import collections
import tensorflow as tf
from datetime import datetime
import math
import time
slim = tf.contrib.slim
class Block(collections.namedtuple('Block', ['scope', 'unit_fn', 'args'])):
'A name tuple describing a ResNet block.'
def subsample(inputs, factor, scope=None):
if factor == 1:
return inputs
else:
return slim.max_pool2d(inputs, [1, 1], stride=factor, scope=scope)
def conv2d_same(inputs, num_outputs, kernel_size, stride, scope=None):
if stride == 1:
return slim.conv2d(inputs, num_outputs, kernel_size, stride=1, padding='SAME', scope=scope)
else:
pad_total = kernel_size - 1
pad_beg = pad_total // 2
pad_end = pad_total - pad_beg
inputs = tf.pad(inputs, [[0, 0], [pad_beg, pad_end], [pad_beg, pad_end], [0, 0]])
return slim.conv2d(inputs, num_outputs, kernel_size, stride=stride, padding='VALID', scope=scope)
@slim.add_arg_scope
def stack_blocks_dense(net, blocks, outputs_collections=None):
for block in blocks:
with tf.variable_scope(block.scope, 'block', [net]) as sc:
for i, unit in enumerate(block.args):
with tf.variable_scope('unit_%d' % (i + 1), values=[net]):
unit_depth, unit_depth_bottleneck, unit_stride = unit
net = block.unit_fn(net,
depth=unit_depth,
depth_bottleneck=unit_depth_bottleneck,
stride=unit_stride)
net = slim.utils.collect_named_outputs(outputs_collections, sc.name, net)
return net
def resnet_arg_scope(is_training=True,
weight_decay=0.0001,
batch_norm_decay=0.997,
batch_norm_epsilon=1e-5,
batch_norm_scale=True):
batch_norm_params = {
'is_training': is_training,
'decay': batch_norm_decay,
'epsilon': batch_norm_epsilon,
'scale': batch_norm_scale,
'updates_collections': tf.GraphKeys.UPDATE_OPS,
}
with slim.arg_scope(
[slim.conv2d],
weights_regularizer=slim.l2_regularizer(weight_decay),
weights_initializer=slim.variance_scaling_initializer(),
activation_fn=tf.nn.relu,
normalizer_fn=slim.batch_norm,
normalizer_params=batch_norm_params):
with slim.arg_scope([slim.batch_norm], **batch_norm_params):
with slim.arg_scope([slim.max_pool2d], padding='SAME') as arg_sc:
return arg_sc
@slim.add_arg_scope
def bottleneck(inputs, depth, depth_bottleneck, stride, outputs_collections=None, scope=None):
with tf.variable_scope(scope, 'bottleneck_v2', [inputs]) as sc:
depth_in = slim.utils.last_dimension(inputs.get_shape(), min_rank=4)
preact = slim.batch_norm(inputs, activation_fn=tf.nn.relu, scope='preact')
if depth == depth_in:
shortcut = subsample(inputs, stride, 'shortcut')
else:
shortcut = slim.conv2d(preact, depth, [1, 1], stride=stride,
normalizer_fn=None, activation_fn=None,
scope='shortcut')
residual = slim.conv2d(preact, depth_bottleneck, [1, 1], stride=1, scope='conv1')
residual = conv2d_same(residual, depth_bottleneck, 3, stride, scope='conv2')
residual = slim.conv2d(residual, depth, [1, 1], stride=1,
normalizer_fn=None, activation_fn=None,
scope='conv3')
output = shortcut + residual
return slim.utils.collect_named_outputs(outputs_collections, sc.name, output)
def resnet_v2(inputs,
blocks,
num_classes=None,
global_pool=True,
include_root_block=True,
reuse=None,
scope=None):
with tf.variable_scope(scope, 'resnet_v2', [inputs], reuse=reuse) as sc:
end_points_collection = sc.original_name_scope + '_end_points'
with slim.arg_scope([slim.conv2d, bottleneck, stack_blocks_dense], outputs_collections=end_points_collection):
net = inputs
if include_root_block:
with slim.arg_scope([slim.conv2d], activation_fn=None, normalizer_fn=None):
net = conv2d_same(net, 64, 7, stride=2, scope='conv1')
net = slim.max_pool2d(net, [3, 3], stride=2, scope='pool1')
net = stack_blocks_dense(net, blocks)
net = slim.batch_norm(net, activation_fn=tf.nn.relu, scope='postnorm')
if global_pool:
net = tf.reduce_mean(net, [1, 2], name='pool5', keep_dims=True)
if num_classes is not None:
net = slim.conv2d(net, num_classes, [1, 1], activation_fn=None,
normalizer_fn=None, scope='logits')
end_points = slim.utils.convert_collection_to_dict(end_points_collection)
if num_classes is not None:
end_points['predictions'] = slim.softmax(net, scope='predictions')
return net, end_points
def resnet_v2_50(inputs,
num_classes=None,
global_pool=True,
reuse=None,
scope='resnet_v2_50'):
blocks=[
Block('block1', bottleneck, [(256, 64, 1)]*2 + [(256, 64, 2)]),
Block('block2', bottleneck, [(512, 128, 1)]*3 + [(512, 128, 2)]),
Block('block3', bottleneck, [(1024, 256, 1)]*5 + [(1024, 256, 2)]),
Block('block4', bottleneck, [(2048, 512, 1)]*3)
]
return resnet_v2(inputs, blocks, num_classes, global_pool,
include_root_block=True, reuse=reuse, scope=scope)
def resnet_v2_101(inputs,
num_classes=None,
global_pool=True,
reuse=None,
scope='resnet_v2_101'):
blocks = [
Block('block1', bottleneck, [(256, 64, 1)]*2 + [(256, 64, 2)]),
Block('block2', bottleneck, [(512, 128, 1)]*3 + [(512, 128, 2)]),
Block('block3', bottleneck, [(1024, 256, 1)]*22 + [(1024, 256, 2)]),
Block('block4', bottleneck, [(2048, 512, 1)]*3)
]
return resnet_v2(inputs, blocks, num_classes, global_pool,
include_root_block=True, reuse=reuse, scope=scope)
def resnet_v2_152(inputs,
num_classes=None,
global_pool=True,
reuse=None,
scope='resnet_v2_152'):
blocks=[
Block('block1', bottleneck, [(256, 64, 1)]*2 + [(256, 64, 2)]),
Block('block2', bottleneck, [(512, 128, 1)]*7 + [(512, 128, 2)]),
Block('block3', bottleneck, [(1024, 256, 1)]*35 + [(1024, 256, 2)]),
Block('block4', bottleneck, [(2048, 512, 1)]*3)
]
return resnet_v2(inputs, blocks, num_classes, global_pool,
include_root_block=True, reuse=reuse, scope=scope)
def resnet_v2_200(inputs,
num_classes=None,
global_pool=True,
reuse=None,
scope='resnet_v2_200'):
blocks=[
Block('block1', bottleneck, [(256, 64, 1)]*2 + [(256, 64, 2)]),
Block('block2', bottleneck, [(512, 256, 1)]*23 + [(512, 128, 2)]),
Block('block3', bottleneck, [(1024, 256, 1)]*35 + [(1024, 256, 2)]),
Block('block4', bottleneck, [(2048, 512, 1)]*3)
]
return resnet_v2(inputs, blocks, num_classes, global_pool,
include_root_block=True, reuse=reuse, scope=scope)
def time_tensorflow_run(session, target, info_string):
num_steps_burn_in = 10
total_duration = 0.0
total_duration_squared = 0.0
for i in range(num_batches + num_steps_burn_in):
start_time = time.time()
_ = session.run(target)
duration = time.time() - start_time
if i >= num_steps_burn_in:
if not i %10:
print('%s: step %d, duration = %.3f' %(datetime.now(), i - num_steps_burn_in, duration))
total_duration += duration
total_duration_squared += duration * duration
mn = total_duration / num_batches
vr = total_duration_squared / num_batches - mn * mn
sd = math.sqrt(vr)
print('%s: %s across %d steps, %.3f +/- %.3f sec / batch' %(datetime.now(), info_string, num_batches, mn, sd))
batch_size=32
height, width = 224, 224
inputs = tf.random_uniform((batch_size, height, width, 3))
with slim.arg_scope(resnet_arg_scope(is_training=False)):
net, end_points = resnet_v2_152(inputs, 1000)
init = tf.global_variables_initializer()
sess = tf.Session()
sess.run(init)
num_batches = 100
time_tensorflow_run(sess, net, 'Forward')
'''
2017-04-14 23:51:30.904068: step 0, duration = 0.969
2017-04-14 23:51:40.467593: step 10, duration = 0.953
2017-04-14 23:51:50.046747: step 20, duration = 0.953
2017-04-14 23:51:59.625897: step 30, duration = 0.953
2017-04-14 23:52:09.205049: step 40, duration = 0.969
2017-04-14 23:52:18.768574: step 50, duration = 0.953
2017-04-14 23:52:28.363353: step 60, duration = 0.969
2017-04-14 23:52:37.942557: step 70, duration = 0.969
2017-04-14 23:52:47.521667: step 80, duration = 0.953
2017-04-14 23:52:57.090941: step 90, duration = 0.959
2017-04-14 23:53:05.717893: Forward across 100 steps, 0.958 +/- 0.007 sec / batch
'''
