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313 lines
11 KiB
313 lines
11 KiB
# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from six.moves import xrange # pylint: disable=redefined-builtin
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from datetime import datetime
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import math
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import time
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import tensorflow.python.platform
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import tensorflow as tf
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FLAGS = tf.app.flags.FLAGS
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tf.app.flags.DEFINE_integer('batch_size', 128, """Batch size.""")
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tf.app.flags.DEFINE_integer('num_batches', 100, """Number of batches to run.""")
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tf.app.flags.DEFINE_boolean('forward_only', False,
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"""Only run the forward pass.""")
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tf.app.flags.DEFINE_boolean('forward_backward_only', False,
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"""Only run the forward-forward pass.""")
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tf.app.flags.DEFINE_string('data_format', 'NCHW',
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"""The data format for Convnet operations.
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Can be either NHWC or NCHW.
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""")
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tf.app.flags.DEFINE_boolean('log_device_placement', False,
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"""Whether to log device placement.""")
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def _conv(name, inpOp, nIn, nOut, kH, kW, dH, dW, padType, wd=0.0005):
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with tf.name_scope(name) as scope:
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kernel = tf.get_variable(
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name + '_w', [kH, kW, nIn, nOut],
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initializer=tf.truncated_normal_initializer(
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stddev=0.01, dtype=tf.float32),
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dtype=tf.float32)
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if wd is not None and wd > 0:
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weight_decay = tf.mul(tf.nn.l2_loss(kernel), wd, name='weight_loss')
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tf.add_to_collection('losses', weight_decay)
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if FLAGS.data_format == 'NCHW':
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strides = [1, 1, dH, dW]
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else:
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strides = [1, dH, dW, 1]
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conv = tf.nn.conv2d(
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inpOp,
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kernel,
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strides,
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padding=padType,
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data_format=FLAGS.data_format)
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biases = tf.get_variable(
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name=name + '_b',
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shape=[nOut],
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initializer=tf.constant_initializer(
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value=0.0, dtype=tf.float32),
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dtype=tf.float32)
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bias = tf.reshape(
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tf.nn.bias_add(
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conv, biases, data_format=FLAGS.data_format),
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conv.get_shape())
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conv1 = tf.nn.relu(bias, name=scope)
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return conv1
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def _affine(name, inpOp, nIn, nOut, wd=0.0005, act=True, drop=None):
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with tf.name_scope(name) as scope:
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kernel = tf.get_variable(
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name + '_w', [nIn, nOut],
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initializer=tf.truncated_normal_initializer(
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stddev=0.01, dtype=tf.float32),
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dtype=tf.float32)
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if wd is not None and wd > 0:
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weight_decay = tf.mul(tf.nn.l2_loss(kernel), wd, name='weight_loss')
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tf.add_to_collection('losses', weight_decay)
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biases = tf.get_variable(
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name + '_b', [nOut],
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initializer=tf.constant_initializer(
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value=0.0, dtype=tf.float32),
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dtype=tf.float32,
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trainable=True)
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affine1 = tf.nn.relu_layer(inpOp, kernel, biases, name=name) if act else \
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tf.matmul(inpOp, kernel) + biases
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output = tf.nn.dropout(affine1, drop) if drop else affine1
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return output
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def _mpool(name, inpOp, kH, kW, dH, dW):
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if FLAGS.data_format == 'NCHW':
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ksize = [1, 1, kH, kW]
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strides = [1, 1, dH, dW]
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else:
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ksize = [1, kH, kW, 1]
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strides = [1, dH, dW, 1]
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return tf.nn.max_pool(
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inpOp,
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ksize=ksize,
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strides=strides,
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padding='VALID',
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data_format=FLAGS.data_format,
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name=name)
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def _norm(name, l_input, lsize=4):
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return tf.nn.lrn(l_input,
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lsize,
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bias=1.0,
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alpha=0.001 / 9.0,
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beta=0.75,
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name=name)
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def loss(logits, labels):
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labels = tf.cast(labels, tf.int64)
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cross_entropy = tf.nn.sparse_softmax_cross_entropy_with_logits(
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logits, labels, name='cross_entropy_per_example')
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cross_entropy_mean = tf.reduce_mean(cross_entropy, name='cross_entropy')
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tf.add_to_collection('losses', cross_entropy_mean)
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# The total loss is defined as the cross entropy loss plus all of the weight
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# decay terms (L2 loss).
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return tf.add_n(tf.get_collection('losses'), name='total_loss')
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def get_incoming_shape(incoming):
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""" Returns the incoming data shape """
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if isinstance(incoming, tf.Tensor):
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return incoming.get_shape().as_list()
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elif type(incoming) in [np.array, list, tuple]:
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return np.shape(incoming)
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else:
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raise Exception("Invalid incoming layer.")
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def inference(images):
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conv1 = _conv('conv1', images, 3, 96, 11, 11, 4, 4, 'VALID')
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pool1 = _mpool('pool1', conv1, 3, 3, 2, 2)
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norm1 = _norm('norm1', pool1, lsize=5)
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conv2 = _conv('conv2', norm1, 96, 256, 5, 5, 1, 1, 'SAME')
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pool2 = _mpool('pool2', conv2, 3, 3, 2, 2)
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norm2 = _norm('norm2', pool2, lsize=5)
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conv3 = _conv('conv3', norm2, 256, 384, 3, 3, 1, 1, 'SAME')
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conv4 = _conv('conv4', conv3, 384, 384, 3, 3, 1, 1, 'SAME')
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conv5 = _conv('conv5', conv4, 384, 256, 3, 3, 1, 1, 'SAME')
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pool5 = _mpool('pool5', conv5, 3, 3, 2, 2)
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resh1 = tf.reshape(pool5, [-1, 256 * 6 * 6])
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affn1 = _affine('fc6', resh1, 256 * 6 * 6, 4096, 0.5)
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affn2 = _affine('fc7', affn1, 4096, 4096, 0.5)
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affn3 = _affine('fc8', affn2, 4096, 1000, wd=None, act=False) # last fc
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return affn3
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def time_tensorflow_run(session, target, info_string):
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num_steps_burn_in = 10
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total_duration = 0.0
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total_duration_squared = 0.0
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if not isinstance(target, list):
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target = [target]
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target_op = tf.group(*target)
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for i in xrange(FLAGS.num_batches + num_steps_burn_in):
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start_time = time.time()
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_ = session.run(target_op)
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duration = time.time() - start_time
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if i > num_steps_burn_in:
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if not i % 10:
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print('%s: step %d, duration = %.3f' %
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(datetime.now(), i - num_steps_burn_in, duration))
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total_duration += duration
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total_duration_squared += duration * duration
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mn = total_duration / FLAGS.num_batches
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vr = total_duration_squared / FLAGS.num_batches - mn * mn
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sd = math.sqrt(vr)
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print('%s: %s across %d steps, %.3f +/- %.3f sec / batch' %
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(datetime.now(), info_string, FLAGS.num_batches, mn, sd))
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def _add_loss_summaries(total_loss):
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"""
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Generates moving average for all losses and associated summaries for
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visualizing the performance of the network.
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Args:
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total_loss: Total loss from loss().
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Returns:
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loss_averages_op: op for generating moving averages of losses.
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"""
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# Compute the moving average of all individual losses and the total loss.
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loss_averages = tf.train.ExponentialMovingAverage(0.9, name='avg')
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losses = tf.get_collection('losses')
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loss_averages_op = loss_averages.apply(losses + [total_loss])
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# Attach a scalar summary to all individual losses and the total loss; do the
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# same for the averaged version of the losses.
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for l in losses + [total_loss]:
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# Name each loss as '(raw)' and name the moving average version of the loss
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# as the original loss name.
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tf.scalar_summary(l.op.name + ' (raw)', l)
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tf.scalar_summary(l.op.name, loss_averages.average(l))
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return loss_averages_op
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def run_benchmark():
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with tf.Graph().as_default():
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with tf.device('/gpu:0'):
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# Generate some dummy images.
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image_size = 224
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# Note that our padding definition is slightly different the cuda-convnet.
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# In order to force the model to start with the same activations sizes,
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# we add 3 to the image_size and employ VALID padding above.
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if FLAGS.data_format == 'NCHW':
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image_shape = [
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FLAGS.batch_size, 3, image_size + 3, image_size + 3
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]
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else:
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image_shape = [
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FLAGS.batch_size, image_size + 3, image_size + 3, 3
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]
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images = tf.get_variable(
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'image',
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image_shape,
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initializer=tf.truncated_normal_initializer(
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stddev=0.1, dtype=tf.float32),
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dtype=tf.float32,
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trainable=False)
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labels = tf.get_variable(
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'label', [FLAGS.batch_size],
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initializer=tf.constant_initializer(1),
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dtype=tf.int32,
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trainable=False)
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# Build a Graph that computes the logits predictions from the
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# inference model.
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last_layer = inference(images)
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objective = loss(last_layer, labels)
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# Compute the gradient with respect to all the parameters.
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# Compute gradients.
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# opt = tf.train.GradientDescentOptimizer(0.001)
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opt = tf.train.MomentumOptimizer(0.001, 0.9)
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grads = opt.compute_gradients(objective)
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global_step = tf.get_variable(
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'global_step', [],
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initializer=tf.constant_initializer(
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0.0, dtype=tf.float32),
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trainable=False,
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dtype=tf.float32)
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apply_gradient_op = opt.apply_gradients(
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grads, global_step=global_step)
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# Track the moving averages of all trainable variables.
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variable_averages = tf.train.ExponentialMovingAverage(0.9,
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global_step)
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variables_averages_op = variable_averages.apply(
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tf.trainable_variables())
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# Build an initialization operation.
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init = tf.initialize_all_variables()
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# Start running operations on the Graph.
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sess = tf.Session(config=tf.ConfigProto(
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allow_soft_placement=True,
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log_device_placement=FLAGS.log_device_placement))
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sess.run(init)
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run_forward = True
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run_forward_backward = True
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if FLAGS.forward_only and FLAGS.forward_backward_only:
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raise ValueError("Cannot specify --forward_only and "
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"--forward_backward_only at the same time.")
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if FLAGS.forward_only:
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run_forward_backward = False
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elif FLAGS.forward_backward_only:
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run_forward = False
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if run_forward:
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time_tensorflow_run(sess, last_layer, "Forward")
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if run_forward_backward:
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with tf.control_dependencies(
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[apply_gradient_op, variables_averages_op]):
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train_op = tf.no_op(name='train')
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time_tensorflow_run(sess, [train_op, objective],
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"Forward-backward")
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def main(_):
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run_benchmark()
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if __name__ == '__main__':
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tf.app.run()
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