diff --git a/run.py b/run.py
index 71244fb9d15f594ac3ffbce60341d5c8dcb15f03..c06bf6480aa17918a1e8131defd3b3a5233ffb44 100644
--- a/run.py
+++ b/run.py
@@ -24,14 +24,14 @@ def main(parser_args):
Training()
- PostProcessing()
+ # PostProcessing()
if __name__ == "__main__":
formatter = '%(asctime)s - %(levelname)s: %(message)s [%(filename)s:%(funcName)s:%(lineno)s]'
- # logging.basicConfig(format=formatter, level=logging.INFO)
- logging.basicConfig(format=formatter, level=logging.DEBUG)
+ logging.basicConfig(format=formatter, level=logging.INFO)
+ # logging.basicConfig(format=formatter, level=logging.DEBUG)
parser = argparse.ArgumentParser()
parser.add_argument('--experiment_date', metavar='--exp_date', type=str, default=None,
diff --git a/src/helpers.py b/src/helpers.py
index 172a8dd3cf04a15e9069347dac7f06c6d2d8ed60..f119f140a159d4e050c77fde563651484a57079d 100644
--- a/src/helpers.py
+++ b/src/helpers.py
@@ -57,6 +57,8 @@ class LearningRateDecay(keras.callbacks.History):
self.base_lr = self.check_param(base_lr, 'base_lr')
self.drop = self.check_param(drop, 'drop')
self.epochs_drop = self.check_param(epochs_drop, 'epochs_drop', upper=None)
+ self.epoch = []
+ self.history = {}
@staticmethod
def check_param(value: float, name: str, lower: Union[float, None] = 0, upper: Union[float, None] = 1):
@@ -80,6 +82,9 @@ class LearningRateDecay(keras.callbacks.History):
raise ValueError(f"{name} is out of allowed range ({lower}, {upper}{')' if upper == np.inf else ']'}: "
f"{name}={value}")
+ def on_train_begin(self, logs=None):
+ pass
+
def on_epoch_begin(self, epoch: int, logs=None):
"""
Lower learning rate every epochs_drop epochs by factor drop.
diff --git a/src/model_modules/model_class.py b/src/model_modules/model_class.py
index 6b0fe236ff8ee726c34a721a6be0ed8be91f2bb8..5e9931d70d33a16cd4478427db7402a2b38dc9c0 100644
--- a/src/model_modules/model_class.py
+++ b/src/model_modules/model_class.py
@@ -110,7 +110,7 @@ class MyLittleModel(AbstractModelClass):
self.initial_lr = 1e-2
self.optimizer = keras.optimizers.SGD(lr=self.initial_lr, momentum=0.9)
self.lr_decay = helpers.LearningRateDecay(base_lr=self.initial_lr, drop=.94, epochs_drop=10)
- self.epochs = 2
+ self.epochs = 20
self.batch_size = int(256)
self.activation = keras.layers.PReLU
@@ -190,7 +190,7 @@ class MyBranchedModel(AbstractModelClass):
self.initial_lr = 1e-2
self.optimizer = keras.optimizers.SGD(lr=self.initial_lr, momentum=0.9)
self.lr_decay = helpers.LearningRateDecay(base_lr=self.initial_lr, drop=.94, epochs_drop=10)
- self.epochs = 2
+ self.epochs = 20
self.batch_size = int(256)
self.activation = keras.layers.PReLU
diff --git a/src/run_modules/model_setup.py b/src/run_modules/model_setup.py
index a7722018c52275b390a10199cb30b7b936ed37a3..a4d89f65d679f10b3bd05191b708761925e2cd4d 100644
--- a/src/run_modules/model_setup.py
+++ b/src/run_modules/model_setup.py
@@ -4,19 +4,20 @@ __date__ = '2019-12-02'
import keras
from keras import losses
-from keras.callbacks import ModelCheckpoint
+from keras.callbacks import ModelCheckpoint, History
from keras.regularizers import l2
from keras.optimizers import SGD
import tensorflow as tf
import logging
import os
+import pickle
from src.run_modules.run_environment import RunEnvironment
from src.helpers import l_p_loss, LearningRateDecay
from src.model_modules.inception_model import InceptionModelBase
from src.model_modules.flatten import flatten_tail
-from src.model_modules.model_class import MyBranchedModel as MyModel
-# from src.model_modules.model_class import MyLittleModel as MyModel
+# from src.model_modules.model_class import MyBranchedModel as MyModel
+from src.model_modules.model_class import MyLittleModel as MyModel
class ModelSetup(RunEnvironment):
@@ -30,13 +31,11 @@ class ModelSetup(RunEnvironment):
exp_name = self.data_store.get("experiment_name", "general")
self.scope = "general.model"
self.checkpoint_name = os.path.join(path, f"{exp_name}_model-best.h5")
+ self.callbacks_name = os.path.join(path, f"{exp_name}_model-best-callbacks-%s.pickle")
self._run()
def _run(self):
- # create checkpoint
- self._set_checkpoint()
-
# set channels depending on inputs
self._set_channels()
@@ -50,6 +49,9 @@ class ModelSetup(RunEnvironment):
if self.data_store.get("trainable", self.scope) is False:
self.load_weights()
+ # create checkpoint
+ self._set_checkpoint()
+
# compile model
self.compile_model()
@@ -64,7 +66,17 @@ class ModelSetup(RunEnvironment):
self.data_store.set("model", self.model, self.scope)
def _set_checkpoint(self):
- checkpoint = ModelCheckpoint(self.checkpoint_name, verbose=1, monitor='val_loss', save_best_only=True, mode='auto')
+ lr = self.data_store.get("lr_decay", scope="general.model")
+ # checkpoint = ModelCheckpoint(filepath=self.checkpoint_name, verbose=1, monitor='val_loss', save_best_only=True, mode='auto')
+ # checkpoint = ModelCheckpointAdvanced(filepath=self.checkpoint_name, verbose=1, monitor='val_loss',
+ # save_best_only=True, mode='auto', callbacks_to_save=lr,
+ # callbacks_filepath=self.callbacks_name)
+ hist = HistoryAdvanced()
+ self.data_store.set("hist", hist, scope="general.model")
+ callbacks = [{"callback": lr, "path": self.callbacks_name % "lr"},
+ {"callback": hist, "path": self.callbacks_name % "hist"}]
+ checkpoint = ModelCheckpointAdvanced2(filepath=self.checkpoint_name, verbose=1, monitor='val_loss',
+ save_best_only=True, mode='auto', callbacks=callbacks)
self.data_store.set("checkpoint", checkpoint, self.scope)
def load_weights(self):
@@ -92,6 +104,61 @@ class ModelSetup(RunEnvironment):
keras.utils.plot_model(self.model, to_file=file_name, show_shapes=True, show_layer_names=True)
+class HistoryAdvanced(History):
+
+ def __init__(self, old_epoch=None, old_history=None):
+ self.epoch = old_epoch or []
+ self.history = old_history or {}
+ super().__init__()
+
+ def on_train_begin(self, logs=None):
+ pass
+
+
+class ModelCheckpointAdvanced(ModelCheckpoint):
+
+ def __init__(self, *args, **kwargs):
+ self.callbacks_to_save = kwargs.pop("callbacks_to_save")
+ self.callbacks_filepath = kwargs.pop("callbacks_filepath")
+ super().__init__(*args, **kwargs)
+
+ def on_epoch_end(self, epoch, logs=None):
+ super().on_epoch_end(epoch, logs)
+
+ file_path = self.callbacks_filepath
+ if self.epochs_since_last_save == 0 and epoch != 0:
+ if self.save_best_only:
+ current = logs.get(self.monitor)
+ if current == self.best:
+ with open(file_path, "wb") as f:
+ pickle.dump(self.callbacks_to_save, f)
+ else:
+ with open(file_path, "wb") as f:
+ pickle.dump(self.callbacks_to_save, f)
+
+
+class ModelCheckpointAdvanced2(ModelCheckpoint):
+
+ def __init__(self, *args, **kwargs):
+ self.callbacks = kwargs.pop("callbacks")
+ super().__init__(*args, **kwargs)
+
+ def on_epoch_end(self, epoch, logs=None):
+ super().on_epoch_end(epoch, logs)
+
+ for callback in self.callbacks:
+ file_path = callback["path"]
+ if self.epochs_since_last_save == 0 and epoch != 0:
+ if self.save_best_only:
+ current = logs.get(self.monitor)
+ if current == self.best:
+ with open(file_path, "wb") as f:
+ pickle.dump(callback["callback"], f)
+ else:
+ with open(file_path, "wb") as f:
+ pickle.dump(callback["callback"], f)
+
+
def my_loss():
loss = l_p_loss(4)
keras_loss = losses.mean_squared_error
diff --git a/src/run_modules/training.py b/src/run_modules/training.py
index 96936ce124e05251af483e758401d833a44531f4..e9c7487b4be94c8dfad6c64a88bd9b0815656064 100644
--- a/src/run_modules/training.py
+++ b/src/run_modules/training.py
@@ -5,11 +5,13 @@ import logging
import os
import json
import keras
+import pickle
from src.run_modules.run_environment import RunEnvironment
from src.data_handling.data_distributor import Distributor
from src.plotting.training_monitoring import PlotModelHistory, PlotModelLearningRate
from src.helpers import LearningRateDecay
+from src.run_modules.model_setup import ModelCheckpointAdvanced2
class Training(RunEnvironment):
@@ -22,8 +24,10 @@ class Training(RunEnvironment):
self.test_set = None
self.batch_size = self.data_store.get("batch_size", "general.model")
self.epochs = self.data_store.get("epochs", "general.model")
- self.checkpoint = self.data_store.get("checkpoint", "general.model")
+ self.checkpoint: ModelCheckpointAdvanced2 = self.data_store.get("checkpoint", "general.model")
+ # self.callbacks = self.data_store.get("callbacks", "general.model")
self.lr_sc = self.data_store.get("lr_decay", "general.model")
+ self.hist = self.data_store.get("hist", "general.model")
self.experiment_name = self.data_store.get("experiment_name", "general")
self._run()
@@ -76,13 +80,36 @@ class Training(RunEnvironment):
model from training is saved for class variable model.
"""
logging.info(f"Train with {len(self.train_set)} mini batches.")
- history = self.model.fit_generator(generator=self.train_set.distribute_on_batches(),
- steps_per_epoch=len(self.train_set),
- epochs=self.epochs,
- verbose=2,
- validation_data=self.val_set.distribute_on_batches(),
- validation_steps=len(self.val_set),
- callbacks=[self.checkpoint, self.lr_sc])
+ if not os.path.exists(self.checkpoint.filepath):
+ history = self.model.fit_generator(generator=self.train_set.distribute_on_batches(),
+ steps_per_epoch=len(self.train_set),
+ epochs=self.epochs,
+ verbose=2,
+ validation_data=self.val_set.distribute_on_batches(),
+ validation_steps=len(self.val_set),
+ # callbacks=self.callbacks)
+ callbacks=[self.checkpoint, self.lr_sc, self.hist])
+ else:
+ lr_filepath = self.checkpoint.callbacks[0]["path"] # TODO: stopped here. why does training start 1 epoch too early or doesn't it?
+ hist_filepath = self.checkpoint.callbacks[1]["path"]
+ lr_callbacks = pickle.load(open(lr_filepath, "rb"))
+ hist_callbacks = pickle.load(open(hist_filepath, "rb"))
+ self.lr_sc = lr_callbacks
+ self.hist = hist_callbacks
+ self.model = keras.models.load_model(self.checkpoint.filepath)
+ initial_epoch = max(hist_callbacks.epoch) + 1
+ callbacks = [{"callback": self.lr_sc, "path": lr_filepath},
+ {"callback": self.hist, "path": hist_filepath}]
+ self.checkpoint.callbacks = callbacks
+ history = self.model.fit_generator(generator=self.train_set.distribute_on_batches(),
+ steps_per_epoch=len(self.train_set),
+ epochs=self.epochs,
+ verbose=2,
+ validation_data=self.val_set.distribute_on_batches(),
+ validation_steps=len(self.val_set),
+ callbacks=[self.checkpoint, self.lr_sc, self.hist],
+ initial_epoch=initial_epoch)
+ history = self.hist
self.save_callbacks(history)
self.load_best_model(self.checkpoint.filepath)
self.create_monitoring_plots(history, self.lr_sc)
@@ -123,6 +150,7 @@ class Training(RunEnvironment):
json.dump(history.history, f)
with open(os.path.join(path, "history_lr.json"), "w") as f:
json.dump(self.lr_sc.lr, f)
+ # json.dump(self.callbacks["learning_rate"].lr, f)
def create_monitoring_plots(self, history: keras.callbacks.History, lr_sc: LearningRateDecay) -> None:
"""