<s> import tensorflow as tf <EOL> from abc import ABCMeta , abstractmethod <EOL> from six . moves import range <EOL> import tqdm <EOL> import re <EOL> from . config import TrainConfig <EOL> from . . utils import * <EOL> from . . callbacks import StatHolder <EOL> from . . tfutils import * <EOL> from . . tfutils . modelutils import describe_model <EOL> __all__ = [ '<STR_LIT>' ] <EOL> class Trainer ( object ) : <EOL> """<STR_LIT>""" <EOL> __metaclass__ = ABCMeta <EOL> def __init__ ( self , config ) : <EOL> """<STR_LIT>""" <EOL> assert isinstance ( config , TrainConfig ) , type ( config ) <EOL> self . config = config <EOL> self . model = config . model <EOL> @ abstractmethod <EOL> def train ( self ) : <EOL> """<STR_LIT>""" <EOL> pass <EOL> @ abstractmethod <EOL> def run_step ( self ) : <EOL> """<STR_LIT>""" <EOL> pass <EOL> def trigger_epoch ( self ) : <EOL> self . _trigger_epoch ( ) <EOL> self . config . callbacks . trigger_epoch ( ) <EOL> self . summary_writer . flush ( ) <EOL> self . stat_holder . finalize ( ) <EOL> @ abstractmethod <EOL> def _trigger_epoch ( self ) : <EOL> """<STR_LIT>""" <EOL> pass <EOL> def _init_summary ( self ) : <EOL> if not hasattr ( logger , '<STR_LIT>' ) : <EOL> raise RuntimeError ( "<STR_LIT>" ) <EOL> self . summary_writer = tf . train . SummaryWriter ( <EOL> logger . LOG_DIR , graph = self . sess . graph ) <EOL> self . summary_op = tf . merge_all_summaries ( ) <EOL> self . stat_holder = StatHolder ( logger . LOG_DIR ) <EOL> def _process_summary ( self , summary_str ) : <EOL> summary = tf . Summary . FromString ( summary_str ) <EOL> for val in summary . value : <EOL> if val . WhichOneof ( '<STR_LIT:value>' ) == '<STR_LIT>' : <EOL> val . tag = re . sub ( '<STR_LIT>' , '<STR_LIT>' , val . tag ) <EOL> self . stat_holder . add_stat ( val . tag , val . simple_value ) <EOL> self . summary_writer . add_summary ( summary , self . global_step ) <EOL> def main_loop ( self ) : <EOL> logger . info ( "<STR_LIT>" ) <EOL> self . _init_summary ( ) <EOL> get_global_step_var ( ) <EOL> callbacks = self . config . callbacks <EOL> callbacks . before_train ( self ) <EOL> self . config . session_init . init ( self . sess ) <EOL> tf . get_default_graph ( ) . finalize ( ) <EOL> self . _start_all_threads ( ) <EOL> with self . sess . as_default ( ) : <EOL> try : <EOL> self . global_step = get_global_step ( ) <EOL> logger . info ( "<STR_LIT>" . format ( self . global_step ) ) <EOL> for epoch in range ( self . config . starting_epoch , self . config . max_epoch + <NUM_LIT:1> ) : <EOL> with timed_operation ( <EOL> '<STR_LIT>' . format ( <EOL> epoch , self . global_step + self . config . step_per_epoch ) ) : <EOL> for step in tqdm . trange ( <EOL> self . config . step_per_epoch , <EOL> leave = True , mininterval = <NUM_LIT:0.5> , <EOL> dynamic_ncols = True , ascii = True ) : <EOL> if self . coord . should_stop ( ) : <EOL> return <EOL> self . run_step ( ) <EOL> self . global_step += <NUM_LIT:1> <EOL> self . trigger_epoch ( ) <EOL> except ( KeyboardInterrupt , Exception ) : <EOL> raise <EOL> finally : <EOL> self . coord . request_stop ( ) <EOL> callbacks . after_train ( ) <EOL> self . summary_writer . close ( ) <EOL> self . sess . close ( ) <EOL> def init_session_and_coord ( self ) : <EOL> describe_model ( ) <EOL> self . sess = tf . Session ( config = self . config . session_config ) <EOL> self . coord = tf . train . Coordinator ( ) <EOL> def _start_all_threads ( self ) : <EOL> """<STR_LIT>""" <EOL> tf . train . start_queue_runners ( <EOL> sess = self . sess , coord = self . coord , daemon = True , start = True ) <EOL> def process_grads ( self , grads ) : <EOL> procs = self . config . model . get_gradient_processor ( ) <EOL> for proc in procs : <EOL> grads = proc . process ( grads ) <EOL> return grads </s>
