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312 lines
10 KiB
312 lines
10 KiB
from six.moves import xrange
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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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parameters = []
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conv_counter = 1
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pool_counter = 1
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affine_counter = 1
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def _conv(inpOp, nIn, nOut, kH, kW, dH, dW, padType, wd=0.0005):
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global conv_counter
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global parameters
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name = 'conv' + str(conv_counter)
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conv_counter += 1
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with tf.name_scope(name) as scope:
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kernel = tf.Variable(
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tf.truncated_normal(
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[kH, kW, nIn, nOut], dtype=tf.float32, stddev=1e-1),
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name='weights')
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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.Variable(
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tf.constant(
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0.0, shape=[nOut], dtype=tf.float32),
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trainable=True,
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name='biases')
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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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parameters += [kernel, biases]
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return conv1
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def _affine(inpOp, nIn, nOut, act=True, wd=0.0005):
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global affine_counter
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global parameters
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name = 'affine' + str(affine_counter)
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affine_counter += 1
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with tf.name_scope(name) as scope:
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kernel = tf.Variable(
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tf.truncated_normal(
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[nIn, nOut], dtype=tf.float32, stddev=1e-1),
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name='weights')
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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.Variable(
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tf.constant(
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0.0, shape=[nOut], dtype=tf.float32),
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trainable=True,
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name='biases')
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affine1 = tf.nn.relu_layer(
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inpOp, kernel, biases,
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name=name) if act else tf.matmul(inpOp, kernel) + biases
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parameters += [kernel, biases]
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return affine1
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def _mpool(inpOp, kH, kW, dH, dW, padding):
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global pool_counter
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global parameters
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name = 'pool' + str(pool_counter)
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pool_counter += 1
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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=padding,
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data_format=FLAGS.data_format,
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name=name)
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def _apool(inpOp, kH, kW, dH, dW, padding):
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global pool_counter
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global parameters
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name = 'pool' + str(pool_counter)
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pool_counter += 1
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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.avg_pool(
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inpOp,
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ksize=ksize,
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strides=strides,
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padding=padding,
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data_format=FLAGS.data_format,
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name=name)
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def _inception(inp, inSize, o1s, o2s1, o2s2, o3s1, o3s2, o4s1, o4s2):
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conv1 = _conv(inp, inSize, o1s, 1, 1, 1, 1, 'VALID')
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conv3_ = _conv(inp, inSize, o2s1, 1, 1, 1, 1, 'VALID')
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conv3 = _conv(conv3_, o2s1, o2s2, 3, 3, 1, 1, 'SAME')
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conv5_ = _conv(inp, inSize, o3s1, 1, 1, 1, 1, 'VALID')
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conv5 = _conv(conv5_, o3s1, o3s2, 5, 5, 1, 1, 'SAME')
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pool_ = _mpool(inp, o4s1, o4s1, 1, 1, 'SAME')
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pool = _conv(pool_, inSize, o4s2, 1, 1, 1, 1, 'VALID')
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if FLAGS.data_format == 'NCHW':
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channel_dim = 1
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else:
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channel_dim = 3
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incept = tf.concat(channel_dim, [conv1, conv3, conv5, pool])
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return incept
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def loss(logits, labels):
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batch_size = tf.size(labels)
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labels = tf.expand_dims(labels, 1)
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indices = tf.expand_dims(tf.range(0, batch_size, 1), 1)
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concated = tf.concat(1, [indices, labels])
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onehot_labels = tf.sparse_to_dense(concated,
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tf.pack([batch_size, 1000]), 1.0, 0.0)
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cross_entropy = tf.nn.softmax_cross_entropy_with_logits(
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logits, onehot_labels, name='xentropy')
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loss = tf.reduce_mean(cross_entropy, name='xentropy_mean')
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return loss
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def inference(images):
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# stage 1
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conv1 = _conv(images, 3, 64, 7, 7, 2, 2, 'SAME')
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pool1 = _mpool(conv1, 3, 3, 2, 2, 'SAME')
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# stage 2
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conv2 = _conv(pool1, 64, 64, 1, 1, 1, 1, 'VALID')
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conv3 = _conv(conv2, 64, 192, 3, 3, 1, 1, 'SAME')
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pool3 = _mpool(conv3, 3, 3, 2, 2, 'SAME')
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# stage 3
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incept3a = _inception(pool3, 192, 64, 96, 128, 16, 32, 3, 32)
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incept3b = _inception(incept3a, 256, 128, 128, 192, 32, 96, 3, 64)
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pool4 = _mpool(incept3b, 3, 3, 2, 2, 'SAME')
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# stage 4
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incept4a = _inception(pool4, 480, 192, 96, 208, 16, 48, 3, 64)
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incept4b = _inception(incept4a, 512, 160, 112, 224, 24, 64, 3, 64)
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incept4c = _inception(incept4b, 512, 128, 128, 256, 24, 64, 3, 64)
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incept4d = _inception(incept4c, 512, 112, 144, 288, 32, 64, 3, 64)
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incept4e = _inception(incept4d, 528, 256, 160, 320, 32, 128, 3, 128)
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pool5 = _mpool(incept4e, 3, 3, 2, 2, 'SAME')
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# stage 5
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incept5a = _inception(pool5, 832, 256, 160, 320, 32, 128, 3, 128)
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incept5b = _inception(incept5a, 832, 384, 192, 384, 48, 128, 3, 128)
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pool6 = _apool(incept5b, 7, 7, 1, 1, 'VALID')
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# output 1
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resh1 = tf.reshape(pool6, [-1, 1024])
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drop = tf.nn.dropout(resh1, 0.4)
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affn1 = _affine(resh1, 1024, 1000, act=False)
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return affn1
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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 range(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 run_benchmark():
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global parameters
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with tf.Graph().as_default():
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# Generate some dummy images.
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image_size = 224
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if FLAGS.data_format == 'NCHW':
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image_shape = [FLAGS.batch_size, 3, image_size, image_size]
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else:
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image_shape = [FLAGS.batch_size, image_size, image_size, 3]
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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 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(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, global_step)
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variables_averages_op = variable_averages.apply(tf.trainable_variables(
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))
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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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# Run the forward benchmark.
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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], "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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