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.""") parameters = [] conv_counter = 1 pool_counter = 1 affine_counter = 1 def _conv(inpOp, nIn, nOut, kH, kW, dH, dW, padType, wd=0.005, act=True): global conv_counter global parameters name = 'conv' + str(conv_counter) conv_counter += 1 with tf.name_scope(name) as scope: kernel = tf.Variable(tf.truncated_normal([kH, kW, nIn, nOut], dtype=tf.float32, stddev=1e-1), name='weights') if wd is not None: 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.Variable(tf.constant(0.0, shape=[nOut], dtype=tf.float32), trainable=True, name='biases') bias = tf.reshape(tf.nn.bias_add(conv, biases, data_format=FLAGS.data_format), conv.get_shape()) conv1 = tf.nn.relu(bias, name=scope) if act else bias parameters += [kernel, biases] return conv1 def _affine(inpOp, nIn, nOut, wd=None, act=True): global affine_counter global parameters name = 'affine' + str(affine_counter) affine_counter += 1 with tf.name_scope(name) as scope: kernel = tf.Variable(tf.truncated_normal([nIn, nOut], dtype=tf.float32, stddev=1e-1), name='weights') if wd is not None: weight_decay = tf.mul(tf.nn.l2_loss(kernel), wd, name='weight_loss') tf.add_to_collection('losses', weight_decay) biases = tf.Variable(tf.constant(0.0, shape=[nOut], dtype=tf.float32), trainable=True, name='biases') affine1 = tf.nn.relu_layer(inpOp, kernel, biases, name=name) if act else tf.matmul(inpOp, kernel) + biases parameters += [kernel, biases] return affine1 def _mpool(inpOp, kH, kW, dH, dW, padding): global pool_counter global parameters name = 'pool' + str(pool_counter) pool_counter += 1 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=padding, data_format=FLAGS.data_format, name=name) def _apool(inpOp, kH, kW, dH, dW, padding): global pool_counter global parameters name = 'pool' + str(pool_counter) pool_counter += 1 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.avg_pool(inpOp, ksize=ksize, strides=strides, padding=padding, 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): batch_size = tf.size(labels) labels = tf.expand_dims(labels, 1) indices = tf.expand_dims(tf.range(0, batch_size, 1), 1) concated = tf.concat(1, [indices, labels]) onehot_labels = tf.sparse_to_dense( concated, tf.pack([batch_size, 10]), 1.0, 0.0) cross_entropy = tf.nn.softmax_cross_entropy_with_logits(logits, onehot_labels, name='xentropy') loss = tf.reduce_mean(cross_entropy, name='xentropy_mean') return 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 (images, 3, 32, 5, 5, 1, 1, 'SAME') pool1 = _mpool(conv1, 3, 3, 2, 2, 'SAME') conv2 = _conv (pool1, 32, 32, 5, 5, 1, 1, 'SAME') pool2 = _apool(conv2, 3, 3, 2, 2, 'SAME') conv3 = _conv (pool2, 32, 64, 5, 5, 1, 1, 'SAME') pool3 = _apool(conv3, 3, 3, 2, 2, 'SAME') resh1 = tf.reshape(pool3, [-1, 64 * 4 * 4]) affn1 = _affine(resh1, 64 * 4 * 4, 64) affn2 = _affine(affn1, 64, 10, act=False) print ('conv1:', get_incoming_shape(conv1)) print ('pool1:', get_incoming_shape(pool1)) print ('conv2:', get_incoming_shape(conv2)) print ('pool2:', get_incoming_shape(pool2)) print ('conv3:', get_incoming_shape(conv3)) print ('pool3:', get_incoming_shape(pool3)) return affn2 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 run_benchmark(): global parameters with tf.Graph().as_default(): # Generate some dummy images. image_size = 32 # 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, image_size] else: image_shape = [FLAGS.batch_size, image_size, image_size, 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 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: # Run the forward benchmark. 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()