def add_kinetic_auxiliary_protocol(self, aux_index, duration, label,
plate_index, protocol_index, order):
new_protocol = Auxiliary(aux_index, duration, label, plate_index,
order)
kinetic_protocol = self.plateList[plate_index].get_protocol(
protocol_index)
kinetic_protocol.add_protocol(new_protocol)
def parse(self, fname):
aux = Auxiliary.Auxiliary()
with open(fname, 'r') as f:
nums = f.read()
nums = aux.fullReplace(nums, ";;", ";")
nums = aux.fullReplace(nums, "о/р ", "о/р")
print(nums)
def add_auxiliary_protocol(self, aux_index, duration, label, plate_index,
order):
"""Function to add an Auxiliary protocol to the list of protocols to execute for a particular PlateConfiguration.
The auxiliary protocol type is defined by the aux_index input.
Arguments:
aux_index {integer} -- possible values 1, 2, 3 or 4 representing which auxiliary port will be triggerd on.
duration {integer} -- duration for how long to trigger the selected auxiliary port for.
label {string} -- protocol label, should be set to 'Auxiliary'
plate_index {integer} -- index of the PlateConfiguration object in self.plateList in which to select wells
order {integer} -- the index of the protocol in the existing list of protocols of the plate configuration object.
If this is the first protocol that is being added to the PlateConfiguration object then the order is equal to zero. If it is the second, then the order is equal to 1 and so on.
"""
new_protocol = Auxiliary(aux_index, duration, label, plate_index,
order)
self.plateList[plate_index].add_protocol(new_protocol)
def DSOD():
image_size, num_classes = CONFIG['image_size'], CONFIG['num_classes']
input = layer.Input(image_size)
x, output = BackBone(input)
x1, x2, x3, x4, x5, x6 = Auxiliary(x, output)
x1_conf, x1_loc, x1_priorbox = predict_block(x1, 'x1', 3, 30.0, [2])
x2_conf, x2_loc, x2_priorbox = predict_block(x2, 'x2', 6, 60.0, [2, 3],
114.0)
x3_conf, x3_loc, x3_priorbox = predict_block(x3, 'x3', 6, 114.0, [2, 3],
168.0)
x4_conf, x4_loc, x4_priorbox = predict_block(x4, 'x4', 6, 168.0, [2, 3],
222.0)
x5_conf, x5_loc, x5_priorbox = predict_block(x5, 'x5', 6, 222.0, [2, 3],
276.0)
x6_conf, x6_loc, x6_priorbox = predict_block(x6, 'x6', 6, 276.0, [2, 3],
330.0)
conf = layer.Concatenate(axis=1, name='conf')(
[x1_conf, x2_conf, x3_conf, x4_conf, x5_conf, x6_conf])
loc = layer.Concatenate(
axis=1, name='loc')([x1_loc, x2_loc, x3_loc, x4_loc, x5_loc, x6_loc])
priorbox = layer.Concatenate(axis=1, name='priorbox')([
x1_priorbox, x2_priorbox, x3_priorbox, x4_priorbox, x5_priorbox,
x6_priorbox
])
num_boxes = K.int_shape(loc)[-1] // 4
conf = layer.Reshape((num_boxes, num_classes), name='conf_reshape')(conf)
conf = layer.Activation(activation='softmax', name='conf_softmax')(conf)
loc = layer.Reshape((num_boxes, 4), name='loc_reshape')(loc)
prediction = layer.Concatenate(axis=2,
name='prediction')([loc, conf, priorbox])
model = keras.Model(input=input, output=prediction)
return model
