Python resnet_v2_block Examples

Example #1

File: resnet_v2_test.py Project: miglopst/models

 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)

Example #2

File: resnet_v2_test.py Project: mbossX/RRPN_FPN_Tensorflow

 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)

Example #3

File: resnet_v2.py Project: dsl2009/tf-dsl-obj-detect

def resnet_v2_50(inputs,
                 num_classes=None,
                 is_training=True,
                 global_pool=True,
                 output_stride=None,
                 spatial_squeeze=True,
                 reuse=None,
                 scope='resnet_v2_50'):
    """ResNet-50 model of [1]. See resnet_v2() for arg and return description."""
    blocks = [
        resnet_v2_block('block1', base_depth=64, num_units=3, stride=2),
        resnet_v2_block('block2', base_depth=128, num_units=4, stride=2),
        resnet_v2_block('block3', base_depth=256, num_units=6, stride=2),
        resnet_v2_block('block4', base_depth=512, num_units=3, stride=2),
    ]
    return resnet_v2(inputs,
                     blocks,
                     num_classes,
                     is_training=is_training,
                     global_pool=global_pool,
                     output_stride=output_stride,
                     include_root_block=True,
                     spatial_squeeze=spatial_squeeze,
                     reuse=reuse,
                     scope=scope)