that specifies the requested ratio of input to
        output spatial resolution. If not None, then we invoke atrous convolution
        if necessary to prevent the network from reducing the spatial resolution
        of the activation maps. Allowed values are 8 (accurate fully convolutional
        mode), 16 (fast fully convolutional mode), 32 (classification mode).
      reuse: Reuse model variables.
      scope: Optional variable scope.
      final_endpoint: The endpoint to construct the network up to.

    Returns:
      Features extracted by MobileNetv2.
    """
    with tf.variable_scope(
            scope, 'MobilenetV2', [net], reuse=reuse) as scope:
        return mobilenet_v2.mobilenet_base(
            net,
            conv_defs=mobilenet_v2.V2_DEF,
            depth_multiplier=depth_multiplier,
            min_depth=8 if depth_multiplier == 1.0 else 1,
            divisible_by=8 if depth_multiplier == 1.0 else 1,
            final_endpoint=final_endpoint or _MOBILENET_V2_FINAL_ENDPOINT,
            output_stride=output_stride,
            scope=scope)


# A map from network name to network function.
Example #21
Source File: mobilenet_v2_test.py    From CVTron with Apache License 2.0 5 votes vote down vote up 
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])
Example #22
Source File: mobilenet_v2_test.py    From CVTron with Apache License 2.0 5 votes vote down vote up 
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])
Example #23
Source File: mobilenet_v2_test.py    From CVTron with Apache License 2.0 5 votes vote down vote up 
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])
Example #24
Source File: mobilenet_v2_test.py    From CVTron with Apache License 2.0 5 votes vote down vote up 
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])
Example #25
Source File: mobilenet_v2_test.py    From CVTron with Apache License 2.0 5 votes vote down vote up 
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])
Example #26
Source File: mobilenet_v2_test.py    From CVTron with Apache License 2.0 5 votes vote down vote up 
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)
Example #27
Source File: mobilenet_v2_test.py    From CVTron with Apache License 2.0 5 votes vote down vote up 
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])
Example #28
Source File: mobilenet_v2_test.py    From CVTron with Apache License 2.0 5 votes vote down vote up 
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)
Example #29
Source File: mobilenet_v2_test.py    From CVTron with Apache License 2.0 5 votes vote down vote up 
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'])
Example #30
Source File: mobilenet_v2_test.py    From CVTron with Apache License 2.0 5 votes vote down vote up 
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)