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https://github.com/vee1e/tflite-micro.git
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b45012b52c
The RISC-V toolchain we're using failed to properly discern the TfLiteEvalTensor type for use with an assignment operator. It produced compiler errors for "no match for 'operator=' with operand types TfLiteEvalTensor and a brace-closed initializer list. This PR resolves this issue by explicitly using the TfLiteEvalTensor constructor for the initializer list. We also apply this approach to the TfLiteNode initialization as well, just for consistency. BUG=#2195
127 lines
4.6 KiB
Python
127 lines
4.6 KiB
Python
# Copyright 2023 The TensorFlow Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ==============================================================================
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""" Tensor class """
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from typing import Dict, Optional
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import string
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import textwrap
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from tflite_micro.codegen import utils
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from tflite_micro.tensorflow.lite.python import schema_py_generated as schema_fb
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_TENSOR_TYPES: Dict[int, str] = {
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schema_fb.TensorType.FLOAT16: "kTfLiteFloat16",
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schema_fb.TensorType.FLOAT32: "kTfLiteFloat32",
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schema_fb.TensorType.FLOAT64: "kTfLiteFloat64",
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schema_fb.TensorType.INT16: "kTfLiteInt16",
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schema_fb.TensorType.UINT16: "kTfLiteUInt16",
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schema_fb.TensorType.INT32: "kTfLiteInt32",
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schema_fb.TensorType.UINT32: "kTfLiteUInt32",
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schema_fb.TensorType.UINT8: "kTfLiteUInt8",
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schema_fb.TensorType.INT8: "kTfLiteInt8",
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schema_fb.TensorType.INT64: "kTfLiteInt64",
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schema_fb.TensorType.UINT64: "kTfLiteUInt64",
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schema_fb.TensorType.STRING: "kTfLiteString",
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schema_fb.TensorType.BOOL: "kTfLiteBool",
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schema_fb.TensorType.COMPLEX64: "kTfLiteComplex64",
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schema_fb.TensorType.COMPLEX128: "kTfLiteComplex128",
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schema_fb.TensorType.RESOURCE: "kTfLiteResource",
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schema_fb.TensorType.VARIANT: "kTfLiteVariant",
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schema_fb.TensorType.INT4: "kTfLiteInt4",
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}
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class Buffer(object):
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""" This buffer could be either a static array or a pointer into the arena """
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def __init__(self, buffer_name: str, buffer: schema_fb.BufferT):
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# TODO(rjascani): Get arena allocation offsets from preprocessor
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self._buffer_name = buffer_name
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self._buffer = buffer
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@property
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def address(self) -> str:
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if self._buffer is None or self._buffer.data is None:
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# TODO(rjascani): This needs to point into the arena
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return f"nullptr /* {self._buffer_name} */"
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return f"&{self._buffer_name}"
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def generate_c_buffer_array(self, indent: str) -> str:
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if self._buffer is None or self._buffer.data is None:
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return f"// {self._buffer_name} is located in the arena\n"
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buffer_template = string.Template(
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"alignas(16) uint8_t ${buffer_name}[${size}] = {\n"
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"${body}\n"
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"};\n")
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byte_strs = ['0x{:02X}'.format(b) for b in self._buffer.data]
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lines = []
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for byte_strs_for_line in utils.split_into_chunks(byte_strs, 12):
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bytes_segment = ', '.join(byte_strs_for_line)
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lines.append(f' {bytes_segment},')
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return textwrap.indent(
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buffer_template.substitute(buffer_name=self._buffer_name,
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size=len(self._buffer.data),
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body='\n'.join(lines)), indent)
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class Tensor(object):
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def __init__(self, buffer: Buffer, tensor: schema_fb.TensorT):
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self._buffer = buffer
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self._tensor: schema_fb.TensorT = tensor
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@property
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def buffer_index(self) -> bool:
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return self._tensor.buffer
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@property
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def buffer(self) -> Buffer:
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return self._buffer
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@property
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def has_shape(self) -> bool:
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return self._tensor.shape is not None
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@property
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def needs_zero_length_int_array(self) -> bool:
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return not self.has_shape
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def generate_c_tensor_dims(self, type_name: str, tensor_name: str) -> str:
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if not self.has_shape:
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return f"// No data dims necessary for {tensor_name}"
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return utils.IntArray(self._tensor.shape).generate_c_struct(
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type_name + "Dims", tensor_name + "_dims")
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def generate_c_tensor_init(self, tflite_tensor_name: str,
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tensor_name: str) -> str:
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init_template = string.Template(
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"${tflite_tensor_name} = TfLiteEvalTensor{\n"
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" .data = {.data = static_cast<void*>(${data})},\n"
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" .dims = ${dims},\n"
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" .type = ${tflite_type}};")
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dims = "reinterpret_cast<TfLiteIntArray*>(&{})".format(
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f"{tensor_name}_dims" if self._tensor.
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shape is not None else "zero_length_int_array")
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return init_template.substitute(
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tflite_tensor_name=tflite_tensor_name,
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tensor_name=tensor_name,
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data=self._buffer.address,
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dims=dims,
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tflite_type=_TENSOR_TYPES[self._tensor.type])
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