fixed converting boolean, keras iterator, pytorch gpu training

Author: marcj

Makefile

@@ -3,6 +3,6 @@ zip:
 	zip deepkit.zip deepkit/*.py deepkit/utils/*.py README.md setup.cfg setup.py
 
 publish:
-	rm -r dist/*
+	rm -rf dist/*
 	python3 setup.py sdist bdist_wheel
 	python3 -m twine upload --repository-url https://upload.pypi.org/legacy/ dist/*

deepkit/experiment.py

@@ -424,7 +424,11 @@ def floatconfig(self, path, default=None):
 
     def boolconfig(self, path, default=None):
         v = self.get_config(path, default)
-        return bool(v) if v is not None else default
+        if v is None:
+            return default
+        if not v or v is 'false' or v is 0 or v is '0':
+            return False
+        return True
 
     def config(self, path, default=None):
         v = self.get_config(path, default)
@@ -449,7 +453,7 @@ def watch_keras_model(self, model, model_input=None, name=None, is_batch=True):
 
             def fit_generator(generator, *args, **kwargs):
                 if debugger.model_input is None:
-                    debugger.set_input(generator)
+                    debugger.set_input(next(iter(generator)))
                 return ori_fit_generator(generator, *args, **kwargs)
 
             model.fit_generator = fit_generator

deepkit/keras_tf.py

@@ -6,6 +6,7 @@
 
 import PIL.Image
 import numpy as np
+
 if 'keras' in sys.modules:
     import keras
 else:

deepkit/pytorch.py

@@ -419,7 +419,7 @@ def hook(module, input, output):
         module.register_forward_hook(hook)
 
     def get_histogram(self, x, tensor):
-        h = np.histogram(tensor.detach().numpy(), bins=20)
+        h = np.histogram(tensor.cpu().detach().numpy(), bins=20)
         # <version><x><bins><...x><...y>, little endian
         # uint8|Uint32|Uint16|...Float32|...Uint32
         # B|L|H|...f|...L
@@ -436,7 +436,7 @@ def get_debug_data(self, x, module, output):
 
             if len(output.shape) > 1:
                 # outputs come in batch usually, so pick first
-                sample = output[0].detach().numpy()
+                sample = output[0].cpu().detach().numpy()
                 if len(sample.shape) == 3:
                     if sample.shape[0] == 3:
                         image = PIL.Image.fromarray(get_layer_vis_square(sample))

examples/keras-cifar10/model.py

@@ -157,6 +157,7 @@
     model.fit_generator(datagen.flow(x_train, y_train,
                                      batch_size=batch_size),
                         epochs=epochs,
+                        steps_per_epoch=len(x_train)/batch_size,
                         verbose=0,
                         callbacks=callbacks,
                         validation_data=(x_test, y_test),

setup.py

@@ -1,7 +1,7 @@
 from setuptools import setup
 from setuptools import find_packages
 
-__version__ = '1.0.0'
+__version__ = '1.0.1'
 
 setup(name='deepkit',
       version=__version__,