Python mobilenet_v2.mobilenet方法代码示例

# 需要导入模块: from nets.mobilenet import mobilenet_v2 [as 别名]
# 或者: from nets.mobilenet.mobilenet_v2 import mobilenet [as 别名]
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) 

# 需要导入模块: from nets.mobilenet import mobilenet_v2 [as 别名]
# 或者: from nets.mobilenet.mobilenet_v2 import mobilenet [as 别名]
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']) 

# 需要导入模块: from nets.mobilenet import mobilenet_v2 [as 别名]
# 或者: from nets.mobilenet.mobilenet_v2 import mobilenet [as 别名]
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) 

# 需要导入模块: from nets.mobilenet import mobilenet_v2 [as 别名]
# 或者: from nets.mobilenet.mobilenet_v2 import mobilenet [as 别名]
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) 

# 需要导入模块: from nets.mobilenet import mobilenet_v2 [as 别名]
# 或者: from nets.mobilenet.mobilenet_v2 import mobilenet [as 别名]
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]) 

# 需要导入模块: from nets.mobilenet import mobilenet_v2 [as 别名]
# 或者: from nets.mobilenet.mobilenet_v2 import mobilenet [as 别名]
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]) 

# 需要导入模块: from nets.mobilenet import mobilenet_v2 [as 别名]
# 或者: from nets.mobilenet.mobilenet_v2 import mobilenet [as 别名]
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]) 

# 需要导入模块: from nets.mobilenet import mobilenet_v2 [as 别名]
# 或者: from nets.mobilenet.mobilenet_v2 import mobilenet [as 别名]
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]) 

# 需要导入模块: from nets.mobilenet import mobilenet_v2 [as 别名]
# 或者: from nets.mobilenet.mobilenet_v2 import mobilenet [as 别名]
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]) 

# 需要导入模块: from nets.mobilenet import mobilenet_v2 [as 别名]
# 或者: from nets.mobilenet.mobilenet_v2 import mobilenet [as 别名]
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]) 

# 需要导入模块: from nets.mobilenet import mobilenet_v2 [as 别名]
# 或者: from nets.mobilenet.mobilenet_v2 import mobilenet [as 别名]
def testBatchNormScopeDoesNotHaveIsTrainingWhenItsSetToNone(self):
    sc = mobilenet.training_scope(is_training=None)
    self.assertNotIn('is_training', sc[slim.arg_scope_func_key(
        slim.batch_norm)]) 

# 需要导入模块: from nets.mobilenet import mobilenet_v2 [as 别名]
# 或者: from nets.mobilenet.mobilenet_v2 import mobilenet [as 别名]
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)])