Python resnet_utils.resnet_arg_scope函数代码示例

  def _extract_box_classifier_features(self, proposal_feature_maps, scope):
    """Extracts second stage box classifier features.

    Args:
      proposal_feature_maps: A 4-D float tensor with shape
        [batch_size * self.max_num_proposals, crop_height, crop_width, depth]
        representing the feature map cropped to each proposal.
      scope: A scope name (unused).

    Returns:
      proposal_classifier_features: A 4-D float tensor with shape
        [batch_size * self.max_num_proposals, height, width, depth]
        representing box classifier features for each proposal.
    """
    with tf.variable_scope(self._architecture, reuse=self._reuse_weights):
      with slim.arg_scope(
          resnet_utils.resnet_arg_scope(
              batch_norm_epsilon=1e-5,
              batch_norm_scale=True,
              weight_decay=self._weight_decay)):
        with slim.arg_scope([slim.batch_norm],
                            is_training=self._train_batch_norm):
          blocks = [
              resnet_utils.Block('block4', resnet_v1.bottleneck, [{
                  'depth': 2048,
                  'depth_bottleneck': 512,
                  'stride': 1
              }] * 3)
          ]
          proposal_classifier_features = resnet_utils.stack_blocks_dense(
              proposal_feature_maps, blocks)
    return proposal_classifier_features

 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)

 def testEndPointsV2(self):
   """Test the end points of a tiny v2 bottleneck network."""
   blocks = [
       resnet_v2.resnet_v2_block(
           'block1', base_depth=1, num_units=2, stride=2),
       resnet_v2.resnet_v2_block(
           'block2', base_depth=2, num_units=2, stride=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)

 def testClassificationEndPoints(self):
   global_pool = True
   num_classes = 10
   inputs = create_test_input(2, 224, 224, 3)
   with slim.arg_scope(resnet_utils.resnet_arg_scope()):
     logits, end_points = self._resnet_small(inputs, num_classes,
                                             global_pool=global_pool,
                                             scope='resnet')
   self.assertTrue(logits.op.name.startswith('resnet/logits'))
   self.assertListEqual(logits.get_shape().as_list(), [2, 1, 1, num_classes])
   self.assertTrue('predictions' in end_points)
   self.assertListEqual(end_points['predictions'].get_shape().as_list(),
                        [2, 1, 1, num_classes])

 def testFullyConvolutionalUnknownHeightWidth(self):
   batch = 2
   height, width = 65, 65
   global_pool = False
   inputs = create_test_input(batch, None, None, 3)
   with slim.arg_scope(resnet_utils.resnet_arg_scope()):
     output, _ = self._resnet_small(inputs, None, global_pool=global_pool)
   self.assertListEqual(output.get_shape().as_list(),
                        [batch, None, None, 32])
   images = create_test_input(batch, height, width, 3)
   with self.test_session() as sess:
     sess.run(tf.global_variables_initializer())
     output = sess.run(output, {inputs: images.eval()})
     self.assertEqual(output.shape, (batch, 3, 3, 32))

  def _extract_proposal_features(self, preprocessed_inputs, scope):
    """Extracts first stage RPN features.

    Args:
      preprocessed_inputs: A [batch, height, width, channels] float32 tensor
        representing a batch of images.
      scope: A scope name.

    Returns:
      rpn_feature_map: A tensor with shape [batch, height, width, depth]
      activations: A dictionary mapping feature extractor tensor names to
        tensors

    Raises:
      InvalidArgumentError: If the spatial size of `preprocessed_inputs`
        (height or width) is less than 33.
      ValueError: If the created network is missing the required activation.
    """
    if len(preprocessed_inputs.get_shape().as_list()) != 4:
      raise ValueError('`preprocessed_inputs` must be 4 dimensional, got a '
                       'tensor of shape %s' % preprocessed_inputs.get_shape())
    shape_assert = tf.Assert(
        tf.logical_and(
            tf.greater_equal(tf.shape(preprocessed_inputs)[1], 33),
            tf.greater_equal(tf.shape(preprocessed_inputs)[2], 33)),
        ['image size must at least be 33 in both height and width.'])

    with tf.control_dependencies([shape_assert]):
      # Disables batchnorm for fine-tuning with smaller batch sizes.
      # TODO(chensun): Figure out if it is needed when image
      # batch size is bigger.
      with slim.arg_scope(
          resnet_utils.resnet_arg_scope(
              batch_norm_epsilon=1e-5,
              batch_norm_scale=True,
              weight_decay=self._weight_decay)):
        with tf.variable_scope(
            self._architecture, reuse=self._reuse_weights) as var_scope:
          _, activations = self._resnet_model(
              preprocessed_inputs,
              num_classes=None,
              is_training=self._train_batch_norm,
              global_pool=False,
              output_stride=self._first_stage_features_stride,
              spatial_squeeze=False,
              scope=var_scope)

    handle = scope + '/%s/block3' % self._architecture
    return activations[handle], activations

 def testFullyConvolutionalEndpointShapes(self):
   global_pool = False
   num_classes = 10
   inputs = create_test_input(2, 321, 321, 3)
   with slim.arg_scope(resnet_utils.resnet_arg_scope()):
     _, end_points = self._resnet_small(inputs, num_classes,
                                        global_pool=global_pool,
                                        scope='resnet')
     endpoint_to_shape = {
         'resnet/block1': [2, 41, 41, 4],
         'resnet/block2': [2, 21, 21, 8],
         'resnet/block3': [2, 11, 11, 16],
         'resnet/block4': [2, 11, 11, 32]}
     for endpoint in endpoint_to_shape:
       shape = endpoint_to_shape[endpoint]
       self.assertListEqual(end_points[endpoint].get_shape().as_list(), shape)

 def testClassificationShapes(self):
   global_pool = True
   num_classes = 10
   inputs = create_test_input(2, 224, 224, 3)
   with slim.arg_scope(resnet_utils.resnet_arg_scope()):
     _, end_points = self._resnet_small(inputs, num_classes,
                                        global_pool=global_pool,
                                        scope='resnet')
     endpoint_to_shape = {
         'resnet/block1': [2, 28, 28, 4],
         'resnet/block2': [2, 14, 14, 8],
         'resnet/block3': [2, 7, 7, 16],
         'resnet/block4': [2, 7, 7, 32]}
     for endpoint in endpoint_to_shape:
       shape = endpoint_to_shape[endpoint]
       self.assertListEqual(end_points[endpoint].get_shape().as_list(), shape)

  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)

 def testUnknownBatchSize(self):
   batch = 2
   height, width = 65, 65
   global_pool = True
   num_classes = 10
   inputs = create_test_input(None, height, width, 3)
   with slim.arg_scope(resnet_utils.resnet_arg_scope()):
     logits, _ = self._resnet_small(inputs, num_classes,
                                    global_pool=global_pool,
                                    scope='resnet')
   self.assertTrue(logits.op.name.startswith('resnet/logits'))
   self.assertListEqual(logits.get_shape().as_list(),
                        [None, 1, 1, num_classes])
   images = create_test_input(batch, height, width, 3)
   with self.test_session() as sess:
     sess.run(tf.global_variables_initializer())
     output = sess.run(logits, {inputs: images.eval()})
     self.assertEqual(output.shape, (batch, 1, 1, num_classes))

 def testRootlessFullyConvolutionalEndpointShapes(self):
   global_pool = False
   num_classes = 10
   inputs = create_test_input(2, 128, 128, 3)
   with slim.arg_scope(resnet_utils.resnet_arg_scope()):
     _, end_points = self._resnet_small(inputs, num_classes,
                                        global_pool=global_pool,
                                        include_root_block=False,
                                        spatial_squeeze=False,
                                        scope='resnet')
     endpoint_to_shape = {
         'resnet/block1': [2, 64, 64, 4],
         'resnet/block2': [2, 32, 32, 8],
         'resnet/block3': [2, 16, 16, 16],
         'resnet/block4': [2, 16, 16, 32]}
     for endpoint in endpoint_to_shape:
       shape = endpoint_to_shape[endpoint]
       self.assertListEqual(end_points[endpoint].get_shape().as_list(), shape)

 def testEndpointNames(self):
   # Like ResnetUtilsTest.testEndPointsV1(), but for the public API.
   global_pool = True
   num_classes = 10
   inputs = create_test_input(2, 224, 224, 3)
   with slim.arg_scope(resnet_utils.resnet_arg_scope()):
     _, end_points = self._resnet_small(inputs, num_classes,
                                        global_pool=global_pool,
                                        scope='resnet')
   expected = ['resnet/conv1']
   for block in range(1, 5):
     for unit in range(1, 4 if block < 4 else 3):
       for conv in range(1, 4):
         expected.append('resnet/block%d/unit_%d/bottleneck_v1/conv%d' %
                         (block, unit, conv))
       expected.append('resnet/block%d/unit_%d/bottleneck_v1' % (block, unit))
     expected.append('resnet/block%d/unit_1/bottleneck_v1/shortcut' % block)
     expected.append('resnet/block%d' % block)
   expected.extend(['global_pool', 'resnet/logits', 'resnet/spatial_squeeze',
                    'predictions'])
   self.assertItemsEqual(end_points.keys(), expected)

 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)

  def testStridingLastUnitVsSubsampleBlockEnd(self):
    """Compares subsampling at the block's last unit or block's end.

    Makes sure that the final output is the same when we use a stride at the
    last unit of a block vs. we subsample activations at the end of a block.
    """
    block = resnet_v1.resnet_v1_block

    blocks = [
        block('block1', base_depth=1, num_units=2, stride=2),
        block('block2', base_depth=2, num_units=2, stride=2),
        block('block3', base_depth=4, num_units=2, stride=2),
        block('block4', base_depth=8, num_units=2, stride=1),
    ]

    # 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)

              # Subsampling at the last unit of the block.
              output = resnet_utils.stack_blocks_dense(
                  inputs, blocks, output_stride,
                  store_non_strided_activations=False,
                  outputs_collections='output')
              output_end_points = slim.utils.convert_collection_to_dict(
                  'output')

              # Make the two networks use the same weights.
              tf.get_variable_scope().reuse_variables()

              # Subsample activations at the end of the blocks.
              expected = resnet_utils.stack_blocks_dense(
                  inputs, blocks, output_stride,
                  store_non_strided_activations=True,
                  outputs_collections='expected')
              expected_end_points = slim.utils.convert_collection_to_dict(
                  'expected')

              sess.run(tf.global_variables_initializer())

              # Make sure that the final output is the same.
              output, expected = sess.run([output, expected])
              self.assertAllClose(output, expected, atol=1e-4, rtol=1e-4)

              # Make sure that intermediate block activations in
              # output_end_points are subsampled versions of the corresponding
              # ones in expected_end_points.
              for i, block in enumerate(blocks[:-1:]):
                output = output_end_points[block.scope]
                expected = expected_end_points[block.scope]
                atrous_activated = (output_stride is not None and
                                    2 ** i >= output_stride)
                if not atrous_activated:
                  expected = resnet_utils.subsample(expected, 2)
                output, expected = sess.run([output, expected])
                self.assertAllClose(output, expected, atol=1e-4, rtol=1e-4)