Transfer learning on TensorFlow-Slim image classification model library
# Copyright 2016 The TensorFlow Authors. All Rights Reserved.## Licensed under the Apache License, Version 2.0 (the "License");# you may not use this file except in compliance with the License.# You may obtain a copy of the License at## http://www.apache.org/licenses/LICENSE-2.0## Unless required by applicable law or agreed to in writing, software# distributed under the License is distributed on an "AS IS" BASIS,# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.# See the License for the specific language governing permissions and# limitations under the License.# =============================================================================="""Generic training script that trains a model using a given dataset."""from __future__ import absolute_importfrom __future__ import divisionfrom __future__ import print_functionimport tensorflow.compat.v1 as tfimport tf_slim as slim#from tensorflow.contrib import quantize as contrib_quantizefrom datasets import dataset_factoryfrom deployment import model_deployfrom nets import nets_factoryfrom preprocessing import preprocessing_factorytf.compat.v1.disable_eager_execution()tf.app.flags.DEFINE_string( 'master', '', 'The address of the TensorFlow master to use.')tf.app.flags.DEFINE_string( 'train_dir', '/tmp/tfmodel/', 'Directory where checkpoints and event logs are written to.')tf.app.flags.DEFINE_float( 'warmup_epochs', 0, 'Linearly warmup learning rate from 0 to learning_rate over this ' 'many epochs.')tf.app.flags.DEFINE_integer('num_clones', 1, 'Number of model clones to deploy. Note For ' 'historical reasons loss from all clones averaged ' 'out and learning rate decay happen per clone ' 'epochs')tf.app.flags.DEFINE_boolean('clone_on_cpu', False, 'Use CPUs to deploy clones.')tf.app.flags.DEFINE_integer('worker_replicas', 1, 'Number of worker replicas.')tf.app.flags.DEFINE_integer( 'num_ps_tasks', 0, 'The number of parameter servers. If the value is 0, then the parameters ' 'are handled locally by the worker.')tf.app.flags.DEFINE_integer( 'num_readers', 4, 'The number of parallel readers that read data from the dataset.')tf.app.flags.DEFINE_integer( 'num_preprocessing_threads', 4, 'The number of threads used to create the batches.')tf.app.flags.DEFINE_integer( 'log_every_n_steps', 10, 'The frequency with which logs are print.')tf.app.flags.DEFINE_integer( 'save_summaries_secs', 600, 'The frequency with which summaries are saved, in seconds.')tf.app.flags.DEFINE_integer( 'save_interval_secs', 600, 'The frequency with which the model is saved, in seconds.')tf.app.flags.DEFINE_integer( 'task', 0, 'Task id of the replica running the training.')####################### Optimization Flags #######################tf.app.flags.DEFINE_float( 'weight_decay', 0.00004, 'The weight decay on the model weights.')tf.app.flags.DEFINE_string( 'optimizer', 'rmsprop', 'The name of the optimizer, one of "adadelta", "adagrad", "adam",' '"ftrl", "momentum", "sgd" or "rmsprop".')tf.app.flags.DEFINE_float( 'adadelta_rho', 0.95, 'The decay rate for adadelta.')tf.app.flags.DEFINE_float( 'adagrad_initial_accumulator_value', 0.1, 'Starting value for the AdaGrad accumulators.')tf.app.flags.DEFINE_float( 'adam_beta1', 0.9, 'The exponential decay rate for the 1st moment estimates.')tf.app.flags.DEFINE_float( 'adam_beta2', 0.999, 'The exponential decay rate for the 2nd moment estimates.')tf.app.flags.DEFINE_float('opt_epsilon', 1.0, 'Epsilon term for the optimizer.')tf.app.flags.DEFINE_float('ftrl_learning_rate_power', -0.5, 'The learning rate power.')tf.app.flags.DEFINE_float( 'ftrl_initial_accumulator_value', 0.1, 'Starting value for the FTRL accumulators.')tf.app.flags.DEFINE_float( 'ftrl_l1', 0.0, 'The FTRL l1 regularization strength.')tf.app.flags.DEFINE_float( 'ftrl_l2', 0.0, 'The FTRL l2 regularization strength.')tf.app.flags.DEFINE_float( 'momentum', 0.9, 'The momentum for the MomentumOptimizer and RMSPropOptimizer.')tf.app.flags.DEFINE_float('rmsprop_momentum', 0.9, 'Momentum.')tf.app.flags.DEFINE_float('rmsprop_decay', 0.9, 'Decay term for RMSProp.')tf.app.flags.DEFINE_integer( 'quantize_delay', -1, 'Number of steps to start quantized training. Set to -1 would disable ' 'quantized training.')######################## Learning Rate Flags ########################tf.app.flags.DEFINE_string( 'learning_rate_decay_type', 'exponential', 'Specifies how the learning rate is decayed. One of "fixed", "exponential",' ' or "polynomial"')tf.app.flags.DEFINE_float('learning_rate', 0.01, 'Initial learning rate.')tf.app.flags.DEFINE_float( 'end_learning_rate', 0.0001, 'The minimal end learning rate used by a polynomial decay learning rate.')tf.app.flags.DEFINE_float( 'label_smoothing', 0.0, 'The amount of label smoothing.')tf.app.flags.DEFINE_float( 'learning_rate_decay_factor', 0.94, 'Learning rate decay factor.')tf.app.flags.DEFINE_float( 'num_epochs_per_decay', 2.0, 'Number of epochs after which learning rate decays. Note: this flag counts ' 'epochs per clone but aggregates per sync replicas. So 1.0 means that ' 'each clone will go over full epoch individually, but replicas will go ' 'once across all replicas.')tf.app.flags.DEFINE_bool( 'sync_replicas', False, 'Whether or not to synchronize the replicas during training.')tf.app.flags.DEFINE_integer( 'replicas_to_aggregate', 1, 'The Number of gradients to collect before updating params.')tf.app.flags.DEFINE_float( 'moving_average_decay', None, 'The decay to use for the moving average.' 'If left as None, then moving averages are not used.')######################## Dataset Flags ########################tf.app.flags.DEFINE_string( 'dataset_name', 'imagenet', 'The name of the dataset to load.')tf.app.flags.DEFINE_string( 'dataset_split_name', 'train', 'The name of the train/test split.')tf.app.flags.DEFINE_string( 'dataset_dir', None, 'The directory where the dataset files are stored.')tf.app.flags.DEFINE_integer( 'labels_offset', 0, 'An offset for the labels in the dataset. This flag is primarily used to ' 'evaluate the VGG and ResNet architectures which do not use a background ' 'class for the ImageNet dataset.')tf.app.flags.DEFINE_string( 'model_name', 'inception_v3', 'The name of the architecture to train.')tf.app.flags.DEFINE_string( 'preprocessing_name', None, 'The name of the preprocessing to use. If left ' 'as `None`, then the model_name flag is used.')tf.app.flags.DEFINE_integer( 'batch_size', 32, 'The number of samples in each batch.')tf.app.flags.DEFINE_integer( 'train_image_size', None, 'Train image size')tf.app.flags.DEFINE_integer('max_number_of_steps', None, 'The maximum number of training steps.')tf.app.flags.DEFINE_bool('use_grayscale', False, 'Whether to convert input images to grayscale.')###################### Fine-Tuning Flags ######################tf.app.flags.DEFINE_string( 'checkpoint_path', None, 'The path to a checkpoint from which to fine-tune.')tf.app.flags.DEFINE_string( 'checkpoint_exclude_scopes', None, 'Comma-separated list of scopes of variables to exclude when restoring ' 'from a checkpoint.')tf.app.flags.DEFINE_string( 'trainable_scopes', None, 'Comma-separated list of scopes to filter the set of variables to train.' 'By default, None would train all the variables.')tf.app.flags.DEFINE_boolean( 'ignore_missing_vars', False, 'When restoring a checkpoint would ignore missing variables.')FLAGS = tf.app.flags.FLAGSdef _configure_learning_rate(num_samples_per_epoch, global_step): """Configures the learning rate. Args: num_samples_per_epoch: The number of samples in each epoch of training. global_step: The global_step tensor. Returns: A `Tensor` representing the learning rate. Raises: ValueError: if """ # Note: when num_clones is > 1, this will actually have each clone to go # over each epoch FLAGS.num_epochs_per_decay times. This is different # behavior from sync replicas and is expected to produce different results. steps_per_epoch = num_samples_per_epoch / FLAGS.batch_size if FLAGS.sync_replicas: steps_per_epoch /= FLAGS.replicas_to_aggregate decay_steps = int(steps_per_epoch * FLAGS.num_epochs_per_decay) if FLAGS.learning_rate_decay_type == 'exponential': learning_rate = tf.train.exponential_decay( FLAGS.learning_rate, global_step, decay_steps, FLAGS.learning_rate_decay_factor, staircase=True, name='exponential_decay_learning_rate') elif FLAGS.learning_rate_decay_type == 'fixed': learning_rate = tf.constant(FLAGS.learning_rate, name='fixed_learning_rate') elif FLAGS.learning_rate_decay_type == 'polynomial': learning_rate = tf.train.polynomial_decay( FLAGS.learning_rate, global_step, decay_steps, FLAGS.end_learning_rate, power=1.0, cycle=False, name='polynomial_decay_learning_rate') else: raise ValueError('learning_rate_decay_type [%s] was not recognized' % FLAGS.learning_rate_decay_type) if FLAGS.warmup_epochs: warmup_lr = ( FLAGS.learning_rate * tf.cast(global_step, tf.float32) / (steps_per_epoch * FLAGS.warmup_epochs)) learning_rate = tf.minimum(warmup_lr, learning_rate) return learning_ratedef _configure_optimizer(learning_rate): """Configures the optimizer used for training. Args: learning_rate: A scalar or `Tensor` learning rate. Returns: An instance of an optimizer. Raises: ValueError: if FLAGS.optimizer is not recognized. """ if FLAGS.optimizer == 'adadelta': optimizer = tf.train.AdadeltaOptimizer( learning_rate, rho=FLAGS.adadelta_rho, epsilon=FLAGS.opt_epsilon) elif FLAGS.optimizer == 'adagrad': optimizer = tf.train.AdagradOptimizer( learning_rate, initial_accumulator_value=FLAGS.adagrad_initial_accumulator_value) elif FLAGS.optimizer == 'adam': optimizer = tf.train.AdamOptimizer( learning_rate, beta1=FLAGS.adam_beta1, beta2=FLAGS.adam_beta2, epsilon=FLAGS.opt_epsilon) elif FLAGS.optimizer == 'ftrl': optimizer = tf.train.FtrlOptimizer( learning_rate, learning_rate_power=FLAGS.ftrl_learning_rate_power, initial_accumulator_value=FLAGS.ftrl_initial_accumulator_value, l1_regularization_strength=FLAGS.ftrl_l1, l2_regularization_strength=FLAGS.ftrl_l2) elif FLAGS.optimizer == 'momentum': optimizer = tf.train.MomentumOptimizer( learning_rate, momentum=FLAGS.momentum, name='Momentum') elif FLAGS.optimizer == 'rmsprop': optimizer = tf.train.RMSPropOptimizer( learning_rate, decay=FLAGS.rmsprop_decay, momentum=FLAGS.rmsprop_momentum, epsilon=FLAGS.opt_epsilon) elif FLAGS.optimizer == 'sgd': optimizer = tf.train.GradientDescentOptimizer(learning_rate) else: raise ValueError('Optimizer [%s] was not recognized' % FLAGS.optimizer) return optimizerdef _get_init_fn(): """Returns a function run by the chief worker to warm-start the training. Note that the init_fn is only run when initializing the model during the very first global step. Returns: An init function run by the supervisor. """ if FLAGS.checkpoint_path is None: return None # Warn the user if a checkpoint exists in the train_dir. Then we'll be # ignoring the checkpoint anyway. if tf.train.latest_checkpoint(FLAGS.train_dir): tf.logging.info( 'Ignoring --checkpoint_path because a checkpoint already exists in %s' % FLAGS.train_dir) return None exclusions = [] if FLAGS.checkpoint_exclude_scopes: exclusions = [scope.strip() for scope in FLAGS.checkpoint_exclude_scopes.split(',')] # TODO(sguada) variables.filter_variables() variables_to_restore = [] for var in slim.get_model_variables(): for exclusion in exclusions: if var.op.name.startswith(exclusion): break else: variables_to_restore.append(var) if tf.gfile.IsDirectory(FLAGS.checkpoint_path): checkpoint_path = tf.train.latest_checkpoint(FLAGS.checkpoint_path) else: checkpoint_path = FLAGS.checkpoint_path tf.logging.info('Fine-tuning from %s' % checkpoint_path) return slim.assign_from_checkpoint_fn( checkpoint_path, variables_to_restore, ignore_missing_vars=FLAGS.ignore_missing_vars)def _get_variables_to_train(): """Returns a list of variables to train. Returns: A list of variables to train by the optimizer. """ if FLAGS.trainable_scopes is None: return tf.trainable_variables() else: scopes = [scope.strip() for scope in FLAGS.trainable_scopes.split(',')] variables_to_train = [] for scope in scopes: variables = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, scope) variables_to_train.extend(variables) return variables_to_traindef main(_): if not FLAGS.dataset_dir: raise ValueError('You must supply the dataset directory with --dataset_dir') tf.logging.set_verbosity(tf.logging.INFO) with tf.Graph().as_default(): ####################### # Config model_deploy # ####################### deploy_config = model_deploy.DeploymentConfig( num_clones=FLAGS.num_clones, clone_on_cpu=FLAGS.clone_on_cpu, replica_id=FLAGS.task, num_replicas=FLAGS.worker_replicas, num_ps_tasks=FLAGS.num_ps_tasks) # Create global_step with tf.device(deploy_config.variables_device()): global_step = slim.create_global_step() ###################### # Select the dataset # ###################### dataset = dataset_factory.get_dataset( FLAGS.dataset_name, FLAGS.dataset_split_name, FLAGS.dataset_dir) ###################### # Select the network # ###################### network_fn = nets_factory.get_network_fn( FLAGS.model_name, num_classes=(dataset.num_classes - FLAGS.labels_offset), weight_decay=FLAGS.weight_decay, is_training=True) ##################################### # Select the preprocessing function # ##################################### preprocessing_name = FLAGS.preprocessing_name or FLAGS.model_name image_preprocessing_fn = preprocessing_factory.get_preprocessing( preprocessing_name, is_training=True, use_grayscale=FLAGS.use_grayscale) ############################################################## # Create a dataset provider that loads data from the dataset # ############################################################## with tf.device(deploy_config.inputs_device()): provider = slim.dataset_data_provider.DatasetDataProvider( dataset, num_readers=FLAGS.num_readers, common_queue_capacity=20 * FLAGS.batch_size, common_queue_min=10 * FLAGS.batch_size) [image, label] = provider.get(['image', 'label']) label -= FLAGS.labels_offset train_image_size = FLAGS.train_image_size or network_fn.default_image_size image = image_preprocessing_fn(image, train_image_size, train_image_size) images, labels = tf.train.batch( [image, label], batch_size=FLAGS.batch_size, num_threads=FLAGS.num_preprocessing_threads, capacity=5 * FLAGS.batch_size) labels = slim.one_hot_encoding( labels, dataset.num_classes - FLAGS.labels_offset) batch_queue = slim.prefetch_queue.prefetch_queue( [images, labels], capacity=2 * deploy_config.num_clones) #################### # Define the model # #################### def clone_fn(batch_queue): """Allows data parallelism by creating multiple clones of network_fn.""" images, labels = batch_queue.dequeue() logits, end_points = network_fn(images) ############################# # Specify the loss function # ############################# if 'AuxLogits' in end_points: slim.losses.softmax_cross_entropy( end_points['AuxLogits'], labels, label_smoothing=FLAGS.label_smoothing, weights=0.4, scope='aux_loss') slim.losses.softmax_cross_entropy( logits, labels, label_smoothing=FLAGS.label_smoothing, weights=1.0) return end_points # Gather initial summaries. summaries = set(tf.get_collection(tf.GraphKeys.SUMMARIES)) clones = model_deploy.create_clones(deploy_config, clone_fn, [batch_queue]) first_clone_scope = deploy_config.clone_scope(0) # Gather update_ops from the first clone. These contain, for example, # the updates for the batch_norm variables created by network_fn. update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS, first_clone_scope) # Add summaries for end_points. end_points = clones[0].outputs for end_point in end_points: x = end_points[end_point] summaries.add(tf.summary.histogram('activations/' + end_point, x)) summaries.add(tf.summary.scalar('sparsity/' + end_point, tf.nn.zero_fraction(x))) # Add summaries for losses. for loss in tf.get_collection(tf.GraphKeys.LOSSES, first_clone_scope): summaries.add(tf.summary.scalar('losses/%s' % loss.op.name, loss)) # Add summaries for variables. for variable in slim.get_model_variables(): summaries.add(tf.summary.histogram(variable.op.name, variable)) ################################# # Configure the moving averages # ################################# if FLAGS.moving_average_decay: moving_average_variables = slim.get_model_variables() variable_averages = tf.train.ExponentialMovingAverage( FLAGS.moving_average_decay, global_step) else: moving_average_variables, variable_averages = None, None if FLAGS.quantize_delay >= 0: contrib_quantize.create_training_graph(quant_delay=FLAGS.quantize_delay) ######################################### # Configure the optimization procedure. # ######################################### with tf.device(deploy_config.optimizer_device()): learning_rate = _configure_learning_rate(dataset.num_samples, global_step) optimizer = _configure_optimizer(learning_rate) summaries.add(tf.summary.scalar('learning_rate', learning_rate)) if FLAGS.sync_replicas: # If sync_replicas is enabled, the averaging will be done in the chief # queue runner. optimizer = tf.train.SyncReplicasOptimizer( opt=optimizer, replicas_to_aggregate=FLAGS.replicas_to_aggregate, total_num_replicas=FLAGS.worker_replicas, variable_averages=variable_averages, variables_to_average=moving_average_variables) elif FLAGS.moving_average_decay: # Update ops executed locally by trainer. update_ops.append(variable_averages.apply(moving_average_variables)) # Variables to train. variables_to_train = _get_variables_to_train() # and returns a train_tensor and summary_op total_loss, clones_gradients = model_deploy.optimize_clones( clones, optimizer, var_list=variables_to_train) # Add total_loss to summary. summaries.add(tf.summary.scalar('total_loss', total_loss)) # Create gradient updates. grad_updates = optimizer.apply_gradients(clones_gradients, global_step=global_step) update_ops.append(grad_updates) update_op = tf.group(*update_ops) with tf.control_dependencies([update_op]): train_tensor = tf.identity(total_loss, name='train_op') # Add the summaries from the first clone. These contain the summaries # created by model_fn and either optimize_clones() or _gather_clone_loss(). summaries |= set(tf.get_collection(tf.GraphKeys.SUMMARIES, first_clone_scope)) # Merge all summaries together. summary_op = tf.summary.merge(list(summaries), name='summary_op') ########################### # Kicks off the training. # ########################### #sess = tf.Session() #saver = tf.train.import_meta_graph('/home/ga63laq/slim-transfer-learning/checkpoints/v3-large-minimalistic_224_1.0_float/pristine/model.ckpt-342500.meta') #saver.restore(sess, "/home/ga63laq/slim-transfer-learning/checkpoints/v3-large-minimalistic_224_1.0_float/pristine/model.ckpt-493500.data-00000-of-00001") slim.learning.train( train_tensor, logdir=FLAGS.train_dir, master=FLAGS.master, is_chief=(FLAGS.task == 0), #saver=saver, init_fn=_get_init_fn(), summary_op=summary_op, number_of_steps=FLAGS.max_number_of_steps, log_every_n_steps=FLAGS.log_every_n_steps, save_summaries_secs=FLAGS.save_summaries_secs, save_interval_secs=FLAGS.save_interval_secs, sync_optimizer=optimizer if FLAGS.sync_replicas else None)if __name__ == '__main__': tf.app.run()