tensorflow36《TensorFlow实战》笔记-06-04 TensorFlow实现 ResNet code

# 《TensorFlow实战》06 TensorFlow实现经典卷积神经网络# win10 Tensorflow1.0.1 python3.5.3# CUDA v8.0 cudnn-8.0-windows10-x64-v5.1# filename:sz06.04.py # TensorFlow实现 ResNet# https://github.com/tensorflow/models/blob/master/slim/nets/resnet_v2.py# tensorflow_models\slim\nets\resnet_v2.pyimport collectionsimport tensorflow as tffrom datetime import datetimeimport mathimport timeslim = tf.contrib.slimclass 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_scopedef 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 netdef 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 [e-mail]_arg_scopedef 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_pointsdef 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=32height, width = 224, 224inputs = 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 = 100time_tensorflow_run(sess, net, 'Forward')'''2017-04-14 23:51:30.904068: step 0, duration = 0.9692017-04-14 23:51:40.467593: step 10, duration = 0.9532017-04-14 23:51:50.046747: step 20, duration = 0.9532017-04-14 23:51:59.625897: step 30, duration = 0.9532017-04-14 23:52:09.205049: step 40, duration = 0.9692017-04-14 23:52:18.768574: step 50, duration = 0.9532017-04-14 23:52:28.363353: step 60, duration = 0.9692017-04-14 23:52:37.942557: step 70, duration = 0.9692017-04-14 23:52:47.521667: step 80, duration = 0.9532017-04-14 23:52:57.090941: step 90, duration = 0.9592017-04-14 23:53:05.717893: Forward across 100 steps, 0.958 +/- 0.007 sec / batch'''