def generate(self):
slope_node_info = self.tflite_interpreter._get_tensor_details(
self.op.Inputs(1))
slope_array = self.tflite_interpreter.get_tensor(
slope_node_info['index'])
slope_array = np.transpose(slope_array, (2, 0, 1))
# make slope onnx node
slope_onnx_node_name = self.node_name + "_slope"
slope_onnx_node = onnx.helper.make_tensor(
slope_onnx_node_name, TensorProto.FLOAT, slope_array.shape,
slope_array.flatten().tolist())
self.weight_node_list.append(slope_onnx_node)
previous_onnx_node_names = self.input_nodes_name.copy()
previous_onnx_node_names.extend([slope_onnx_node_name])
prelu_node = onnx.helper.make_node('PRelu',
inputs=previous_onnx_node_names,
outputs=[self.node_name],
name=self.node_name)
self.node_list.append(prelu_node)
# original layer output
node_output_detail = self.tflite_interpreter._get_tensor_details(
self.op.Outputs(0))
out_shape_info = onnx.helper.make_tensor_value_info(
self.node_name, TensorProto.FLOAT,
utils.tflite2onnx_shape_map(node_output_detail['shape'].tolist()))
self.value_infos.append(out_shape_info)
return self.node_list, self.value_infos, self.weight_node_list
def generate(self):
relu_name = self.node_name
relu_node = helper.make_node("Relu",
name=relu_name,
inputs=self.input_nodes_name,
outputs=[relu_name])
node_output_detail = self.tflite_interpreter._get_tensor_details(
self.op.Outputs(0))
out_shape_info = onnx.helper.make_tensor_value_info(
relu_name, TensorProto.FLOAT,
utils.tflite2onnx_shape_map(node_output_detail['shape'].tolist()))
self.value_infos.append(out_shape_info)
self.node_list.append(relu_node)
return self.node_list, self.value_infos, self.weight_node_list
def generate(self):
clip_name = self.node_name
clip_node = helper.make_node('Clip',
inputs=self.input_nodes_name,
outputs=[clip_name],
min=0.0,
max=6.0,
name=clip_name)
node_output_detail = self.tflite_interpreter._get_tensor_details(
self.op.Outputs(0))
out_shape_info = helper.make_tensor_value_info(
clip_name, TensorProto.FLOAT,
utils.tflite2onnx_shape_map(node_output_detail['shape'].tolist()))
self.value_infos.append(out_shape_info)
self.node_list.append(clip_node)
return self.node_list, self.value_infos, self.weight_node_list
def generate(self):
node_output_detail = self.tflite_interpreter._get_tensor_details(
self.op.Outputs(0))
node_input_detail = self.tflite_interpreter._get_tensor_details(
self.op.Inputs(0))
kernel_shape = [
self.tflite_avgpool_parser.FilterHeight(),
self.tflite_avgpool_parser.FilterWidth()
]
strides_len = [
self.tflite_avgpool_parser.StrideH(),
self.tflite_avgpool_parser.StrideW()
]
padding_stradegy = 'NONE'
if self.tflite_avgpool_parser.Padding() is Padding.SAME:
padding_stradegy = 'SAME'
elif self.tflite_avgpool_parser.Padding() is Padding.VALID:
padding_stradegy = 'VALID'
input_feature_map_shape = node_input_detail['shape']
avg_pool_name = self.node_name
avg_pool_node = helper.make_node(
'AveragePool',
inputs=self.input_nodes_name,
outputs=[avg_pool_name],
kernel_shape=kernel_shape,
strides=strides_len,
pads=utils.getPadding(input_feature_map_shape, kernel_shape,
strides_len, None, padding_stradegy),
name=avg_pool_name)
out_shape_info = helper.make_tensor_value_info(
avg_pool_name, TensorProto.FLOAT,
utils.tflite2onnx_shape_map(node_output_detail['shape'].tolist()))
# update tables
self.value_infos.append(out_shape_info)
self.node_list.append(avg_pool_node)
return self.node_list, self.value_infos, self.weight_node_list
def generate(self):
node_output_detail = self.tflite_interpreter._get_tensor_details(self.op.Inputs(0))
node_input_detail = self.tflite_interpreter._get_tensor_details(self.op.Inputs(2))
output_shape_value = self.tflite_interpreter.get_tensor(node_output_detail['index'])
weights_node_info = self.tflite_interpreter._get_tensor_details(self.op.Inputs(1))
weights_array = self.tflite_interpreter.get_tensor(weights_node_info['index'])
kernel_shape=[weights_array.shape[1], weights_array.shape[2]]
strides_len = [self.tflite_tconv_parser.StrideH(),self.tflite_tconv_parser.StrideW()]
padding_stradegy = 'NONE'
if self.tflite_tconv_parser.Padding() is Padding.SAME:
padding_stradegy = 'SAME'
elif self.tflite_tconv_parser.Padding() is Padding.VALID:
padding_stradegy = 'VALID'
input_feature_map_shape = node_input_detail['shape']
# transpose because shape define diffent between tflite and onnx
weights_array = np.transpose(weights_array, (3, 0, 1, 2))
# make weight onnx node
weight_onnx_node_name = self.node_name + "_weight"
weight_onnx_node = onnx.helper.make_tensor(
weight_onnx_node_name,
TensorProto.FLOAT,
weights_array.shape,
weights_array.flatten().tolist()
)
# make conv onnx node
previous_onnx_node_names = self.input_nodes_name.copy()
previous_onnx_node_names.extend([weight_onnx_node_name])
tconv_onnx_node = onnx.helper.make_node(
'ConvTranspose',
inputs= previous_onnx_node_names,
outputs=[self.node_name],
kernel_shape=kernel_shape,
strides=strides_len,
# TODO: calculate padding for tanspose conv
#pads = utils.getPadding(input_feature_map_shape, kernel_shape, strides_len, padding_stradegy),
name=self.node_name,
group=1
)
# original layer output
out_shape_info = onnx.helper.make_tensor_value_info(
self.node_name,
TensorProto.FLOAT,
utils.tflite2onnx_shape_map(output_shape_value.tolist())
)
self.value_infos.append(out_shape_info)
# add weight, bias node
self.weight_node_list.append(weight_onnx_node)
self.node_list.append(tconv_onnx_node)
return self.node_list, self.value_infos, self.weight_node_list
def generate(self):
if utils.ONNX_VERSION_1_4_1 == onnx.__version__:
warnings.warn(self.__class__.__name__ + ' is implemented by `Upsample` op, and not support `align_corners`,'
'`half_pixel_centers` attributes.',
UserWarning)
node_output_detail = self.tflite_interpreter._get_tensor_details(self.op.Outputs(0))
node_input_detail = self.tflite_interpreter._get_tensor_details(self.op.Inputs(0))
# create constant node
tensor_input_detail = self.tflite_interpreter._get_tensor_details(self.op.Inputs(1))
source_width, source_height = node_input_detail['shape'].tolist()[1:3]
target_width, targwt_height = self.tflite_interpreter.get_tensor(tensor_input_detail['index']).tolist()
source_size = np.array([1.0, 1.0, source_height, source_width], dtype=np.int32)
target_siz = np.array([1.0, 1.0, targwt_height, target_width], dtype=np.int32)
constant_val = target_siz/source_size
constant_node_name = self.node_name + '_scales'
constant_tensor = onnx.helper.make_tensor(
name=tensor_input_detail['name'],
data_type=TensorProto.FLOAT,
dims=constant_val.shape,
vals=constant_val.ravel())
constant_node = onnx.helper.make_node(
op_type="Constant",
inputs=[],
outputs=[constant_node_name],
name=constant_node_name,
value=constant_tensor)
constant_info = onnx.helper.make_tensor_value_info(
name=constant_node_name,
elem_type=TensorProto.FLOAT,
shape=constant_val.shape)
# self.weight_node_list.append(constant_tensor)
self.node_list.append(constant_node)
self.value_infos.append(constant_info)
previous_onnx_node_names = self.input_nodes_name.copy()
previous_onnx_node_names.extend([constant_node_name])
resize_nearest_neighbor_node = onnx.helper.make_node(
op_type='Upsample',
inputs=previous_onnx_node_names,
outputs=[self.node_name],
name=self.node_name,
mode='linear'
)
resize_nearest_neighbor_info = onnx.helper.make_tensor_value_info(
name=self.node_name,
elem_type=TensorProto.FLOAT,
shape=utils.tflite2onnx_shape_map(node_output_detail['shape'].tolist())
)
# update tables
self.node_list.append(resize_nearest_neighbor_node)
self.value_infos.append(resize_nearest_neighbor_info)
else:
NotImplementedError('Partially Support ONNX ' + utils.ONNX_VERSION_1_4_1)
return self.node_list, self.value_infos, self.weight_node_list
def generate(self):
node_output_detail = self.tflite_interpreter._get_tensor_details(self.op.Outputs(0))
node_input_detail = self.tflite_interpreter._get_tensor_details(self.op.Inputs(0))
weights_node_info = self.tflite_interpreter._get_tensor_details(self.op.Inputs(1))
bias_node_info = self.tflite_interpreter._get_tensor_details(self.op.Inputs(2))
weights_array = self.tflite_interpreter.get_tensor(weights_node_info['index'])
bias_array = self.tflite_interpreter.get_tensor(bias_node_info['index'])
kernel_shape=[weights_array.shape[1], weights_array.shape[2]]
strides_len = [self.tflite_conv_parser.StrideH(),self.tflite_conv_parser.StrideW()]
dilation_factor = [self.tflite_conv_parser.DilationHFactor(), self.tflite_conv_parser.DilationWFactor()]
padding_stradegy = 'NONE'
if self.tflite_conv_parser.Padding() is Padding.SAME:
padding_stradegy = 'SAME'
elif self.tflite_conv_parser.Padding() is Padding.VALID:
padding_stradegy = 'VALID'
input_feature_map_shape = node_input_detail['shape']
# transpose because shape define diffent between tflite and onnx
weights_array = np.transpose(weights_array, (0, 3, 1, 2))
# make weight onnx node
weight_onnx_node_name = self.node_name + "_weight"
weight_onnx_node = onnx.helper.make_tensor(
weight_onnx_node_name,
TensorProto.FLOAT,
weights_array.shape,
weights_array.flatten().tolist()
)
# make bias onnx node
bias_onnx_node_name = self.node_name + "_bias"
bias_onnx_node = onnx.helper.make_tensor(
bias_onnx_node_name,
TensorProto.FLOAT,
bias_array.shape,
bias_array.flatten().tolist()
)
# make conv onnx node
previous_onnx_node_names = self.input_nodes_name.copy()
previous_onnx_node_names.extend([weight_onnx_node_name, bias_onnx_node_name])
conv_onnx_node = onnx.helper.make_node(
'Conv',
inputs= previous_onnx_node_names,
outputs=[self.node_name],
kernel_shape=kernel_shape,
strides=strides_len,
pads=utils.getPadding(input_feature_map_shape, kernel_shape, strides_len, dilation_factor, padding_stradegy),
dilations=dilation_factor,
name=self.node_name,
group=1
)
# original layer output
out_shape_info = onnx.helper.make_tensor_value_info(
self.node_name,
TensorProto.FLOAT,
utils.tflite2onnx_shape_map(node_output_detail['shape'].tolist())
)
self.value_infos.append(out_shape_info)
# add weight, bias node
self.weight_node_list.append(weight_onnx_node)
self.weight_node_list.append(bias_onnx_node)
self.node_list.append(conv_onnx_node)
# change output node's input_name
for o_n in self.output_nodes:
for idx, o_n_i_n in enumerate(o_n.input_nodes_name):
if o_n_i_n == self.node_name:
o_n.input_nodes_name[idx] = self.node_list[-1].name
return self.node_list, self.value_infos, self.weight_node_list