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cell.py
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from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import tensorflow as tf
slim = tf.contrib.slim
class NASBaseCell(object):
"""NASNet Cell class that is used as a 'layer' in image architectures.
Args:
num_conv_filters: The number of filters for each convolution operation.
operations: List of operations that are performed in the NASNet Cell in
order.
used_hiddenstates: Binary array that signals if the hiddenstate was used
within the cell. This is used to determine what outputs of the cell
should be concatenated together.
hiddenstate_indices: Determines what hiddenstates should be combined
together with the specified operations to create the NASNet cell.
"""
def __init__(self, num_conv_filters, operations, used_hiddenstates,
hiddenstate_indices, drop_path_keep_prob, total_num_cells,
total_training_steps):
assert len(hiddenstate_indices) == len(operations)
assert len(operations) % 2 == 0
self._num_conv_filters = num_conv_filters
self._operations = operations
self._used_hiddenstates = used_hiddenstates
self._hiddenstate_indices = hiddenstate_indices
self._drop_path_keep_prob = drop_path_keep_prob
self._total_num_cells = total_num_cells
self._total_training_steps = total_training_steps
def __call__(self, net, scope, filter_scaling, stride, prev_layer, cell_num):
self._cell_num = cell_num
self._filter_scaling = filter_scaling
self._filter_size = int(self._num_conv_filters * filter_scaling)
with tf.variable_scope(scope):
net = self._cell_base(net, prev_layer)
for i in range(int(len(self._operations) / 2)):
with tf.variable_scope('comb_iter_{}'.format(i)):
h1 = net[self._hiddenstate_indices[i * 2]]
h2 = net[self._hiddenstate_indices[i * 2 + 1]]
with tf.variable_scope('left'):
h1 = self._apply_operation(h1, self._operations[i * 2], stride,
self._hiddenstate_indices[i * 2] < 2)
with tf.variable_scope('right'):
h2 = self._apply_operation(h2, self._operations[i * 2 + 1], stride,
self._hiddenstate_indices[i * 2 + 1] < 2)
with tf.variable_scope('combine'):
h = h1 + h2
net.append(h)
with tf.variable_scope('cell_output'):
net = self._combine_unused_states(net)
return net
def _cell_base(self, net, prev_layer):
filter_size = self._filter_size
if prev_layer is None:
prev_layer = net
elif net.shape[2] != prev_layer.shape[2]:
prev_layer = tf.nn.relu(prev_layer)
prev_layer = self._factorized_reduction(prev_layer, filter_size, stride=2)
elif filter_size != prev_layer.shape[3]:
prev_layer = tf.nn.relu(prev_layer)
prev_layer = slim.conv2d(prev_layer, filter_size, 1, scope='prev_1x1')
prev_layer = slim.batch_norm(prev_layer, scope='prev_bn')
net = tf.nn.relu(net)
net = slim.conv2d(net, filter_size, 1, scope='1x1')
net = slim.batch_norm(net, scope='beginning_bn')
net = tf.split(axis=3, num_or_size_splits=1, value=net)
for split in net:
assert split.shape[3] == filter_size
net.append(prev_layer)
return net
def _apply_operation(self, net, operation, stride, is_from_original_input):
if stride > 1 and not is_from_original_input:
stride = 1
input_filters = net.shape[3]
filter_size = self._filter_size
if 'separable' in operation:
num_layers = int(operation.split('_')[-1])
kernel_size = int(operation.split('x')[0][-1])
for layer_num in range(num_layers):
net = tf.nn.relu(net)
net = slim.separable_conv2d(
net,
filter_size,
kernel_size,
depth_multiplier=1,
scope='separable_{0}x{0}_{1}'.format(kernel_size, layer_num + 1),
stride=stride)
net = slim.batch_norm(
net, scope='bn_sep_{0}x{0}_{1}'.format(kernel_size, layer_num + 1))
stride = 1
elif operation in ['none']:
if stride > 1 or (input_filters != filter_size):
net = tf.nn.relu(net)
net = slim.conv2d(net, filter_size, 1, stride=stride, scope='1x1')
net = slim.batch_norm(net, scope='bn_1')
elif 'pool' in operation:
pooling_type = operation.split('_')[0]
pooling_shape = int(operation.split('_')[-1].split('x')[0])
if pooling_type == 'avg':
net = slim.avg_pool2d(net, pooling_shape, stride=stride, padding='SAME')
elif pooling_type == 'max':
net = slim.max_pool2d(net, pooling_shape, stride=stride, padding='SAME')
else:
raise ValueError('Unimplemented pooling type: ', pooling_type)
if input_filters != filter_size:
net = slim.conv2d(net, filter_size, 1, stride=1, scope='1x1')
net = slim.batch_norm(net, scope='bn_1')
else:
raise ValueError('Unimplemented operation: ', operation)
if operation != 'none':
net = self._apply_drop_path(net)
return net
def _combine_unused_states(self, net):
used_hiddenstates = self._used_hiddenstates
states_to_combine = (
[h for h, is_used in zip(net, used_hiddenstates) if not is_used])
net = tf.concat(values=states_to_combine, axis=3)
return net
def _apply_drop_path(self, net):
drop_path_keep_prob = self._drop_path_keep_prob
if drop_path_keep_prob < 1.0:
# Scale keep prob by layer number
assert self._cell_num != -1
layer_ratio = (self._cell_num + 1) / float(self._total_num_cells)
drop_path_keep_prob = 1 - layer_ratio * (1 - drop_path_keep_prob)
# Decrease keep prob over time
current_step = tf.cast(tf.train.get_or_create_global_step(), tf.float32)
current_ratio = tf.minimum(1.0, current_step / self._total_training_steps)
drop_path_keep_prob = 1 - current_ratio * (1 - drop_path_keep_prob)
# Drop path
noise_shape = [net.shape[0], 1, 1, 1]
random_tensor = drop_path_keep_prob
random_tensor += tf.random_uniform(noise_shape, dtype=tf.float32)
binary_tensor = tf.cast(tf.floor(random_tensor), net.dtype)
keep_prob_inv = tf.cast(1.0 / drop_path_keep_prob, net.dtype)
net = net * keep_prob_inv * binary_tensor
return net
def _factorized_reduction(self, net, output_filters, stride):
assert output_filters % 2 == 0
if stride == 1:
net = slim.conv2d(net, output_filters, 1, scope='path_conv')
net = slim.batch_norm(net, scope='path_bn')
return net
stride_spec = [1, stride, stride, 1]
# Skip path 1
path1 = tf.nn.avg_pool(net, [1, 1, 1, 1], stride_spec, 'VALID')
path1 = slim.conv2d(path1, int(output_filters / 2), 1, scope='path1_conv')
# Skip path 2
pad_arr = [[0, 0], [0, 1], [0, 1], [0, 0]]
path2 = tf.pad(net, pad_arr)[:, 1:, 1:, :]
path2 = tf.nn.avg_pool(path2, [1, 1, 1, 1], stride_spec, 'VALID')
path2 = slim.conv2d(path2, int(output_filters / 2), 1, scope='path2_conv')
# Concat and apply BN
final_path = tf.concat(values=[path1, path2], axis=3)
final_path = slim.batch_norm(final_path, scope='final_path_bn')
return final_path
class PNASCell(NASBaseCell):
"""PNASNet Cell."""
def __init__(self, num_conv_filters, drop_path_keep_prob, total_num_cells,
total_training_steps):
# Configuration for the PNASNet-5 model.
operations = [
'separable_5x5_2', 'max_pool_3x3', 'separable_7x7_2', 'max_pool_3x3',
'separable_5x5_2', 'separable_3x3_2', 'separable_3x3_2', 'max_pool_3x3',
'separable_3x3_2', 'none'
]
used_hiddenstates = [1, 1, 0, 0, 0, 0, 0]
hiddenstate_indices = [1, 1, 0, 0, 0, 0, 4, 0, 1, 0]
super(PNASCell, self).__init__(
num_conv_filters, operations, used_hiddenstates, hiddenstate_indices,
drop_path_keep_prob, total_num_cells, total_training_steps)