def residual_block(x,
in_filter,
out_filter,
stride,
update_bn=True,
activate_before_residual=False):
"""Adds residual connection to `x` in addition to applying BN->ReLU->3x3 Conv.
Args:
x: Tensor that is the output of the previous layer in the model.
in_filter: Number of filters `x` has.
out_filter: Number of filters that the output of this layer will have.
stride: Integer that specified what stride should be applied to `x`.
activate_before_residual: Boolean on whether a BN->ReLU should be applied
to x before the convolution is applied.
Returns:
A Tensor that is the result of applying two sequences of BN->ReLU->3x3 Conv
and then adding that Tensor to `x`.
"""
if activate_before_residual: # Pass up RELU and BN activation for resnet
with tf.variable_scope('shared_activation'):
x = ops.batch_norm(x, update_stats=update_bn, scope='init_bn')
x = tf.nn.relu(x)
orig_x = x
else:
orig_x = x
block_x = x
if not activate_before_residual:
with tf.variable_scope('residual_only_activation'):
block_x = ops.batch_norm(block_x,
update_stats=update_bn,
scope='init_bn')
block_x = tf.nn.relu(block_x)
with tf.variable_scope('sub1'):
block_x = ops.conv2d(block_x,
out_filter,
3,
stride=stride,
scope='conv1')
with tf.variable_scope('sub2'):
block_x = ops.batch_norm(block_x, update_stats=update_bn, scope='bn2')
block_x = tf.nn.relu(block_x)
block_x = ops.conv2d(block_x, out_filter, 3, stride=1, scope='conv2')
with tf.variable_scope(
'sub_add'): # If number of filters do not agree then zero pad them
if in_filter != out_filter:
orig_x = ops.avg_pool(orig_x, stride, stride)
orig_x = ops.zero_pad(orig_x, in_filter, out_filter)
x = orig_x + block_x
return x
def _shake_shake_skip_connection(x, output_filters, stride):
"""Adds a residual connection to the filter x for the shake-shake model."""
curr_filters = int(x.shape[3])
if curr_filters == output_filters:
return x
stride_spec = ops.stride_arr(stride, stride)
# Skip path 1
path1 = tf.nn.avg_pool(
x, [1, 1, 1, 1], stride_spec, 'VALID', data_format='NHWC')
path1 = ops.conv2d(path1, int(output_filters / 2), 1, scope='path1_conv')
# Skip path 2
# First pad with 0's then crop
pad_arr = [[0, 0], [0, 1], [0, 1], [0, 0]]
path2 = tf.pad(x, pad_arr)[:, 1:, 1:, :]
concat_axis = 3
path2 = tf.nn.avg_pool(
path2, [1, 1, 1, 1], stride_spec, 'VALID', data_format='NHWC')
path2 = ops.conv2d(path2, int(output_filters / 2), 1, scope='path2_conv')
# Concat and apply BN
final_path = tf.concat(values=[path1, path2], axis=concat_axis)
final_path = ops.batch_norm(final_path, scope='final_path_bn')
return final_path
def build_shake_shake_model(images, num_classes, hparams, is_training):
"""Builds the Shake-Shake model.
Build the Shake-Shake model from https://arxiv.org/abs/1705.07485.
Args:
images: Tensor of images that will be fed into the Wide ResNet Model.
num_classes: Number of classed that the model needs to predict.
hparams: tf.HParams object that contains additional hparams needed to
construct the model. In this case it is the `shake_shake_widen_factor`
that is used to determine how many filters the model has.
is_training: Is the model training or not.
Returns:
The logits of the Shake-Shake model.
"""
depth = 26
k = hparams.shake_shake_widen_factor # The widen factor
n = int((depth - 2) / 6)
x = images
x = ops.conv2d(x, 16, 3, scope='init_conv')
x = ops.batch_norm(x, scope='init_bn')
with tf.variable_scope('L1'):
x = _shake_shake_layer(x, 16 * k, n, 1, is_training)
with tf.variable_scope('L2'):
x = _shake_shake_layer(x, 32 * k, n, 2, is_training)
with tf.variable_scope('L3'):
x = _shake_shake_layer(x, 64 * k, n, 2, is_training)
x = tf.nn.relu(x)
x = ops.global_avg_pool(x)
# Fully connected
logits = ops.fc(x, num_classes)
return logits
def build_wrn_model(images, num_classes, wrn_size):
"""Builds the WRN model.
Build the Wide ResNet model from https://arxiv.org/abs/1605.07146.
Args:
images: Tensor of images that will be fed into the Wide ResNet Model.
num_classes: Number of classed that the model needs to predict.
wrn_size: Parameter that scales the number of filters in the Wide ResNet
model.
Returns:
The logits of the Wide ResNet model.
"""
kernel_size = wrn_size
filter_size = 3
num_blocks_per_resnet = 4
filters = [
min(kernel_size, 16), kernel_size, kernel_size * 2, kernel_size * 4
]
strides = [1, 2, 2] # stride for each resblock
# Run the first conv
with tf.variable_scope('init'):
x = images
output_filters = filters[0]
x = ops.conv2d(x, output_filters, filter_size, scope='init_conv')
first_x = x # Res from the beginning
orig_x = x # Res from previous block
for block_num in range(1, 4):
with tf.variable_scope('unit_{}_0'.format(block_num)):
activate_before_residual = True if block_num == 1 else False
x = residual_block(
x,
filters[block_num - 1],
filters[block_num],
strides[block_num - 1],
activate_before_residual=activate_before_residual)
for i in range(1, num_blocks_per_resnet):
with tf.variable_scope('unit_{}_{}'.format(block_num, i)):
x = residual_block(x,
filters[block_num],
filters[block_num],
1,
activate_before_residual=False)
x, orig_x = _res_add(filters[block_num - 1], filters[block_num],
strides[block_num - 1], x, orig_x)
final_stride_val = np.prod(strides)
x, _ = _res_add(filters[0], filters[3], final_stride_val, x, first_x)
with tf.variable_scope('unit_last'):
x = ops.batch_norm(x, scope='final_bn')
x = tf.nn.relu(x)
x = ops.global_avg_pool(x)
logits = ops.fc(x, num_classes)
return logits
