def _setup_cntk_layer_def(self, cntk_layer_type, raw_layer, inputs_info):
cntk_layer_def = cntkmodel.CntkLayersDefinition()
cntk_layer_def.op_name = raw_layer.name
cntk_layer_def.op_type = cntk_layer_type
for input_info in inputs_info:
cntk_layer_def.inputs.append(input_info.op_name)
cntk_layer_def.outputs.append(raw_layer.name)
getattr(SetupCaffeParameters,
cntk_layer_type.name)(self._get_layer_parameters(raw_layer),
inputs_info, cntk_layer_def)
return cntk_layer_def
def _adapt_data_provider(self):
if not self._raw_net:
raise KeyError('Invalid net structure\n')
raw_layers = self._raw_net.layer or self._raw_net.layers
for raw_layer in raw_layers:
if raw_layer.type != 'Input':
continue
if not self._inclusive_layer(raw_layer):
continue
for i in range(0, len(raw_layer.top)):
data_provider = cntkmodel.CntkLayersDefinition()
data_provider.op_name = raw_layer.top[i]
data_provider.tensor = raw_layer.input_param.shape[i].dim[
1:] # cancel mini-batch
self._uni_model.data_provider.append(data_provider)
def _install_test_layer(op_type, parameters, weights, input_data):
para_cls_id = 'Cntk' + op_type + 'Parameters'
para_instance = eval('.'.join(('cntkmodel', para_cls_id)))()
for key, value in parameters.items():
setattr(para_instance, key, value)
layer_def = cntkmodel.CntkLayersDefinition()
layer_def.parameters = para_instance
layer_def.op_type = getattr(cntkmodel.CntkLayerType, utils.format.camel_to_snake(op_type))
layer_def.op_name = '_'.join(('test', op_type))
layer_def.parameter_tensor = []
if weights is not None:
for weight in weights:
weight_tensor = cntkmodel.CntkTensorDefinition()
weight_tensor.tensor = np.array(weight).shape
weight_tensor.data = weight
layer_def.parameter_tensor.append(weight_tensor)
inputs_variable = []
for input_tensor in input_data:
inputs_variable.append(cntk.input(input_tensor.shape))
return layer_def, inputs_variable
