ethosu/vela/tflite_reader.py - ml/ethos-u/ethos-u-vela - Gitiles

 # Copyright (C) 2020 Arm Limited or its affiliates. All rights reserved.
 #
 # SPDX-License-Identifier: Apache-2.0
 #
 # Licensed under the Apache License, Version 2.0 (the License); you may
 # not use this file except in compliance with the License.
 # You may obtain a copy of the License at
 #
 # www.apache.org/licenses/LICENSE-2.0
 #
 # Unless required by applicable law or agreed to in writing, software
 # distributed under the License is distributed on an AS IS BASIS, WITHOUT
 # WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.


 # Description:
 # Functions used to read from a TensorFlow Lite format file.

 from .tflite.Model import Model
 from .tflite.BuiltinOperator import BuiltinOperator

 import numpy as np
 import os.path
 from .nn_graph import Graph, Operation, Subgraph
 from .tensor import Tensor, QuantizationParameters

 from .tflite_mapping import builtin_operator_map, datatype_map, datatype_map_numpy, DataType


 def decode_str(s):
     if s is None:
         return ""
     return s.decode("utf-8")


 def reshape_tensor_add_const_op(tens, reorder):
     if not tens.reshaped:
         original_shape = tens.shape
         tens.name = tens.name + "_reshape"
         tens.shape = [original_shape[idx] for idx in reorder]
         tens.bandwidth_shape = tens.shape
         tens.storage_shape = tens.shape

         if tens.values is not None:
             tens.values = tens.values.transpose(reorder)

         if tens.quant_values is not None:
             tens.quant_values = tens.quant_values.transpose(reorder)

         op = Operation("Const", tens.name)
         op.outputs = [tens]
         tens.ops = [op]
         tens.reshaped = True


 class TFLiteSubgraph:
     def __init__(self, graph, subgraph):
         self.graph = graph
         self.name = decode_str(subgraph.Name())

         self.tensors = []
         for idx in range(subgraph.TensorsLength()):
             self.tensors.append(self.parse_tensor(subgraph.Tensors(idx)))

         for idx in range(subgraph.OperatorsLength()):
             self.parse_operator(subgraph.Operators(idx))

         self.outputs = [self.tensors[idx] for idx in subgraph.OutputsAsNumpy()]
         self.inputs = [self.tensors[idx] for idx in subgraph.InputsAsNumpy()]

         # Fix up tensors without operations. Generate either Placeholder or Constant ops
         for tens in self.inputs:
             assert not tens.ops
             op = Operation("Placeholder", tens.name)
             op.outputs = [tens]
             tens.ops = [op]

         for tens in self.tensors:
             if not tens.ops:
                 op = Operation("Const", tens.name)
                 op.outputs = [tens]
                 tens.ops = [op]

     def parse_tensor(self, tens_data):
         np_shape = tens_data.ShapeAsNumpy()
         shape = list(np_shape) if type(np_shape) is np.ndarray else []
         name = decode_str(tens_data.Name())
         dtype = datatype_map[tens_data.Type()]

         tens = Tensor(shape, dtype, name)

         quant = tens_data.Quantization()

         def len1_array_to_scalar(arr):
             # The following flatbuffer quantisation fields all return a scalar value of 0 if they are not definied in
             # the input buffer. This is represented in Vela by using None.
             # Otherwise, the fields returned are a single or multi-element array. In which case, single element arrays
             # are converted to scalars
             if isinstance(arr, int) and arr == 0:
                 return None
             if len(arr) == 1:
                 return arr[0]
             return arr

         tens.quantization = QuantizationParameters()
         tens.quantization.min = len1_array_to_scalar(quant.MinAsNumpy())
         tens.quantization.max = len1_array_to_scalar(quant.MaxAsNumpy())
         tens.quantization.scale_f32 = len1_array_to_scalar(quant.ScaleAsNumpy())
         tens.quantization.zero_point = len1_array_to_scalar(quant.ZeroPointAsNumpy())

         if dtype == DataType.uint8:
             tens.quantization.quant_min = 0
             tens.quantization.quant_max = (1 << dtype.bits) - 1
         elif dtype in set((DataType.int8, DataType.int16, DataType.int32, DataType.int64)):
             tens.quantization.quant_min = -(1 << (dtype.bits - 1))
             tens.quantization.quant_max = (1 << (dtype.bits - 1)) - 1
         else:
             raise Exception("DataType '" + str(dtype) + "' is not supported for quantization.")

         if tens.quantization.scale_f32 is None and tens.quantization.zero_point is None:
             tens.quantization = None

         tens.values = None
         buf = self.graph.buffers[tens_data.Buffer()]
         if buf is not None:
             tens.values = np.array(buf.view(datatype_map_numpy[tens_data.Type()]).reshape(shape))
             if tens.quantization is not None:
                 tens.quant_values = tens.values
                 tens.values = tens.quantization.dequantize(tens.quant_values)
         return tens

     def parse_operator(self, op_data):
         op_type, opt_serializer = self.graph.operator_codes[op_data.OpcodeIndex()]
         inputs = [self.tensors[idx] for idx in op_data.InputsAsNumpy()]
         outputs = [self.tensors[idx] for idx in op_data.OutputsAsNumpy()]
         name = "unknown_op_name"
         if len(outputs):
             name = outputs[0].name
         op = Operation(op_type, name)
         op.inputs = inputs
         op.outputs = outputs
         for out in op.outputs:
             out.ops = [op]

         activation_function_to_split_out = None

         if op_type.startswith("DepthwiseConv2d") or op_type.startswith("Conv2D"):
             reshape_tensor_add_const_op(inputs[1], (1, 2, 3, 0))

         if op_type.startswith("FullyConnected"):
             reshape_tensor_add_const_op(inputs[1], (1, 0))

         if opt_serializer is not None:
             op.attrs = opt_serializer.deserialize(op_data.BuiltinOptions(), op_data.CustomOptionsAsNumpy())

             if "stride_w" in op.attrs:
                 op.attrs["strides"] = (1, op.attrs["stride_h"], op.attrs["stride_w"], 1)
             if "filter_width" in op.attrs:
                 op.attrs["ksize"] = (1, op.attrs["filter_height"], op.attrs["filter_width"], 1)
             if "dilation_w_factor" in op.attrs:
                 op.attrs["dilation"] = (1, op.attrs["dilation_h_factor"], op.attrs["dilation_w_factor"], 1)
             if "depth_multiplier" in op.attrs:
                 op.attrs["channel_multiplier"] = op.attrs["depth_multiplier"]

             if "fused_activation_function" in op.attrs:
                 if op_type in set(("ConcatTFLite",)):
                     act = op.attrs["fused_activation_function"]
                     del op.attrs["fused_activation_function"]
                     if act is not None:
                         activation_function_to_split_out = act

         if activation_function_to_split_out is not None:
             act_op = Operation(activation_function_to_split_out, name + activation_function_to_split_out)
             out_tens = op.outputs[0]
             intermediate_tens = out_tens.clone("_act_intermediate")
             out_tens.ops = [act_op]
             act_op.outputs = [out_tens]
             intermediate_tens.ops = [op]
             op.outputs[0] = intermediate_tens
             act_op.inputs = [intermediate_tens]


 class TFLiteGraph:
     def __init__(
         self,
         filename,
         batch_size=1,
         feed_dict={},
         output_node_names=[],
         initialisation_nodes=[],
     ):

         self.op_times = {}
         if batch_size is None:
             batch_size = 1
         self.batch_size = batch_size
         self.name = os.path.splitext(os.path.basename(filename))[0]
         self.initialisation_nodes = initialisation_nodes

         with open(filename, "rb") as f:
             buf = bytearray(f.read())

         model = Model.GetRootAsModel(buf, 0)

         self.buffers = []
         for idx in range(model.BuffersLength()):
             self.buffers.append(self.parse_buffer(model.Buffers(idx)))

         self.operator_codes = []
         for idx in range(model.OperatorCodesLength()):
             self.operator_codes.append(self.parse_operator_code(model.OperatorCodes(idx)))

         self.subgraphs = []
         for idx in range(model.SubgraphsLength()):
             self.subgraphs.append(TFLiteSubgraph(self, model.Subgraphs(idx)))

         self.nng = Graph(self.name, self.batch_size)
         for tflite_sg in self.subgraphs:
             sg = Subgraph(tflite_sg.name)
             sg.original_inputs = tflite_sg.inputs  # Preserve the original input order
             sg.output_tensors = tflite_sg.outputs
             self.nng.subgraphs.append(sg)

     def parse_buffer(self, buf_data):
         if buf_data.DataLength() == 0:
             return None
         data = buf_data.DataAsNumpy()
         return data

     def parse_operator_code(self, code):
         c = code.BuiltinCode()
         op_type, ser = builtin_operator_map[c]
         if c == BuiltinOperator.CUSTOM:
             op_type += decode_str(code.CustomCode())
         return op_type, ser


 def read_tflite(
     filename,
     batch_size=1,
     feed_dict={},
     output_node_names=[],
     initialisation_nodes=[],
 ):
     tflite_graph = TFLiteGraph(
         filename, batch_size, feed_dict, output_node_names, initialisation_nodes
     )
     nng = tflite_graph.nng
     nng.refresh_after_modification()
     return nng
	# Copyright (C) 2020 Arm Limited or its affiliates. All rights reserved.
	#
	# SPDX-License-Identifier: Apache-2.0
	#
	# Licensed under the Apache License, Version 2.0 (the License); you may
	# not use this file except in compliance with the License.
	# You may obtain a copy of the License at
	#
	# www.apache.org/licenses/LICENSE-2.0
	#
	# Unless required by applicable law or agreed to in writing, software
	# distributed under the License is distributed on an AS IS BASIS, WITHOUT
	# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
	# See the License for the specific language governing permissions and
	# limitations under the License.


	# Description:
	# Functions used to read from a TensorFlow Lite format file.

	from .tflite.Model import Model
	from .tflite.BuiltinOperator import BuiltinOperator

	import numpy as np
	import os.path
	from .nn_graph import Graph, Operation, Subgraph
	from .tensor import Tensor, QuantizationParameters

	from .tflite_mapping import builtin_operator_map, datatype_map, datatype_map_numpy, DataType


	def decode_str(s):
	if s is None:
	return ""
	return s.decode("utf-8")


	def reshape_tensor_add_const_op(tens, reorder):
	if not tens.reshaped:
	original_shape = tens.shape
	tens.name = tens.name + "_reshape"
	tens.shape = [original_shape[idx] for idx in reorder]
	tens.bandwidth_shape = tens.shape
	tens.storage_shape = tens.shape

	if tens.values is not None:
	tens.values = tens.values.transpose(reorder)

	if tens.quant_values is not None:
	tens.quant_values = tens.quant_values.transpose(reorder)

	op = Operation("Const", tens.name)
	op.outputs = [tens]
	tens.ops = [op]
	tens.reshaped = True


	class TFLiteSubgraph:
	def __init__(self, graph, subgraph):
	self.graph = graph
	self.name = decode_str(subgraph.Name())

	self.tensors = []
	for idx in range(subgraph.TensorsLength()):
	self.tensors.append(self.parse_tensor(subgraph.Tensors(idx)))

	for idx in range(subgraph.OperatorsLength()):
	self.parse_operator(subgraph.Operators(idx))

	self.outputs = [self.tensors[idx] for idx in subgraph.OutputsAsNumpy()]
	self.inputs = [self.tensors[idx] for idx in subgraph.InputsAsNumpy()]

	# Fix up tensors without operations. Generate either Placeholder or Constant ops
	for tens in self.inputs:
	assert not tens.ops
	op = Operation("Placeholder", tens.name)
	op.outputs = [tens]
	tens.ops = [op]

	for tens in self.tensors:
	if not tens.ops:
	op = Operation("Const", tens.name)
	op.outputs = [tens]
	tens.ops = [op]

	def parse_tensor(self, tens_data):
	np_shape = tens_data.ShapeAsNumpy()
	shape = list(np_shape) if type(np_shape) is np.ndarray else []
	name = decode_str(tens_data.Name())
	dtype = datatype_map[tens_data.Type()]

	tens = Tensor(shape, dtype, name)

	quant = tens_data.Quantization()

	def len1_array_to_scalar(arr):
	# The following flatbuffer quantisation fields all return a scalar value of 0 if they are not definied in
	# the input buffer. This is represented in Vela by using None.
	# Otherwise, the fields returned are a single or multi-element array. In which case, single element arrays
	# are converted to scalars
	if isinstance(arr, int) and arr == 0:
	return None
	if len(arr) == 1:
	return arr[0]
	return arr

	tens.quantization = QuantizationParameters()
	tens.quantization.min = len1_array_to_scalar(quant.MinAsNumpy())
	tens.quantization.max = len1_array_to_scalar(quant.MaxAsNumpy())
	tens.quantization.scale_f32 = len1_array_to_scalar(quant.ScaleAsNumpy())
	tens.quantization.zero_point = len1_array_to_scalar(quant.ZeroPointAsNumpy())

	if dtype == DataType.uint8:
	tens.quantization.quant_min = 0
	tens.quantization.quant_max = (1 << dtype.bits) - 1
	elif dtype in set((DataType.int8, DataType.int16, DataType.int32, DataType.int64)):
	tens.quantization.quant_min = -(1 << (dtype.bits - 1))
	tens.quantization.quant_max = (1 << (dtype.bits - 1)) - 1
	else:
	raise Exception("DataType '" + str(dtype) + "' is not supported for quantization.")

	if tens.quantization.scale_f32 is None and tens.quantization.zero_point is None:
	tens.quantization = None

	tens.values = None
	buf = self.graph.buffers[tens_data.Buffer()]
	if buf is not None:
	tens.values = np.array(buf.view(datatype_map_numpy[tens_data.Type()]).reshape(shape))
	if tens.quantization is not None:
	tens.quant_values = tens.values
	tens.values = tens.quantization.dequantize(tens.quant_values)
	return tens

	def parse_operator(self, op_data):
	op_type, opt_serializer = self.graph.operator_codes[op_data.OpcodeIndex()]
	inputs = [self.tensors[idx] for idx in op_data.InputsAsNumpy()]
	outputs = [self.tensors[idx] for idx in op_data.OutputsAsNumpy()]
	name = "unknown_op_name"
	if len(outputs):
	name = outputs[0].name
	op = Operation(op_type, name)
	op.inputs = inputs
	op.outputs = outputs
	for out in op.outputs:
	out.ops = [op]

	activation_function_to_split_out = None

	if op_type.startswith("DepthwiseConv2d") or op_type.startswith("Conv2D"):
	reshape_tensor_add_const_op(inputs[1], (1, 2, 3, 0))

	if op_type.startswith("FullyConnected"):
	reshape_tensor_add_const_op(inputs[1], (1, 0))

	if opt_serializer is not None:
	op.attrs = opt_serializer.deserialize(op_data.BuiltinOptions(), op_data.CustomOptionsAsNumpy())

	if "stride_w" in op.attrs:
	op.attrs["strides"] = (1, op.attrs["stride_h"], op.attrs["stride_w"], 1)
	if "filter_width" in op.attrs:
	op.attrs["ksize"] = (1, op.attrs["filter_height"], op.attrs["filter_width"], 1)
	if "dilation_w_factor" in op.attrs:
	op.attrs["dilation"] = (1, op.attrs["dilation_h_factor"], op.attrs["dilation_w_factor"], 1)
	if "depth_multiplier" in op.attrs:
	op.attrs["channel_multiplier"] = op.attrs["depth_multiplier"]

	if "fused_activation_function" in op.attrs:
	if op_type in set(("ConcatTFLite",)):
	act = op.attrs["fused_activation_function"]
	del op.attrs["fused_activation_function"]
	if act is not None:
	activation_function_to_split_out = act

	if activation_function_to_split_out is not None:
	act_op = Operation(activation_function_to_split_out, name + activation_function_to_split_out)
	out_tens = op.outputs[0]
	intermediate_tens = out_tens.clone("_act_intermediate")
	out_tens.ops = [act_op]
	act_op.outputs = [out_tens]
	intermediate_tens.ops = [op]
	op.outputs[0] = intermediate_tens
	act_op.inputs = [intermediate_tens]


	class TFLiteGraph:
	def __init__(
	self,
	filename,
	batch_size=1,
	feed_dict={},
	output_node_names=[],
	initialisation_nodes=[],
	):

	self.op_times = {}
	if batch_size is None:
	batch_size = 1
	self.batch_size = batch_size
	self.name = os.path.splitext(os.path.basename(filename))[0]
	self.initialisation_nodes = initialisation_nodes

	with open(filename, "rb") as f:
	buf = bytearray(f.read())

	model = Model.GetRootAsModel(buf, 0)

	self.buffers = []
	for idx in range(model.BuffersLength()):
	self.buffers.append(self.parse_buffer(model.Buffers(idx)))

	self.operator_codes = []
	for idx in range(model.OperatorCodesLength()):
	self.operator_codes.append(self.parse_operator_code(model.OperatorCodes(idx)))

	self.subgraphs = []
	for idx in range(model.SubgraphsLength()):
	self.subgraphs.append(TFLiteSubgraph(self, model.Subgraphs(idx)))

	self.nng = Graph(self.name, self.batch_size)
	for tflite_sg in self.subgraphs:
	sg = Subgraph(tflite_sg.name)
	sg.original_inputs = tflite_sg.inputs # Preserve the original input order
	sg.output_tensors = tflite_sg.outputs
	self.nng.subgraphs.append(sg)

	def parse_buffer(self, buf_data):
	if buf_data.DataLength() == 0:
	return None
	data = buf_data.DataAsNumpy()
	return data

	def parse_operator_code(self, code):
	c = code.BuiltinCode()
	op_type, ser = builtin_operator_map[c]
	if c == BuiltinOperator.CUSTOM:
	op_type += decode_str(code.CustomCode())
	return op_type, ser


	def read_tflite(
	filename,
	batch_size=1,
	feed_dict={},
	output_node_names=[],
	initialisation_nodes=[],
	):
	tflite_graph = TFLiteGraph(
	filename, batch_size, feed_dict, output_node_names, initialisation_nodes
	)
	nng = tflite_graph.nng
	nng.refresh_after_modification()
	return nng