def __init__(self, parent=None):
super(LayerDataProvider, self).__init__(parent)
self._blobs = []
self._availableModes = ["Blobs", "Params"]
self._mode = self._availableModes[0]
self._model = None
self._network = None
self._layer = ""
self._image = ""
self._backend = CaffeBackend()
self._normalize = False
# self._backend.setNetwork("/home/hannes/ownCloud/Studium/Deep_Learning/proto/5_chars_memory_data.caffeproto")
self._backend.setNetwork("/home/hannes/ownCloud/Studium/Deep_Learning/proto/5_chars.caffeproto")
self._backend.setModel("/home/hannes/ownCloud/Studium/Deep_Learning/models/5_chars.caffemodel")
self._layer = self._backend.layerNames(self._mode)[0]
self.updateBlobs()
self.layerChanged.connect(self.updateBlobs)
self.imageChanged.connect(self.updateBlobs)
self.modeChanged.connect(self.updateBlobs)
self.normalizeChanged.connect(self.updateBlobs)
class LayerDataProvider(QObject):
# Signals and Slots
#
availableModesChanged = pyqtSignal()
modeChanged = pyqtSignal()
layerChanged = pyqtSignal()
imageChanged = pyqtSignal()
availableLayersChanged = pyqtSignal()
blobsChanged = pyqtSignal()
normalizeChanged = pyqtSignal()
@pyqtSlot()
def updateBlobs(self):
# TODO exception handling
if self._mode == "Blobs":
try:
self.getPredictions()
except:
pass
elif self._mode == "Params":
try:
self.getParams()
except:
pass
else:
return
self.blobsChanged.emit()
def getPredictions(self):
imagepath = QUrl(self._image).toString(QUrl.RemoveScheme)
if not os.path.isfile(imagepath):
print("error: invalid image: {}".format(imagepath))
return
# print("updating...")
self._blobs[:] = []
for blob in self._backend.predictions(imagepath, self._layer, self.normalize):
self._blobs.append(BlobData(blobdata=QByteArray(blob)))
def getParams(self):
self._blobs[:] = []
for blob in self._backend.params(self._layer, self.normalize):
self._blobs.append(BlobData(blobdata=QByteArray(blob)))
# Properties
#
@pyqtProperty("QVariant", notify=availableModesChanged)
def modes(self):
return QVariant(self._availableModes)
@pyqtProperty("QString", notify=modeChanged)
def mode(self):
return self._mode
@mode.write
def mode(self, mode):
self._mode = mode
self.availableLayersChanged.emit()
self.modeChanged.emit()
@pyqtProperty("QString", notify=layerChanged)
def layer(self):
return self._layer
@layer.write
def layer(self, name):
self._layer = name
self.layerChanged.emit()
@pyqtProperty("QString")
def network(self):
return self._network
@network.write
def network(self, net):
self._network = net
self._backend.setNetwork(net)
@pyqtProperty("QString")
def model(self):
return self._model
@model.write
def model(self, model):
self._model = model
self._backend.setModel(model)
@pyqtProperty("QUrl", notify=imageChanged)
def image(self):
return self._model
@image.write
def image(self, img):
self._image = img
self.imageChanged.emit()
@pyqtProperty(QVariant, notify=availableLayersChanged)
def availableLayers(self):
return QVariant(self._backend.layerNames(self._mode))
@pyqtProperty(QVariant, notify=blobsChanged)
def blobs(self):
return QVariant(self._blobs)
@pyqtProperty(bool, notify=normalizeChanged)
def normalize(self):
return self._normalize
@normalize.write
def normalize(self, n):
self._normalize = n
self.normalizeChanged.emit()
def __init__(self, parent=None):
super(LayerDataProvider, self).__init__(parent)
self._blobs = []
self._availableModes = ["Blobs", "Params"]
self._mode = self._availableModes[0]
self._model = None
self._network = None
self._layer = ""
self._image = ""
self._backend = CaffeBackend()
self._normalize = False
# self._backend.setNetwork("/home/hannes/ownCloud/Studium/Deep_Learning/proto/5_chars_memory_data.caffeproto")
self._backend.setNetwork("/home/hannes/ownCloud/Studium/Deep_Learning/proto/5_chars.caffeproto")
self._backend.setModel("/home/hannes/ownCloud/Studium/Deep_Learning/models/5_chars.caffemodel")
self._layer = self._backend.layerNames(self._mode)[0]
self.updateBlobs()
self.layerChanged.connect(self.updateBlobs)
self.imageChanged.connect(self.updateBlobs)
self.modeChanged.connect(self.updateBlobs)
self.normalizeChanged.connect(self.updateBlobs)
