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import os
import tensorflow as tf
import tf_utils as tfu
from tqdm import tqdm
from abc import ABC, abstractmethod
from utils import RANDOM
class TfContext:
def __init__(self,
sess,
saver,
learn_rate_op):
self.sess = sess
self.saver = saver
self.learn_rate_op = learn_rate_op
self.epoch = 0
class GraphPartBase(ABC):
def __init__(self, for_usage, global_settings, current_settings, optimiser, reset_optimiser, key, metric_names):
self.key = key
self.global_settings = global_settings
self.filler = global_settings['filler']
self.main_metric_name = current_settings['main_metric_type']
self.settings = current_settings
self.for_usage = for_usage
self.optimiser = optimiser
self.reset_optimiser = reset_optimiser
self.metric_names = metric_names
self.max_word_size = global_settings['max_word_size']
self.checkpoints_keep = 10000
self.chars_count = len(global_settings['chars']) + 1
self.dataset_path = global_settings['dataset_path']
self.grammemes_count = len(global_settings['grammemes_types'])
self.main_classes = global_settings['main_classes']
self.main_classes_count = len(self.main_classes)
self.metrics_reset = []
self.metrics_update = []
self.devices_metrics = {metr: [] for metr in self.metric_names}
self.main_scope_name = key.title()
self.save_path = global_settings['save_path']
self.dev_grads = []
self.losses = []
self.devices = global_settings['train_devices']
self.devices_count = len(self.devices)
self.dataset_path = global_settings['dataset_path']
self.xs = []
self.x_seq_lens = []
self.prints = []
self.main_cls_dic = self.global_settings['main_classes']
self.learn_rate_val = self.settings['learn_rate']
self.best_model_metric = None
self.best_epoch = None
self.init_checkpoint = None
def train(self, tc):
return_step = 0
trains = self.__load_dataset__('train')
valids = self.__load_dataset__('valid')
self.best_model_metric = self.__valid_loop__(tc, valids)
self.best_epoch = -1
while True:
tqdm.write(self.filler)
tqdm.write(self.filler)
tqdm.write(self.main_scope_name)
train_main_metric = self.__train_loop__(tc, trains)
valid_main_metric = self.__valid_loop__(tc, valids)
tqdm.write(f"Epoch {tc.epoch} Train {self.main_metric_name}: {train_main_metric} Validation {self.main_metric_name}: {valid_main_metric}")
need_decay = False
delta = self.__calc_metric_delta__(self.best_model_metric, valid_main_metric)
if delta > 0:
if delta < self.settings['stop_main_metric_delta']:
tqdm.write(f"{self.main_metric_name} delta is less then min value")
need_decay = True
else:
return_step = 0
self.best_model_metric = valid_main_metric
self.best_epoch = tc.epoch
tc.saver.save(tc.sess, self.save_path, tc.epoch)
tc.epoch += 1
else:
tqdm.write("Best epoch is better then current")
tc.sess.run(self.reset_optimiser)
need_decay = True
self.__restore_best_epoch__(tc)
if not need_decay:
continue
if return_step == self.settings['return_step']:
self.__decay_params__()
if self.learn_rate_val < self.settings['min_learn_rate']:
tqdm.write(f"Learning rate {self.learn_rate_val} is less then min learning rate")
finish = self.__before_finish__()
if finish:
break
return_step = 0
else:
RANDOM.shuffle(trains)
tqdm.write(f"Return step increased")
return_step += 1
return self.best_epoch, self.best_model_metric
def __train_loop__(self, tc, trains):
tc.sess.run(self.metrics_reset)
for item in tqdm(trains, desc=f"Train, epoch {tc.epoch}"):
launch = [self.optimize]
launch.extend(self.metrics_update)
if len(self.prints):
launch.extend(self.prints)
feed_dic = self.__create_feed_dict__('train', item)
feed_dic[tc.learn_rate_op] = self.learn_rate_val
tc.sess.run(launch, feed_dic)
train_main_metric = self.__write_metrics_report__(tc.sess, "Train")
return train_main_metric
def __valid_loop__(self, tc, valids):
tc.sess.run(self.metrics_reset)
for item in tqdm(valids, desc=f"Validation, epoch {tc.epoch}"):
launch = []
launch.extend(self.metrics_update)
if len(self.prints):
launch.extend(self.prints)
feed_dic = self.__create_feed_dict__('valid', item)
tc.sess.run(launch, feed_dic)
valid_main_metric = self.__write_metrics_report__(tc.sess, "Valid")
return valid_main_metric
def __calc_metric_delta__(self, best_model_metric, cur_model_metric):
delta = best_model_metric - cur_model_metric
if self.main_metric_name != "Loss":
delta = -delta
return delta
def __before_finish__(self):
return True
def __decay_params__(self):
self.learn_rate_val = self.learn_rate_val * self.settings['learn_rate_decay_step']
tqdm.write(f"Learning rate decayed. New value: {self.learn_rate_val}")
def __restore_best_epoch__(self, tc):
if self.best_epoch == -1 and tc.epoch == 0:
tqdm.write(f"Restoring from init_checkpoint {self.best_epoch}")
self.restore(tc.sess, self.init_checkpoint)
elif self.best_epoch == -1:
tqdm.write(f"Restoring best epoch {tc.epoch}")
self.restore(tc.sess, os.path.join(self.save_path, f"-{tc.epoch}"))
else:
tqdm.write(f"Restoring best epoch {self.best_epoch}")
self.restore(tc.sess, os.path.join(self.save_path, f"-{self.best_epoch}"))
def build_graph_end(self):
with tf.variable_scope(self.main_scope_name, reuse=tf.AUTO_REUSE) as scope:
self.metrics = {
metr: tf.reduce_mean(self.devices_metrics[metr], name=metr)
for metr in self.devices_metrics
}
if not self.for_usage:
self.grads = tfu.average_gradients(self.dev_grads)
if self.settings['clip_grads']:
self.grads = [(tf.clip_by_value(grad, -1., 1.), var) for grad, var in self.grads]
self.optimize = self.optimiser.apply_gradients(self.grads, name='Optimize')
self.loss = tf.reduce_sum(self.losses, name='GlobalLoss')
def build_graph_for_device(self, *args):
with tf.variable_scope(self.main_scope_name, reuse=tf.AUTO_REUSE) as scope:
self.__build_graph_for_device__(*args)
def restore(self, sess, check_point):
try:
vars = [
var
for var in tf.global_variables(f"{self.main_scope_name}/")
if "Adam" not in var.name
]
saver = tf.train.Saver(var_list=vars)
saver.restore(sess, check_point)
self.init_checkpoint = check_point
tqdm.write(f"Restoration for graph part '{self.key}', scope {self.main_scope_name} success")
except Exception as ex:
tqdm.write(f"Restoration for graph part '{self.key}', scope {self.main_scope_name} failed. Error: {ex}")
def __write_metrics_report__(self, sess, step_name):
tqdm.write('')
launch_results = sess.run(self.metrics)
result = [f"{step_name} metrics: "]
for index, metr in enumerate(self.metrics):
result.append('{:>8}'.format(self.metric_names[index]))
result.append("=")
result.append("{0:.7f}".format(launch_results[metr]))
result.append(" ")
result = "".join(result)
tqdm.write(result)
return launch_results[self.main_metric_name]
def create_mean_metric(self, metric_index, values):
metr_epoch_loss, metr_update, metr_reset = tfu.create_reset_metric(
tf.metrics.mean,
self.metric_names[metric_index],
values
)
self.metrics_reset.append(metr_reset)
self.metrics_update.append(metr_update)
self.devices_metrics[self.metric_names[metric_index]].append(metr_epoch_loss)
def create_accuracy_metric(self, metric_index, labels, predictions):
metr_epoch_loss, metr_update, metr_reset = tfu.create_reset_metric(
tf.metrics.accuracy,
self.metric_names[metric_index],
labels=labels,
predictions=predictions
)
self.metrics_reset.append(metr_reset)
self.metrics_update.append(metr_update)
self.devices_metrics[self.metric_names[metric_index]].append(metr_epoch_loss)
def __create_feed_dict__(self, op_name, item):
feed_dic = {}
for dev_num, batch in enumerate(item):
feed_dic[self.xs[dev_num]] = batch['x']
feed_dic[self.x_seq_lens[dev_num]] = batch['x_seq_len']
self.__update_feed_dict__(op_name, feed_dic, batch, dev_num)
return feed_dic
@abstractmethod
def __update_feed_dict__(self, op_name, feed_dict, batch, dev_num):
pass
@abstractmethod
def __build_graph_for_device__(self, *args):
pass
@abstractmethod
def __load_dataset__(self, operation_name):
return []
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