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Python resnet_v2.bottleneck方法代码示例

本文整理汇总了Python中nets.resnet_v2.bottleneck方法的典型用法代码示例。如果您正苦于以下问题:Python resnet_v2.bottleneck方法的具体用法?Python resnet_v2.bottleneck怎么用?Python resnet_v2.bottleneck使用的例子?那么, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在nets.resnet_v2的用法示例。


在下文中一共展示了resnet_v2.bottleneck方法的5个代码示例,这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞,您的评价将有助于系统推荐出更棒的Python代码示例。

示例1: testEndPointsV2

# 需要导入模块: from nets import resnet_v2 [as 别名]
# 或者: from nets.resnet_v2 import bottleneck [as 别名]
def testEndPointsV2(self):
    """Test the end points of a tiny v2 bottleneck network."""
    bottleneck = resnet_v2.bottleneck
    blocks = [resnet_utils.Block('block1', bottleneck, [(4, 1, 1), (4, 1, 2)]),
              resnet_utils.Block('block2', bottleneck, [(8, 2, 1), (8, 2, 1)])]
    inputs = create_test_input(2, 32, 16, 3)
    with slim.arg_scope(resnet_utils.resnet_arg_scope()):
      _, end_points = self._resnet_plain(inputs, blocks, scope='tiny')
    expected = [
        'tiny/block1/unit_1/bottleneck_v2/shortcut',
        'tiny/block1/unit_1/bottleneck_v2/conv1',
        'tiny/block1/unit_1/bottleneck_v2/conv2',
        'tiny/block1/unit_1/bottleneck_v2/conv3',
        'tiny/block1/unit_2/bottleneck_v2/conv1',
        'tiny/block1/unit_2/bottleneck_v2/conv2',
        'tiny/block1/unit_2/bottleneck_v2/conv3',
        'tiny/block2/unit_1/bottleneck_v2/shortcut',
        'tiny/block2/unit_1/bottleneck_v2/conv1',
        'tiny/block2/unit_1/bottleneck_v2/conv2',
        'tiny/block2/unit_1/bottleneck_v2/conv3',
        'tiny/block2/unit_2/bottleneck_v2/conv1',
        'tiny/block2/unit_2/bottleneck_v2/conv2',
        'tiny/block2/unit_2/bottleneck_v2/conv3']
    self.assertItemsEqual(expected, end_points) 
开发者ID:wenwei202,项目名称:terngrad,代码行数:26,代码来源:resnet_v2_test.py

示例2: testAtrousValuesBottleneck

# 需要导入模块: from nets import resnet_v2 [as 别名]
# 或者: from nets.resnet_v2 import bottleneck [as 别名]
def testAtrousValuesBottleneck(self):
    self._atrousValues(resnet_v2.bottleneck) 
开发者ID:wenwei202,项目名称:terngrad,代码行数:4,代码来源:resnet_v2_test.py

示例3: _resnet_small

# 需要导入模块: from nets import resnet_v2 [as 别名]
# 或者: from nets.resnet_v2 import bottleneck [as 别名]
def _resnet_small(self,
                    inputs,
                    num_classes=None,
                    is_training=True,
                    global_pool=True,
                    output_stride=None,
                    include_root_block=True,
                    reuse=None,
                    scope='resnet_v2_small'):
    """A shallow and thin ResNet v2 for faster tests."""
    bottleneck = resnet_v2.bottleneck
    blocks = [
        resnet_utils.Block(
            'block1', bottleneck, [(4, 1, 1)] * 2 + [(4, 1, 2)]),
        resnet_utils.Block(
            'block2', bottleneck, [(8, 2, 1)] * 2 + [(8, 2, 2)]),
        resnet_utils.Block(
            'block3', bottleneck, [(16, 4, 1)] * 2 + [(16, 4, 2)]),
        resnet_utils.Block(
            'block4', bottleneck, [(32, 8, 1)] * 2)]
    return resnet_v2.resnet_v2(inputs, blocks, num_classes,
                               is_training=is_training,
                               global_pool=global_pool,
                               output_stride=output_stride,
                               include_root_block=include_root_block,
                               reuse=reuse,
                               scope=scope) 
开发者ID:wenwei202,项目名称:terngrad,代码行数:29,代码来源:resnet_v2_test.py

示例4: _atrousValues

# 需要导入模块: from nets import resnet_v2 [as 别名]
# 或者: from nets.resnet_v2 import bottleneck [as 别名]
def _atrousValues(self, bottleneck):
    """Verify the values of dense feature extraction by atrous convolution.

    Make sure that dense feature extraction by stack_blocks_dense() followed by
    subsampling gives identical results to feature extraction at the nominal
    network output stride using the simple self._stack_blocks_nondense() above.

    Args:
      bottleneck: The bottleneck function.
    """
    blocks = [
        resnet_utils.Block('block1', bottleneck, [(4, 1, 1), (4, 1, 2)]),
        resnet_utils.Block('block2', bottleneck, [(8, 2, 1), (8, 2, 2)]),
        resnet_utils.Block('block3', bottleneck, [(16, 4, 1), (16, 4, 2)]),
        resnet_utils.Block('block4', bottleneck, [(32, 8, 1), (32, 8, 1)])
    ]
    nominal_stride = 8

    # Test both odd and even input dimensions.
    height = 30
    width = 31
    with slim.arg_scope(resnet_utils.resnet_arg_scope()):
      with slim.arg_scope([slim.batch_norm], is_training=False):
        for output_stride in [1, 2, 4, 8, None]:
          with tf.Graph().as_default():
            with self.test_session() as sess:
              tf.set_random_seed(0)
              inputs = create_test_input(1, height, width, 3)
              # Dense feature extraction followed by subsampling.
              output = resnet_utils.stack_blocks_dense(inputs,
                                                       blocks,
                                                       output_stride)
              if output_stride is None:
                factor = 1
              else:
                factor = nominal_stride // output_stride

              output = resnet_utils.subsample(output, factor)
              # Make the two networks use the same weights.
              tf.get_variable_scope().reuse_variables()
              # Feature extraction at the nominal network rate.
              expected = self._stack_blocks_nondense(inputs, blocks)
              sess.run(tf.global_variables_initializer())
              output, expected = sess.run([output, expected])
              self.assertAllClose(output, expected, atol=1e-4, rtol=1e-4) 
开发者ID:wenwei202,项目名称:terngrad,代码行数:47,代码来源:resnet_v2_test.py

示例5: _atrousValues

# 需要导入模块: from nets import resnet_v2 [as 别名]
# 或者: from nets.resnet_v2 import bottleneck [as 别名]
def _atrousValues(self, bottleneck):
    """Verify the values of dense feature extraction by atrous convolution.

    Make sure that dense feature extraction by stack_blocks_dense() followed by
    subsampling gives identical results to feature extraction at the nominal
    network output stride using the simple self._stack_blocks_nondense() above.

    Args:
      bottleneck: The bottleneck function.
    """
    blocks = [
        resnet_utils.Block('block1', bottleneck, [(4, 1, 1), (4, 1, 2)]),
        resnet_utils.Block('block2', bottleneck, [(8, 2, 1), (8, 2, 2)]),
        resnet_utils.Block('block3', bottleneck, [(16, 4, 1), (16, 4, 2)]),
        resnet_utils.Block('block4', bottleneck, [(32, 8, 1), (32, 8, 1)])
    ]
    nominal_stride = 8

    # Test both odd and even input dimensions.
    height = 30
    width = 31
    with slim.arg_scope(resnet_utils.resnet_arg_scope()):
      with slim.arg_scope([slim.batch_norm], is_training=False):
        for output_stride in [1, 2, 4, 8, None]:
          with tf.Graph().as_default():
            with self.test_session() as sess:
              tf.set_random_seed(0)
              inputs = create_test_input(1, height, width, 3)
              # Dense feature extraction followed by subsampling.
              output = resnet_utils.stack_blocks_dense(inputs,
                                                       blocks,
                                                       output_stride)
              if output_stride is None:
                factor = 1
              else:
                factor = nominal_stride // output_stride

              output = resnet_utils.subsample(output, factor)
              # Make the two networks use the same weights.
              tf.get_variable_scope().reuse_variables()
              # Feature extraction at the nominal network rate.
              expected = self._stack_blocks_nondense(inputs, blocks)
              sess.run(tf.initialize_all_variables())
              output, expected = sess.run([output, expected])
              self.assertAllClose(output, expected, atol=1e-4, rtol=1e-4) 
开发者ID:coderSkyChen,项目名称:Action_Recognition_Zoo,代码行数:47,代码来源:resnet_v2_test.py


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