Python resnet_utils.Block方法代码示例

# 需要导入模块: from tensorflow.contrib.slim.python.slim.nets import resnet_utils [as 别名]
# 或者: from tensorflow.contrib.slim.python.slim.nets.resnet_utils import Block [as 别名]
def resnet_v1_block(scope, base_depth, num_units, stride):
  """Helper function for creating a resnet_v1 bottleneck block.

  Args:
    scope: The scope of the block.
    base_depth: The depth of the bottleneck layer for each unit.
    num_units: The number of units in the block.
    stride: The stride of the block, implemented as a stride in the last unit.
      All other units have stride=1.

  Returns:
    A resnet_v1 bottleneck block.
  """
  return resnet_utils.Block(scope, bottleneck, [{
      'depth': base_depth * 4,
      'depth_bottleneck': base_depth,
      'stride': 1
  }] * (num_units - 1) + [{
      'depth': base_depth * 4,
      'depth_bottleneck': base_depth,
      'stride': stride
  }]) 

# 需要导入模块: from tensorflow.contrib.slim.python.slim.nets import resnet_utils [as 别名]
# 或者: from tensorflow.contrib.slim.python.slim.nets.resnet_utils import Block [as 别名]
def resnet_v2_block(scope, base_depth, num_units, stride):
  """Helper function for creating a resnet_v2 bottleneck block.

  Args:
    scope: The scope of the block.
    base_depth: The depth of the bottleneck layer for each unit.
    num_units: The number of units in the block.
    stride: The stride of the block, implemented as a stride in the last unit.
      All other units have stride=1.

  Returns:
    A resnet_v2 bottleneck block.
  """
  return resnet_utils.Block(scope, bottleneck, [{
      'depth': base_depth * 4,
      'depth_bottleneck': base_depth,
      'stride': 1
  }] * (num_units - 1) + [{
      'depth': base_depth * 4,
      'depth_bottleneck': base_depth,
      'stride': stride
  }]) 

# 需要导入模块: from tensorflow.contrib.slim.python.slim.nets import resnet_utils [as 别名]
# 或者: from tensorflow.contrib.slim.python.slim.nets.resnet_utils import Block [as 别名]
def resnet_v2_50(inputs,
                 num_classes=None,
                 global_pool=True,
                 output_stride=None,
                 reuse=None,
                 scope='resnet_v2_50'):
  """ResNet-50 model of [1]. See resnet_v2() for arg and return description."""
  blocks = [
      resnet_utils.Block('block1', bottleneck,
                         [(256, 64, 1)] * 2 + [(256, 64, 2)]),
      resnet_utils.Block('block2', bottleneck,
                         [(512, 128, 1)] * 3 + [(512, 128, 2)]),
      resnet_utils.Block('block3', bottleneck,
                         [(1024, 256, 1)] * 5 + [(1024, 256, 2)]),
      resnet_utils.Block('block4', bottleneck, [(2048, 512, 1)] * 3)
  ]
  return resnet_v2(
      inputs,
      blocks,
      num_classes,
      global_pool,
      output_stride,
      include_root_block=True,
      reuse=reuse,
      scope=scope) 

# 需要导入模块: from tensorflow.contrib.slim.python.slim.nets import resnet_utils [as 别名]
# 或者: from tensorflow.contrib.slim.python.slim.nets.resnet_utils import Block [as 别名]
def resnet_v2_101(inputs,
                  num_classes=None,
                  global_pool=True,
                  output_stride=None,
                  reuse=None,
                  scope='resnet_v2_101'):
  """ResNet-101 model of [1]. See resnet_v2() for arg and return description."""
  blocks = [
      resnet_utils.Block('block1', bottleneck,
                         [(256, 64, 1)] * 2 + [(256, 64, 2)]),
      resnet_utils.Block('block2', bottleneck,
                         [(512, 128, 1)] * 3 + [(512, 128, 2)]),
      resnet_utils.Block('block3', bottleneck,
                         [(1024, 256, 1)] * 22 + [(1024, 256, 2)]),
      resnet_utils.Block('block4', bottleneck, [(2048, 512, 1)] * 3)
  ]
  return resnet_v2(
      inputs,
      blocks,
      num_classes,
      global_pool,
      output_stride,
      include_root_block=True,
      reuse=reuse,
      scope=scope) 

# 需要导入模块: from tensorflow.contrib.slim.python.slim.nets import resnet_utils [as 别名]
# 或者: from tensorflow.contrib.slim.python.slim.nets.resnet_utils import Block [as 别名]
def resnet_v2_152(inputs,
                  num_classes=None,
                  global_pool=True,
                  output_stride=None,
                  reuse=None,
                  scope='resnet_v2_152'):
  """ResNet-152 model of [1]. See resnet_v2() for arg and return description."""
  blocks = [
      resnet_utils.Block('block1', bottleneck,
                         [(256, 64, 1)] * 2 + [(256, 64, 2)]),
      resnet_utils.Block('block2', bottleneck,
                         [(512, 128, 1)] * 7 + [(512, 128, 2)]),
      resnet_utils.Block('block3', bottleneck,
                         [(1024, 256, 1)] * 35 + [(1024, 256, 2)]),
      resnet_utils.Block('block4', bottleneck, [(2048, 512, 1)] * 3)
  ]
  return resnet_v2(
      inputs,
      blocks,
      num_classes,
      global_pool,
      output_stride,
      include_root_block=True,
      reuse=reuse,
      scope=scope) 

# 需要导入模块: from tensorflow.contrib.slim.python.slim.nets import resnet_utils [as 别名]
# 或者: from tensorflow.contrib.slim.python.slim.nets.resnet_utils import Block [as 别名]
def resnet_v2_200(inputs,
                  num_classes=None,
                  global_pool=True,
                  output_stride=None,
                  reuse=None,
                  scope='resnet_v2_200'):
  """ResNet-200 model of [2]. See resnet_v2() for arg and return description."""
  blocks = [
      resnet_utils.Block('block1', bottleneck,
                         [(256, 64, 1)] * 2 + [(256, 64, 2)]),
      resnet_utils.Block('block2', bottleneck,
                         [(512, 128, 1)] * 23 + [(512, 128, 2)]),
      resnet_utils.Block('block3', bottleneck,
                         [(1024, 256, 1)] * 35 + [(1024, 256, 2)]),
      resnet_utils.Block('block4', bottleneck, [(2048, 512, 1)] * 3)
  ]
  return resnet_v2(
      inputs,
      blocks,
      num_classes,
      global_pool,
      output_stride,
      include_root_block=True,
      reuse=reuse,
      scope=scope) 

# 需要导入模块: from tensorflow.contrib.slim.python.slim.nets import resnet_utils [as 别名]
# 或者: from tensorflow.contrib.slim.python.slim.nets.resnet_utils import Block [as 别名]
def _resnet_small(self,
                    inputs,
                    num_classes=None,
                    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, global_pool,
                               output_stride, include_root_block, reuse, scope) 

# 需要导入模块: from tensorflow.contrib.slim.python.slim.nets import resnet_utils [as 别名]
# 或者: from tensorflow.contrib.slim.python.slim.nets.resnet_utils import Block [as 别名]
def resnet_v1_50(inputs,
                 num_classes=None,
                 global_pool=True,
                 output_stride=None,
                 reuse=None,
                 scope='resnet_v1_50'):
  """ResNet-50 model of [1]. See resnet_v1() for arg and return description."""
  blocks = [
      resnet_utils.Block('block1', bottleneck,
                         [(256, 64, 1)] * 2 + [(256, 64, 2)]),
      resnet_utils.Block('block2', bottleneck,
                         [(512, 128, 1)] * 3 + [(512, 128, 2)]),
      resnet_utils.Block('block3', bottleneck,
                         [(1024, 256, 1)] * 5 + [(1024, 256, 2)]),
      resnet_utils.Block('block4', bottleneck, [(2048, 512, 1)] * 3)
  ]
  return resnet_v1(
      inputs,
      blocks,
      num_classes,
      global_pool,
      output_stride,
      include_root_block=True,
      reuse=reuse,
      scope=scope) 

# 需要导入模块: from tensorflow.contrib.slim.python.slim.nets import resnet_utils [as 别名]
# 或者: from tensorflow.contrib.slim.python.slim.nets.resnet_utils import Block [as 别名]
def resnet_v1_101(inputs,
                  num_classes=None,
                  global_pool=True,
                  output_stride=None,
                  reuse=None,
                  scope='resnet_v1_101'):
  """ResNet-101 model of [1]. See resnet_v1() for arg and return description."""
  blocks = [
      resnet_utils.Block('block1', bottleneck,
                         [(256, 64, 1)] * 2 + [(256, 64, 2)]),
      resnet_utils.Block('block2', bottleneck,
                         [(512, 128, 1)] * 3 + [(512, 128, 2)]),
      resnet_utils.Block('block3', bottleneck,
                         [(1024, 256, 1)] * 22 + [(1024, 256, 2)]),
      resnet_utils.Block('block4', bottleneck, [(2048, 512, 1)] * 3)
  ]
  return resnet_v1(
      inputs,
      blocks,
      num_classes,
      global_pool,
      output_stride,
      include_root_block=True,
      reuse=reuse,
      scope=scope) 

# 需要导入模块: from tensorflow.contrib.slim.python.slim.nets import resnet_utils [as 别名]
# 或者: from tensorflow.contrib.slim.python.slim.nets.resnet_utils import Block [as 别名]
def resnet_v1_152(inputs,
                  num_classes=None,
                  global_pool=True,
                  output_stride=None,
                  reuse=None,
                  scope='resnet_v1_152'):
  """ResNet-152 model of [1]. See resnet_v1() for arg and return description."""
  blocks = [
      resnet_utils.Block('block1', bottleneck,
                         [(256, 64, 1)] * 2 + [(256, 64, 2)]),
      resnet_utils.Block('block2', bottleneck,
                         [(512, 128, 1)] * 7 + [(512, 128, 2)]),
      resnet_utils.Block('block3', bottleneck,
                         [(1024, 256, 1)] * 35 + [(1024, 256, 2)]),
      resnet_utils.Block('block4', bottleneck, [(2048, 512, 1)] * 3)
  ]
  return resnet_v1(
      inputs,
      blocks,
      num_classes,
      global_pool,
      output_stride,
      include_root_block=True,
      reuse=reuse,
      scope=scope) 

# 需要导入模块: from tensorflow.contrib.slim.python.slim.nets import resnet_utils [as 别名]
# 或者: from tensorflow.contrib.slim.python.slim.nets.resnet_utils import Block [as 别名]
def resnet_v1_200(inputs,
                  num_classes=None,
                  global_pool=True,
                  output_stride=None,
                  reuse=None,
                  scope='resnet_v1_200'):
  """ResNet-200 model of [2]. See resnet_v1() for arg and return description."""
  blocks = [
      resnet_utils.Block('block1', bottleneck,
                         [(256, 64, 1)] * 2 + [(256, 64, 2)]),
      resnet_utils.Block('block2', bottleneck,
                         [(512, 128, 1)] * 23 + [(512, 128, 2)]),
      resnet_utils.Block('block3', bottleneck,
                         [(1024, 256, 1)] * 35 + [(1024, 256, 2)]),
      resnet_utils.Block('block4', bottleneck, [(2048, 512, 1)] * 3)
  ]
  return resnet_v1(
      inputs,
      blocks,
      num_classes,
      global_pool,
      output_stride,
      include_root_block=True,
      reuse=reuse,
      scope=scope) 

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

# 需要导入模块: from tensorflow.contrib.slim.python.slim.nets import resnet_utils [as 别名]
# 或者: from tensorflow.contrib.slim.python.slim.nets.resnet_utils import Block [as 别名]
def testEndPointsV1(self):
    """Test the end points of a tiny v1 bottleneck network."""
    bottleneck = resnet_v1.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 arg_scope(resnet_utils.resnet_arg_scope()):
      _, end_points = self._resnet_plain(inputs, blocks, scope='tiny')
    expected = [
        'tiny/block1/unit_1/bottleneck_v1/shortcut',
        'tiny/block1/unit_1/bottleneck_v1/shortcut/BatchNorm',
        'tiny/block1/unit_1/bottleneck_v1/conv1',
        'tiny/block1/unit_1/bottleneck_v1/conv2',
        'tiny/block1/unit_1/bottleneck_v1/conv3',
        'tiny/block1/unit_1/bottleneck_v1/conv3/BatchNorm',
        'tiny/block1/unit_2/bottleneck_v1/conv1',
        'tiny/block1/unit_2/bottleneck_v1/conv2',
        'tiny/block1/unit_2/bottleneck_v1/conv3',
        'tiny/block1/unit_2/bottleneck_v1/conv3/BatchNorm',
        'tiny/block2/unit_1/bottleneck_v1/shortcut',
        'tiny/block2/unit_1/bottleneck_v1/shortcut/BatchNorm',
        'tiny/block2/unit_1/bottleneck_v1/conv1',
        'tiny/block2/unit_1/bottleneck_v1/conv2',
        'tiny/block2/unit_1/bottleneck_v1/conv3',
        'tiny/block2/unit_1/bottleneck_v1/conv3/BatchNorm',
        'tiny/block2/unit_2/bottleneck_v1/conv1',
        'tiny/block2/unit_2/bottleneck_v1/conv2',
        'tiny/block2/unit_2/bottleneck_v1/conv3',
        'tiny/block2/unit_2/bottleneck_v1/conv3/BatchNorm'
    ]
    self.assertItemsEqual(expected, end_points) 

# 需要导入模块: from tensorflow.contrib.slim.python.slim.nets import resnet_utils [as 别名]
# 或者: from tensorflow.contrib.slim.python.slim.nets.resnet_utils import Block [as 别名]
def _stack_blocks_nondense(self, net, blocks):
    """A simplified ResNet Block stacker without output stride control."""
    for block in blocks:
      with variable_scope.variable_scope(block.scope, 'block', [net]):
        for i, unit in enumerate(block.args):
          depth, depth_bottleneck, stride = unit
          with variable_scope.variable_scope('unit_%d' % (i + 1), values=[net]):
            net = block.unit_fn(
                net,
                depth=depth,
                depth_bottleneck=depth_bottleneck,
                stride=stride,
                rate=1)
    return net 

# 需要导入模块: from tensorflow.contrib.slim.python.slim.nets import resnet_utils [as 别名]
# 或者: from tensorflow.contrib.slim.python.slim.nets.resnet_utils import Block [as 别名]
def _decide_blocks(self):
    # choose different blocks for different number of layers
    if self._num_layers == 50:
      if tf.__version__ == '1.1.0':
        self._blocks     = [resnet_utils.Block('block1', resnet_v1.bottleneck,[(256,   64, 1)] * 2 + [(256,   64, 2)]),
                               resnet_utils.Block('block2', resnet_v1.bottleneck,[(512,  128, 1)] * 3 + [(512,  128, 2)]),
                               resnet_utils.Block('block3', resnet_v1.bottleneck,[(1024, 256, 1)] * 5 + [(1024, 256, 1)]),
                               resnet_utils.Block('block4', resnet_v1.bottleneck,[(2048, 512, 1)] * 3)]
      else:
        from tensorflow.contrib.slim.python.slim.nets.resnet_v1 import resnet_v1_block
        self._blocks = [resnet_v1_block('block1', base_depth=64, num_units=3, stride=2),
                       resnet_v1_block('block2', base_depth=128, num_units=4, stride=2),
                       resnet_v1_block('block3', base_depth=256, num_units=6, stride=1),
                       resnet_v1_block('block4', base_depth=512, num_units=3, stride=1)]

    elif self._num_layers == 101:
      self._blocks = [resnet_v1_block('block1', base_depth=64, num_units=3, stride=2),
                      resnet_v1_block('block2', base_depth=128, num_units=4, stride=2),
                      # use stride 1 for the last conv4 layer
                      resnet_v1_block('block3', base_depth=256, num_units=23, stride=1),
                      resnet_v1_block('block4', base_depth=512, num_units=3, stride=1)]

    elif self._num_layers == 152:
      self._blocks = [resnet_v1_block('block1', base_depth=64, num_units=3, stride=2),
                      resnet_v1_block('block2', base_depth=128, num_units=8, stride=2),
                      # use stride 1 for the last conv4 layer
                      resnet_v1_block('block3', base_depth=256, num_units=36, stride=1),
                      resnet_v1_block('block4', base_depth=512, num_units=3, stride=1)]

    else:
      # other numbers are not supported
      raise NotImplementedError