def resnet_v1_200(inputs,
num_classes=None,
is_training=True,
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,
is_training,
global_pool=global_pool,
output_stride=output_stride,
include_root_block=True,
reuse=reuse,
scope=scope)
def create_trunk(self, images, global_pool=True):
red, green, blue = tf.split(images * 255, 3, axis=3)
images = tf.concat([blue, green, red], 3) - MEAN_COLOR
with slim.arg_scope(
resnet_v1.resnet_arg_scope(is_training=self.training,
weight_decay=self.weight_decay,
batch_norm_decay=args.bn_decay)):
blocks = [
resnet_utils.Block('block1', bottleneck, [(256, 64, 1)] * 3),
resnet_utils.Block('block2', bottleneck,
[(512, 128, 2)] + [(512, 128, 1)] * 3),
resnet_utils.Block('block3', bottleneck, [(1024, 256, 2)] +
[(1024, 256, 1)] * self.num_block3),
resnet_utils.Block('block4', bottleneck,
[(2048, 512, 2)] + [(2048, 512, 1)] * 2)
]
net, endpoints = resnet_v1.resnet_v1(images,
blocks,
global_pool=global_pool,
reuse=self.reuse,
scope=self.scope)
self.outputs = endpoints
def resnet_v2_block(scope, base_depth, num_units, stride):
"""
Helper function for creating a resnet_v2 bottleneck block.
:param scope: The scope of the block.
:param base_depth: The depth of the bottleneck layer for each unit.
:param num_units: The number of units in the block.
:param stride: The stride of the block, implemented as a stride in the last unit.
All other units have stride=1.
:return: 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
}])
def resnet_v2_block(scope, base_depth, num_units, stride, centered_stride=False):
"""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,
'centered_stride': centered_stride,
}])
