def forward_fn(inputs, is_train, data_format):
"""Forward pass function.
Args:
* inputs: inputs to the network's forward pass
* is_train: whether to use the forward pass with training operations inserted
* data_format: data format ('channels_last' OR 'channels_first')
Returns:
* inputs: outputs from the network's forward pass
"""
# setup hyper-parameters
nb_blocks = (FLAGS.resnet_size - 2) // 6
bottleneck = False
nb_classes = FLAGS.nb_classes
nb_filters = 16
kernel_size = 3
conv_stride = 1
first_pool_size = None
first_pool_stride = None
block_sizes = [nb_blocks] * 3
block_strides = [1, 2, 2]
# model definition
model = ResNet.Model(
FLAGS.resnet_size, bottleneck, nb_classes, nb_filters, kernel_size, conv_stride,
first_pool_size, first_pool_stride, block_sizes, block_strides, data_format=data_format)
inputs = model(inputs, is_train)
return inputs
def forward_fn(inputs, is_train, data_format):
"""Forward pass function.
Args:
* inputs: inputs to the network's forward pass
* is_train: whether to use the forward pass with training operations inserted
* data_format: data format ('channels_last' OR 'channels_first')
Returns:
* inputs: outputs from the network's forward pass
"""
# for deeper networks, use bottleneck layers for speed-up
if FLAGS.resnet_size < 50:
bottleneck = False
else:
bottleneck = True
# setup hyper-parameters
nb_classes = FLAGS.nb_classes
nb_filters = 64
kernel_size = 7
conv_stride = 2
first_pool_size = 3
first_pool_stride = 2
block_sizes = get_block_sizes(FLAGS.resnet_size)
block_strides = [1, 2, 2, 2]
# model definition
model = ResNet.Model(
FLAGS.resnet_size, bottleneck, nb_classes, nb_filters, kernel_size, conv_stride,
first_pool_size, first_pool_stride, block_sizes, block_strides, data_format=data_format)
inputs = model(inputs, is_train)
return inputs