from six.moves import xrange # pylint: disable=redefined-builtin from datetime import datetime import math import time import tensorflow.python.platform import tensorflow as tf FLAGS = tf.app.flags.FLAGS tf.app.flags.DEFINE_integer('batch_size', 128, """Batch size.""") tf.app.flags.DEFINE_integer('num_batches', 100, """Number of batches to run.""") tf.app.flags.DEFINE_boolean('forward_only', False, """Only run the forward pass.""") tf.app.flags.DEFINE_boolean('forward_backward_only', False, """Only run the forward-forward pass.""") tf.app.flags.DEFINE_string('data_format', 'NCHW', """The data format for Convnet operations. Can be either NHWC or NCHW. """) tf.app.flags.DEFINE_boolean('log_device_placement', False, """Whether to log device placement.""") def _conv(name, inpOp, nIn, nOut, kH, kW, dH, dW, padType, wd=0.0005): with tf.name_scope(name) as scope: kernel = tf.get_variable(name + '_w',[kH, kW, nIn, nOut], initializer=tf.truncated_normal_initializer(stddev=0.01, dtype=tf.float32), dtype=tf.float32) if wd is not None and wd > 0: weight_decay = tf.mul(tf.nn.l2_loss(kernel), wd, name='weight_loss') tf.add_to_collection('losses', weight_decay) if FLAGS.data_format == 'NCHW': strides = [1, 1, dH, dW] else: strides = [1, dH, dW, 1] conv = tf.nn.conv2d(inpOp, kernel, strides, padding=padType, data_format=FLAGS.data_format) biases = tf.get_variable(name=name + '_b', shape=[nOut], initializer=tf.constant_initializer(value=0.0, dtype=tf.float32), dtype=tf.float32) bias = tf.reshape( tf.nn.bias_add(conv, biases, data_format=FLAGS.data_format), conv.get_shape()) conv1 = tf.nn.relu(bias, name=scope) return conv1 def _affine(name, inpOp, nIn, nOut, wd=0.0005, act=True, drop=None): with tf.name_scope(name) as scope: kernel = tf.get_variable(name + '_w', [nIn, nOut], initializer=tf.truncated_normal_initializer(stddev=0.01, dtype=tf.float32), dtype=tf.float32) if wd is not None and wd > 0: weight_decay = tf.mul(tf.nn.l2_loss(kernel), wd, name='weight_loss') tf.add_to_collection('losses', weight_decay) biases = tf.get_variable(name + '_b', [nOut], initializer=tf.constant_initializer(value=0.0, dtype=tf.float32), dtype=tf.float32,trainable=True) affine1 = tf.nn.relu_layer(inpOp, kernel, biases, name=name) if act else \ tf.matmul(inpOp, kernel) + biases output = tf.nn.dropout(affine1, drop) if drop else affine1 return output def _mpool(name, inpOp, kH, kW, dH, dW): if FLAGS.data_format == 'NCHW': ksize = [1, 1, kH, kW] strides = [1, 1, dH, dW] else: ksize = [1, kH, kW, 1] strides = [1, dH, dW, 1] return tf.nn.max_pool(inpOp, ksize=ksize, strides=strides, padding='VALID', data_format=FLAGS.data_format, name=name) def _norm(name, l_input, lsize=4): return tf.nn.lrn(l_input, lsize, bias=1.0, alpha=0.001 / 9.0, beta=0.75, name=name) def loss(logits, labels): labels = tf.cast(labels, tf.int64) cross_entropy = tf.nn.sparse_softmax_cross_entropy_with_logits( logits, labels, name='cross_entropy_per_example') cross_entropy_mean = tf.reduce_mean(cross_entropy, name='cross_entropy') tf.add_to_collection('losses', cross_entropy_mean) # The total loss is defined as the cross entropy loss plus all of the weight # decay terms (L2 loss). return tf.add_n(tf.get_collection('losses'), name='total_loss') def get_incoming_shape(incoming): """ Returns the incoming data shape """ if isinstance(incoming, tf.Tensor): return incoming.get_shape().as_list() elif type(incoming) in [np.array, list, tuple]: return np.shape(incoming) else: raise Exception("Invalid incoming layer.") def inference(images): conv1 = _conv ('conv1', images, 3, 96, 11, 11, 4, 4, 'VALID') pool1 = _mpool('pool1', conv1, 3, 3, 2, 2) norm1 = _norm ('norm1', pool1, lsize=5) conv2 = _conv ('conv2', norm1, 96, 256, 5, 5, 1, 1, 'SAME') pool2 = _mpool('pool2', conv2, 3, 3, 2, 2) norm2 = _norm ('norm2', pool2, lsize=5) conv3 = _conv ('conv3', norm2, 256, 384, 3, 3, 1, 1, 'SAME') conv4 = _conv ('conv4', conv3, 384, 384, 3, 3, 1, 1, 'SAME') conv5 = _conv ('conv5', conv4, 384, 256, 3, 3, 1, 1, 'SAME') pool5 = _mpool('pool5', conv5, 3, 3, 2, 2) resh1 = tf.reshape(pool5, [-1, 256 * 6 * 6]) affn1 = _affine('fc6', resh1, 256 * 6 * 6, 4096, 0.5) affn2 = _affine('fc7', affn1, 4096, 4096, 0.5) affn3 = _affine('fc8', affn2, 4096, 1000, wd=None, act=False) # last fc return affn3 def time_tensorflow_run(session, target, info_string): num_steps_burn_in = 10 total_duration = 0.0 total_duration_squared = 0.0 if not isinstance(target, list): target = [target] target_op = tf.group(*target) for i in xrange(FLAGS.num_batches + num_steps_burn_in): start_time = time.time() _ = session.run(target_op) 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 / FLAGS.num_batches vr = total_duration_squared / FLAGS.num_batches - mn * mn sd = math.sqrt(vr) print ('%s: %s across %d steps, %.3f +/- %.3f sec / batch' % (datetime.now(), info_string, FLAGS.num_batches, mn, sd)) def _add_loss_summaries(total_loss): """ Generates moving average for all losses and associated summaries for visualizing the performance of the network. Args: total_loss: Total loss from loss(). Returns: loss_averages_op: op for generating moving averages of losses. """ # Compute the moving average of all individual losses and the total loss. loss_averages = tf.train.ExponentialMovingAverage(0.9, name='avg') losses = tf.get_collection('losses') loss_averages_op = loss_averages.apply(losses + [total_loss]) # Attach a scalar summary to all individual losses and the total loss; do the # same for the averaged version of the losses. for l in losses + [total_loss]: # Name each loss as '(raw)' and name the moving average version of the loss # as the original loss name. tf.scalar_summary(l.op.name +' (raw)', l) tf.scalar_summary(l.op.name, loss_averages.average(l)) return loss_averages_op def run_benchmark(): with tf.Graph().as_default(): with tf.device('/gpu:0'): # Generate some dummy images. image_size = 224 # Note that our padding definition is slightly different the cuda-convnet. # In order to force the model to start with the same activations sizes, # we add 3 to the image_size and employ VALID padding above. if FLAGS.data_format == 'NCHW': image_shape = [FLAGS.batch_size, 3, image_size + 3, image_size + 3] else: image_shape = [FLAGS.batch_size, image_size + 3, image_size + 3, 3] images = tf.get_variable('image', image_shape, initializer=tf.truncated_normal_initializer(stddev=0.1, dtype=tf.float32), dtype=tf.float32, trainable=False) labels = tf.get_variable('label', [FLAGS.batch_size], initializer=tf.constant_initializer(1), dtype=tf.int32, trainable=False) # Build a Graph that computes the logits predictions from the # inference model. last_layer = inference(images) objective = loss(last_layer, labels) # Compute the gradient with respect to all the parameters. # Compute gradients. # opt = tf.train.GradientDescentOptimizer(0.001) opt = tf.train.MomentumOptimizer(0.001, 0.9) grads = opt.compute_gradients(objective) global_step = tf.get_variable('global_step', [], initializer=tf.constant_initializer(0.0, dtype=tf.float32), trainable=False, dtype=tf.float32) apply_gradient_op = opt.apply_gradients(grads, global_step=global_step) # Track the moving averages of all trainable variables. variable_averages = tf.train.ExponentialMovingAverage( 0.9, global_step) variables_averages_op = variable_averages.apply(tf.trainable_variables()) # Build an initialization operation. init = tf.initialize_all_variables() # Start running operations on the Graph. sess = tf.Session(config=tf.ConfigProto( allow_soft_placement=True, log_device_placement=FLAGS.log_device_placement)) sess.run(init) run_forward = True run_forward_backward = True if FLAGS.forward_only and FLAGS.forward_backward_only: raise ValueError("Cannot specify --forward_only and " "--forward_backward_only at the same time.") if FLAGS.forward_only: run_forward_backward = False elif FLAGS.forward_backward_only: run_forward = False if run_forward: time_tensorflow_run(sess, last_layer, "Forward") if run_forward_backward: with tf.control_dependencies([apply_gradient_op, variables_averages_op]): train_op = tf.no_op(name='train') time_tensorflow_run(sess, [train_op, objective], "Forward-backward") def main(_): run_benchmark() if __name__ == '__main__': tf.app.run()