Python resnet_utils.subsample方法代码示例

# 需要导入模块: from tensorflow.contrib.slim.nets import resnet_utils [as 别名]
# 或者: from tensorflow.contrib.slim.nets.resnet_utils import subsample [as 别名]
def testAtrousFullyConvolutionalValues(self):
    """Verify dense feature extraction with atrous convolution."""
    nominal_stride = 32
    for output_stride in [4, 8, 16, 32, None]:
      with slim.arg_scope(resnet_utils.resnet_arg_scope(is_training=False)):
        with tf.Graph().as_default():
          with self.test_session() as sess:
            tf.set_random_seed(0)
            inputs = create_test_input(2, 81, 81, 3)
            # Dense feature extraction followed by subsampling.
            output, _ = self._resnet_small(inputs, None, global_pool=False,
                                           output_stride=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._resnet_small(inputs, None, global_pool=False)
            sess.run(tf.global_variables_initializer())
            self.assertAllClose(output.eval(), expected.eval(),
                                atol=1e-4, rtol=1e-4) 

# 需要导入模块: from tensorflow.contrib.slim.nets import resnet_utils [as 别名]
# 或者: from tensorflow.contrib.slim.nets.resnet_utils import subsample [as 别名]
def testAtrousFullyConvolutionalValues(self):
    """Verify dense feature extraction with atrous convolution."""
    nominal_stride = 32
    for output_stride in [4, 8, 16, 32, None]:
      with slim.arg_scope(xception.xception_arg_scope()):
        with tf.Graph().as_default():
          with self.test_session() as sess:
            tf.set_random_seed(0)
            inputs = create_test_input(2, 96, 97, 3)
            # Dense feature extraction followed by subsampling.
            output, _ = self._xception_small(
                inputs,
                None,
                is_training=False,
                global_pool=False,
                output_stride=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._xception_small(
                inputs,
                None,
                is_training=False,
                global_pool=False)
            sess.run(tf.global_variables_initializer())
            self.assertAllClose(output.eval(), expected.eval(),
                                atol=1e-5, rtol=1e-5) 

# 需要导入模块: from tensorflow.contrib.slim.nets import resnet_utils [as 别名]
# 或者: from tensorflow.contrib.slim.nets.resnet_utils import subsample [as 别名]
def testSubsampleThreeByThree(self):
    x = tf.reshape(tf.to_float(tf.range(9)), [1, 3, 3, 1])
    x = resnet_utils.subsample(x, 2)
    expected = tf.reshape(tf.constant([0, 2, 6, 8]), [1, 2, 2, 1])
    with self.test_session():
      self.assertAllClose(x.eval(), expected.eval()) 

# 需要导入模块: from tensorflow.contrib.slim.nets import resnet_utils [as 别名]
# 或者: from tensorflow.contrib.slim.nets.resnet_utils import subsample [as 别名]
def testSubsampleFourByFour(self):
    x = tf.reshape(tf.to_float(tf.range(16)), [1, 4, 4, 1])
    x = resnet_utils.subsample(x, 2)
    expected = tf.reshape(tf.constant([0, 2, 8, 10]), [1, 2, 2, 1])
    with self.test_session():
      self.assertAllClose(x.eval(), expected.eval()) 

# 需要导入模块: from tensorflow.contrib.slim.nets import resnet_utils [as 别名]
# 或者: from tensorflow.contrib.slim.nets.resnet_utils import subsample [as 别名]
def testConv2DSameEven(self):
    n, n2 = 4, 2

    # Input image.
    x = create_test_input(1, n, n, 1)

    # Convolution kernel.
    w = create_test_input(1, 3, 3, 1)
    w = tf.reshape(w, [3, 3, 1, 1])

    tf.get_variable('Conv/weights', initializer=w)
    tf.get_variable('Conv/biases', initializer=tf.zeros([1]))
    tf.get_variable_scope().reuse_variables()

    y1 = slim.conv2d(x, 1, [3, 3], stride=1, scope='Conv')
    y1_expected = tf.to_float([[14, 28, 43, 26],
                               [28, 48, 66, 37],
                               [43, 66, 84, 46],
                               [26, 37, 46, 22]])
    y1_expected = tf.reshape(y1_expected, [1, n, n, 1])

    y2 = resnet_utils.subsample(y1, 2)
    y2_expected = tf.to_float([[14, 43],
                               [43, 84]])
    y2_expected = tf.reshape(y2_expected, [1, n2, n2, 1])

    y3 = resnet_utils.conv2d_same(x, 1, 3, stride=2, scope='Conv')
    y3_expected = y2_expected

    y4 = slim.conv2d(x, 1, [3, 3], stride=2, scope='Conv')
    y4_expected = tf.to_float([[48, 37],
                               [37, 22]])
    y4_expected = tf.reshape(y4_expected, [1, n2, n2, 1])

    with self.test_session() as sess:
      sess.run(tf.global_variables_initializer())
      self.assertAllClose(y1.eval(), y1_expected.eval())
      self.assertAllClose(y2.eval(), y2_expected.eval())
      self.assertAllClose(y3.eval(), y3_expected.eval())
      self.assertAllClose(y4.eval(), y4_expected.eval()) 

# 需要导入模块: from tensorflow.contrib.slim.nets import resnet_utils [as 别名]
# 或者: from tensorflow.contrib.slim.nets.resnet_utils import subsample [as 别名]
def testConv2DSameOdd(self):
    n, n2 = 5, 3

    # Input image.
    x = create_test_input(1, n, n, 1)

    # Convolution kernel.
    w = create_test_input(1, 3, 3, 1)
    w = tf.reshape(w, [3, 3, 1, 1])

    tf.get_variable('Conv/weights', initializer=w)
    tf.get_variable('Conv/biases', initializer=tf.zeros([1]))
    tf.get_variable_scope().reuse_variables()

    y1 = slim.conv2d(x, 1, [3, 3], stride=1, scope='Conv')
    y1_expected = tf.to_float([[14, 28, 43, 58, 34],
                               [28, 48, 66, 84, 46],
                               [43, 66, 84, 102, 55],
                               [58, 84, 102, 120, 64],
                               [34, 46, 55, 64, 30]])
    y1_expected = tf.reshape(y1_expected, [1, n, n, 1])

    y2 = resnet_utils.subsample(y1, 2)
    y2_expected = tf.to_float([[14, 43, 34],
                               [43, 84, 55],
                               [34, 55, 30]])
    y2_expected = tf.reshape(y2_expected, [1, n2, n2, 1])

    y3 = resnet_utils.conv2d_same(x, 1, 3, stride=2, scope='Conv')
    y3_expected = y2_expected

    y4 = slim.conv2d(x, 1, [3, 3], stride=2, scope='Conv')
    y4_expected = y2_expected

    with self.test_session() as sess:
      sess.run(tf.global_variables_initializer())
      self.assertAllClose(y1.eval(), y1_expected.eval())
      self.assertAllClose(y2.eval(), y2_expected.eval())
      self.assertAllClose(y3.eval(), y3_expected.eval())
      self.assertAllClose(y4.eval(), y4_expected.eval()) 

# 需要导入模块: from tensorflow.contrib.slim.nets import resnet_utils [as 别名]
# 或者: from tensorflow.contrib.slim.nets.resnet_utils import subsample [as 别名]
def testAtrousFullyConvolutionalValues(self):
    """Verify dense feature extraction with atrous convolution."""
    nominal_stride = 32
    for output_stride in [4, 8, 16, 32, None]:
      with slim.arg_scope(resnet_utils.resnet_arg_scope()):
        with tf.Graph().as_default():
          with self.test_session() as sess:
            tf.set_random_seed(0)
            inputs = create_test_input(2, 81, 81, 3)
            # Dense feature extraction followed by subsampling.
            output, _ = self._resnet_small(inputs,
                                           None,
                                           is_training=False,
                                           global_pool=False,
                                           output_stride=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._resnet_small(inputs,
                                             None,
                                             is_training=False,
                                             global_pool=False)
            sess.run(tf.global_variables_initializer())
            self.assertAllClose(output.eval(), expected.eval(),
                                atol=1e-4, rtol=1e-4) 

# 需要导入模块: from tensorflow.contrib.slim.nets import resnet_utils [as 别名]
# 或者: from tensorflow.contrib.slim.nets.resnet_utils import subsample [as 别名]
def testSeparableConv2DSameWithInputEvenSize(self):
    n, n2 = 4, 2

    # Input image.
    x = create_test_input(1, n, n, 1)

    # Convolution kernel.
    dw = create_test_input(1, 3, 3, 1)
    dw = tf.reshape(dw, [3, 3, 1, 1])

    tf.get_variable('Conv/depthwise_weights', initializer=dw)
    tf.get_variable('Conv/pointwise_weights',
                    initializer=tf.ones([1, 1, 1, 1]))
    tf.get_variable('Conv/biases', initializer=tf.zeros([1]))
    tf.get_variable_scope().reuse_variables()

    y1 = slim.separable_conv2d(x, 1, [3, 3], depth_multiplier=1,
                               stride=1, scope='Conv')
    y1_expected = tf.to_float([[14, 28, 43, 26],
                               [28, 48, 66, 37],
                               [43, 66, 84, 46],
                               [26, 37, 46, 22]])
    y1_expected = tf.reshape(y1_expected, [1, n, n, 1])

    y2 = resnet_utils.subsample(y1, 2)
    y2_expected = tf.to_float([[14, 43],
                               [43, 84]])
    y2_expected = tf.reshape(y2_expected, [1, n2, n2, 1])

    y3 = xception.separable_conv2d_same(x, 1, 3, depth_multiplier=1,
                                        regularize_depthwise=True,
                                        stride=2, scope='Conv')
    y3_expected = y2_expected

    y4 = slim.separable_conv2d(x, 1, [3, 3], depth_multiplier=1,
                               stride=2, scope='Conv')
    y4_expected = tf.to_float([[48, 37],
                               [37, 22]])
    y4_expected = tf.reshape(y4_expected, [1, n2, n2, 1])

    with self.test_session() as sess:
      sess.run(tf.global_variables_initializer())
      self.assertAllClose(y1.eval(), y1_expected.eval())
      self.assertAllClose(y2.eval(), y2_expected.eval())
      self.assertAllClose(y3.eval(), y3_expected.eval())
      self.assertAllClose(y4.eval(), y4_expected.eval()) 

# 需要导入模块: from tensorflow.contrib.slim.nets import resnet_utils [as 别名]
# 或者: from tensorflow.contrib.slim.nets.resnet_utils import subsample [as 别名]
def testSeparableConv2DSameWithInputOddSize(self):
    n, n2 = 5, 3

    # Input image.
    x = create_test_input(1, n, n, 1)

    # Convolution kernel.
    dw = create_test_input(1, 3, 3, 1)
    dw = tf.reshape(dw, [3, 3, 1, 1])

    tf.get_variable('Conv/depthwise_weights', initializer=dw)
    tf.get_variable('Conv/pointwise_weights',
                    initializer=tf.ones([1, 1, 1, 1]))
    tf.get_variable('Conv/biases', initializer=tf.zeros([1]))
    tf.get_variable_scope().reuse_variables()

    y1 = slim.separable_conv2d(x, 1, [3, 3], depth_multiplier=1,
                               stride=1, scope='Conv')
    y1_expected = tf.to_float([[14, 28, 43, 58, 34],
                               [28, 48, 66, 84, 46],
                               [43, 66, 84, 102, 55],
                               [58, 84, 102, 120, 64],
                               [34, 46, 55, 64, 30]])
    y1_expected = tf.reshape(y1_expected, [1, n, n, 1])

    y2 = resnet_utils.subsample(y1, 2)
    y2_expected = tf.to_float([[14, 43, 34],
                               [43, 84, 55],
                               [34, 55, 30]])
    y2_expected = tf.reshape(y2_expected, [1, n2, n2, 1])

    y3 = xception.separable_conv2d_same(x, 1, 3, depth_multiplier=1,
                                        regularize_depthwise=True,
                                        stride=2, scope='Conv')
    y3_expected = y2_expected

    y4 = slim.separable_conv2d(x, 1, [3, 3], depth_multiplier=1,
                               stride=2, scope='Conv')
    y4_expected = y2_expected

    with self.test_session() as sess:
      sess.run(tf.global_variables_initializer())
      self.assertAllClose(y1.eval(), y1_expected.eval())
      self.assertAllClose(y2.eval(), y2_expected.eval())
      self.assertAllClose(y3.eval(), y3_expected.eval())
      self.assertAllClose(y4.eval(), y4_expected.eval()) 

# 需要导入模块: from tensorflow.contrib.slim.nets import resnet_utils [as 别名]
# 或者: from tensorflow.contrib.slim.nets.resnet_utils import subsample [as 别名]
def bottleneck(inputs, depth, depth_bottleneck, stride, rate=1,
               outputs_collections=None, scope=None):
  """Bottleneck residual unit variant with BN before convolutions.

  This is the full preactivation residual unit variant proposed in [2]. See
  Fig. 1(b) of [2] for its definition. Note that we use here the bottleneck
  variant which has an extra bottleneck layer.

  When putting together two consecutive ResNet blocks that use this unit, one
  should use stride = 2 in the last unit of the first block.

  Args:
    inputs: A tensor of size [batch, height, width, channels].
    depth: The depth of the ResNet unit output.
    depth_bottleneck: The depth of the bottleneck layers.
    stride: The ResNet unit's stride. Determines the amount of downsampling of
      the units output compared to its input.
    rate: An integer, rate for atrous convolution.
    outputs_collections: Collection to add the ResNet unit output.
    scope: Optional variable_scope.

  Returns:
    The ResNet unit's output.
  """
  with tf.variable_scope(scope, 'bottleneck_v2', [inputs]) as sc:
    depth_in = slim.utils.last_dimension(inputs.get_shape(), min_rank=4)
    preact = slim.batch_norm(inputs, activation_fn=tf.nn.relu, scope='preact')
    if depth == depth_in:
      shortcut = resnet_utils.subsample(inputs, stride, 'shortcut')
    else:
      shortcut = slim.conv2d(preact, depth, [1, 1], stride=stride,
                             normalizer_fn=None, activation_fn=None,
                             scope='shortcut')

    residual = slim.conv2d(preact, depth_bottleneck, [1, 1], stride=1,
                           scope='conv1')
    residual = resnet_utils.conv2d_same(residual, depth_bottleneck, 3, stride,
                                        rate=rate, scope='conv2')
    residual = slim.conv2d(residual, depth, [1, 1], stride=1,
                           normalizer_fn=None, activation_fn=None,
                           scope='conv3')

    output = shortcut + residual

    return slim.utils.collect_named_outputs(outputs_collections,
                                            sc.name,
                                            output) 

# 需要导入模块: from tensorflow.contrib.slim.nets import resnet_utils [as 别名]
# 或者: from tensorflow.contrib.slim.nets.resnet_utils import subsample [as 别名]
def bottleneck(inputs, depth, depth_bottleneck, stride, rate=1,
               outputs_collections=None, scope=None):
  """Bottleneck residual unit variant with BN after convolutions.

  This is the original residual unit proposed in [1]. See Fig. 1(a) of [2] for
  its definition. Note that we use here the bottleneck variant which has an
  extra bottleneck layer.

  When putting together two consecutive ResNet blocks that use this unit, one
  should use stride = 2 in the last unit of the first block.

  Args:
    inputs: A tensor of size [batch, height, width, channels].
    depth: The depth of the ResNet unit output.
    depth_bottleneck: The depth of the bottleneck layers.
    stride: The ResNet unit's stride. Determines the amount of downsampling of
      the units output compared to its input.
    rate: An integer, rate for atrous convolution.
    outputs_collections: Collection to add the ResNet unit output.
    scope: Optional variable_scope.

  Returns:
    The ResNet unit's output.
  """
  with tf.variable_scope(scope, 'bottleneck_v1', [inputs]) as sc:
    depth_in = slim.utils.last_dimension(inputs.get_shape(), min_rank=4)
    if depth == depth_in:
      shortcut = resnet_utils.subsample(inputs, stride, 'shortcut')
    else:
      shortcut = slim.conv2d(inputs, depth, [1, 1], stride=stride,
                             activation_fn=None, scope='shortcut')

    residual = slim.conv2d(inputs, depth_bottleneck, [1, 1], stride=1,
                           scope='conv1')
    residual = resnet_utils.conv2d_same(residual, depth_bottleneck, 3, stride,
                                        rate=rate, scope='conv2')
    residual = slim.conv2d(residual, depth, [1, 1], stride=1,
                           activation_fn=None, scope='conv3')

    output = tf.nn.relu(shortcut + residual)

    return slim.utils.collect_named_outputs(outputs_collections,
                                            sc.name,
                                            output) 

# 需要导入模块: from tensorflow.contrib.slim.nets import resnet_utils [as 别名]
# 或者: from tensorflow.contrib.slim.nets.resnet_utils import subsample [as 别名]
def bottleneck(inputs,
               depth,
               depth_bottleneck,
               stride,
               unit_rate=1,
               rate=1,
               outputs_collections=None,
               scope=None):
    """Bottleneck residual unit variant with BN after convolutions.

    This is the original residual unit proposed in [1]. See Fig. 1(a) of [2] for
    its definition. Note that we use here the bottleneck variant which has an
    extra bottleneck layer.

    When putting together two consecutive ResNet blocks that use this unit, one
    should use stride = 2 in the last unit of the first block.

    Args:
    inputs: A tensor of size [batch, height, width, channels].
    depth: The depth of the ResNet unit output.
    depth_bottleneck: The depth of the bottleneck layers.
    stride: The ResNet unit's stride. Determines the amount of downsampling of
      the units output compared to its input.
    unit_rate: An integer, unit rate for atrous convolution.
    rate: An integer, rate for atrous convolution.
    outputs_collections: Collection to add the ResNet unit output.
    scope: Optional variable_scope.

    Returns:
    The ResNet unit's output.
    """
    with tf.variable_scope(scope, 'bottleneck_v1', [inputs]) as sc:
        depth_in = slim.utils.last_dimension(inputs.get_shape(), min_rank=4)
        if depth == depth_in:
            shortcut = resnet_utils.subsample(inputs, stride, 'shortcut')
        else:
            shortcut = slim.conv2d(
                inputs,
                depth,
                [1, 1],
                stride=stride,
                activation_fn=None,
                scope='shortcut')

        residual = slim.conv2d(inputs, depth_bottleneck, [1, 1], stride=1,
                               scope='conv1')
        residual = resnet_utils.conv2d_same(residual, depth_bottleneck, 3, stride,
                                            rate=rate * unit_rate, scope='conv2')
        residual = slim.conv2d(residual, depth, [1, 1], stride=1,
                               activation_fn=None, scope='conv3')
        output = tf.nn.relu(shortcut + residual)

        return slim.utils.collect_named_outputs(outputs_collections,
                                                sc.name,
                                                output) 

# 需要导入模块: from tensorflow.contrib.slim.nets import resnet_utils [as 别名]
# 或者: from tensorflow.contrib.slim.nets.resnet_utils import subsample [as 别名]
def bottleneck(inputs,
               depth,
               depth_bottleneck,
               stride,
               unit_rate=1,
               rate=1,
               outputs_collections=None,
               scope=None):
  """Bottleneck residual unit variant with BN after convolutions.

  This is the original residual unit proposed in [1]. See Fig. 1(a) of [2] for
  its definition. Note that we use here the bottleneck variant which has an
  extra bottleneck layer.

  When putting together two consecutive ResNet blocks that use this unit, one
  should use stride = 2 in the last unit of the first block.

  Args:
    inputs: A tensor of size [batch, height, width, channels].
    depth: The depth of the ResNet unit output.
    depth_bottleneck: The depth of the bottleneck layers.
    stride: The ResNet unit's stride. Determines the amount of downsampling of
      the units output compared to its input.
    unit_rate: An integer, unit rate for atrous convolution.
    rate: An integer, rate for atrous convolution.
    outputs_collections: Collection to add the ResNet unit output.
    scope: Optional variable_scope.

  Returns:
    The ResNet unit's output.
  """
  with tf.variable_scope(scope, 'bottleneck_v1', [inputs]) as sc:
    depth_in = slim.utils.last_dimension(inputs.get_shape(), min_rank=4)
    if depth == depth_in:
      shortcut = resnet_utils.subsample(inputs, stride, 'shortcut')
    else:
      shortcut = slim.conv2d(
          inputs,
          depth,
          [1, 1],
          stride=stride,
          activation_fn=None,
          scope='shortcut')

    residual = slim.conv2d(inputs, depth_bottleneck, [1, 1], stride=1,
                           scope='conv1')
    residual = resnet_utils.conv2d_same(residual, depth_bottleneck, 3, stride,
                                        rate=rate*unit_rate, scope='conv2')
    residual = slim.conv2d(residual, depth, [1, 1], stride=1,
                           activation_fn=None, scope='conv3')
    output = tf.nn.relu(shortcut + residual)

    return slim.utils.collect_named_outputs(outputs_collections,
                                            sc.name,
                                            output)