Dev segmentation (#36)

* added loaders

* fix pytorch callbacks

* prepare for v0.1.9, dropped not used tests

* Update requirements.txt

requirements.txt

@@ -1,7 +1,3 @@
-steppy==0.1.4
-neptune-cli==2.8.5
-attrdict==2.0.0
-numpy==1.14.3
-pandas==0.23.0
-pytest==3.6.0
-setuptools==39.2.0
+neptune-cli>=2.8.0
+setuptools>=39.2.0
+steppy>=0.1.9

setup.py

@@ -15,22 +15,18 @@
 
 setup(name='steppy-toolkit',
       packages=find_packages(),
-      version='0.1.8',
+      version='0.1.9',
       description='Set of tools to make your work with steppy faster and more effective.',
       long_description=long_description,
       url='https://github.com/minerva-ml/steppy-toolkit',
-      download_url='https://github.com/minerva-ml/steppy-toolkit/archive/0.1.8.tar.gz',
+      download_url='https://github.com/minerva-ml/steppy-toolkit/archive/0.1.9.tar.gz',
       author='Kamil A. Kaczmarek, Jakub Czakon',
       author_email='kamil.kaczmarek@neptune.ml, jakub.czakon@neptune.ml',
       keywords=['machine-learning', 'reproducibility', 'pipeline', 'tools'],
       license='MIT',
       install_requires=[
-          'steppy>=0.1.4',
-          'neptune-cli>=2.8.5',
-          'attrdict>=2.0.0',
-          'numpy>=1.14.0',
-          'pandas>=0.23.0',
-          'pytest>=3.6.0',
-          'setuptools>=39.2.0'],
+          'neptune-cli>=2.8.0',
+          'setuptools>=39.2.0',
+          'steppy>=0.1.9'],
       zip_safe=False,
       classifiers=[])

toolkit/pytorch_transformers/callbacks.py

@@ -44,21 +44,21 @@ def on_epoch_begin(self, *args, **kwargs):
     def on_epoch_end(self, *args, **kwargs):
         self.epoch_id += 1
 
-    def training_break(self, *args, **kwargs):
-        return False
-
     def on_batch_begin(self, *args, **kwargs):
         pass
 
     def on_batch_end(self, *args, **kwargs):
         self.batch_id += 1
 
+    def training_break(self, *args, **kwargs):
+        return False
+
     def get_validation_loss(self):
-        if self.validation_loss is None:
-            self.validation_loss = {}
-        return self.validation_loss.setdefault(self.epoch_id, score_model(self.model,
-                                                                          self.loss_function,
-                                                                          self.validation_datagen))
+        if self.epoch_id not in self.validation_loss.keys():
+            self.validation_loss[self.epoch_id] = score_model(self.model,
+                                                              self.loss_function,
+                                                              self.validation_datagen)
+        return self.validation_loss[self.epoch_id]
 
 
 class CallbackList:
@@ -93,10 +93,6 @@ def on_epoch_end(self, *args, **kwargs):
         for callback in self.callbacks:
             callback.on_epoch_end(*args, **kwargs)
 
-    def training_break(self, *args, **kwargs):
-        callback_out = [callback.training_break(*args, **kwargs) for callback in self.callbacks]
-        return any(callback_out)
-
     def on_batch_begin(self, *args, **kwargs):
         for callback in self.callbacks:
             callback.on_batch_begin(*args, **kwargs)
@@ -105,6 +101,10 @@ def on_batch_end(self, *args, **kwargs):
         for callback in self.callbacks:
             callback.on_batch_end(*args, **kwargs)
 
+    def training_break(self, *args, **kwargs):
+        callback_out = [callback.training_break(*args, **kwargs) for callback in self.callbacks]
+        return any(callback_out)
+
 
 class TrainingMonitor(Callback):
     def __init__(self, epoch_every=None, batch_every=None):
@@ -176,8 +176,9 @@ def __init__(self, patience, minimize=True):
         self.minimize = minimize
         self.best_score = None
         self.epoch_since_best = 0
+        self._training_break = False
 
-    def training_break(self, *args, **kwargs):
+    def on_epoch_end(self, *args, **kwargs):
         self.model.eval()
         val_loss = self.get_validation_loss()
         loss_sum = val_loss['sum']
@@ -195,9 +196,12 @@ def training_break(self, *args, **kwargs):
             self.epoch_since_best += 1
 
         if self.epoch_since_best > self.patience:
-            return True
-        else:
-            return False
+            self._training_break = True
+
+        self.epoch_id += 1
+
+    def training_break(self, *args, **kwargs):
+        return self._training_break
 
 
 class ExponentialLRScheduler(Callback):