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
def _adapt_parameter_data(self):
sys.stdout.write('start parameter loading...\n')
start_time = time.time()
caffe_impl = caffeimpl.CaffeResolver()
if caffe_impl.runtime():
paras = caffe_impl.caffe.Net(self._source_solver.model_path,
self._source_solver.weights_path,
caffe_impl.caffe.TEST).params
caffe_blobs = [(layer_name, map(lambda blobs: blobs.data,
blob_vec))
for layer_name, blob_vec in paras.items()]
else:
sys.stdout.write(
'loading weights via protopb, may take longer time than runtime'
)
params = caffe_impl.net()
params.MergeFromString(
open(self._source_solver.weights_path, 'rb').read())
params_layers = params.layer or params.layers
caffe_blobs = [(layer.name, [blob.data for blob in layer.blobs])
for layer in params_layers if layer.blobs]
sys.stdout.write('finished loading, total time: %d\n' %
(time.time() - start_time))
# mapping the script into layers
sys.stdout.write('start parameter matching...\n')
for caffe_blob in caffe_blobs:
try:
cntk_layer = self._uni_model.cntk_layers[caffe_blob[0]]
except KeyError:
caffe_layers = self._raw_net.layer or self._raw_net.layers
try:
special_layer = [
layer for layer in caffe_layers
if layer.name == caffe_blob[0]
][0]
if special_layer.type == 'Scale':
previous_layer = [
caffe_layers[i - 1]
for i in range(len(caffe_layers))
if caffe_layers[i] == special_layer
][0]
if previous_layer.type != 'BatchNorm':
raise AssertionError(
'un-support pure Scale layer without BN in %s'
% caffe_layers.name)
cntk_layer = self._uni_model.cntk_layers[
previous_layer.name]
else:
raise AssertionError(
'un-match layer name %s while matching parameters\n'
% caffe_blob.name)
except IndexError:
sys.stderr.write(
'ignore weights for %s, since not contained in graph\n'
% caffe_blob[0])
for blob in caffe_blob[1]:
cntk_parameter_tensor = cntkmodel.CntkTensorDefinition()
cntk_parameter_tensor.data = blob
cntk_layer.parameter_tensor.append(cntk_parameter_tensor)
sys.stdout.write('finished matching.\n')