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Diffstat (limited to 'ethosu/vela/tflite_reader.py')
| -rw-r--r-- | ethosu/vela/tflite_reader.py | 252 |
1 files changed, 252 insertions, 0 deletions
diff --git a/ethosu/vela/tflite_reader.py b/ethosu/vela/tflite_reader.py new file mode 100644 index 00000000..535847d7 --- /dev/null +++ b/ethosu/vela/tflite_reader.py @@ -0,0 +1,252 @@ +# 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 |