mirror of
https://github.com/mit-han-lab/tinyengine.git
synced 2025-05-09 17:11:01 +08:00
9a79c3aef0
* complation skeleton * pointwise imp * clean up headers * clean up bin * fix k3inpch3 kernel * add test * minor * bug fix * fix bug * pointwise func tests * fix 3x3 * fix randomize * missing file * minor * depthwise conv template * Support OpenCV with camera * convert kernel to c * update ground true * fix * Update to c * fix path * upload codegen * include sequence * minor * readme * readme * minor * update makefile * update opencv path info * update ins * revise demo UI * revise demo UI * fix bug * minor fix * larger model * force formating * Support MNIST * minor * draft version * basline finish * update main * c to cc * source * opt template * support demo w/ multi img * revise UI * minor * simd * README * include ifdef * fix bias * update ui and imp choice * minor * readme * minor * fix simd * minor * batch implementation * remove weight * refine weights * windows support * readme for windows * clean up vww op * tiling * reorganize * fix * multithreading * simd * fix multithread * opt imp * minor * rename * mv dirs * update readme --------- Co-authored-by: RaymondWang0 <wweichen@mit.edu>
188 lines
7.3 KiB
Python
188 lines
7.3 KiB
Python
# ----------------------------------------------------------------------
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# Project: TinyEngine
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# Title: TfliteConvertor.py
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#
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# Reference papers:
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# - MCUNet: Tiny Deep Learning on IoT Device, NeurIPS 2020
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# - MCUNetV2: Memory-Efficient Patch-based Inference for Tiny Deep Learning, NeurIPS 2021
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# - MCUNetV3: On-Device Training Under 256KB Memory, NeurIPS 2022
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# Contact authors:
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# - Wei-Ming Chen, wmchen@mit.edu
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# - Wei-Chen Wang, wweichen@mit.edu
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# - Ji Lin, jilin@mit.edu
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# - Ligeng Zhu, ligeng@mit.edu
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# - Song Han, songhan@mit.edu
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#
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# Target ISA: ARMv7E-M
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# ----------------------------------------------------------------------
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import logging
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import code_generator.converters.tflite_parser as TF_Parser
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from code_generator.converters.tflite_parser.mean1dto2d import MEAN2D
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from code_generator.converters.tflite_parser.utils import get_input_tensors, get_output_tensors, getOpCodeStr
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from .constant import SKIP_OPs
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from .tflite import Model
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# Parse tflite model into TinyEngine IR format
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class TfliteConvertor(object):
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def __init__(self, filepath):
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# path to the tflite file
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self.filepath = filepath
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self.model = self.loadTFmodel(filepath)
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self.subgraph = self.model.Subgraphs(0)
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self.layer = []
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self.tmpPADIndice = None
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self.skip_transpose = None
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self.average_1D_to_2D_holder = MEAN2D() # For merging 1D to 2D
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# public functions
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def loadTFmodel(self, filepath):
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buf = open(filepath, "rb").read()
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return Model.Model.GetRootAsModel(buf, 0)
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def dumpModelInfo(self):
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version = self.model.Version()
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print("Model version:", version)
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description = self.model.Description().decode("utf-8")
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print("Description:", description)
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subgraph_len = self.model.SubgraphsLength()
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print("Subgraph length:", subgraph_len)
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self.dumpLayerInfo()
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def dumpLayerInfo(self):
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print("Layer length:", len(self.layer))
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# print brief info about each layer
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for i, layer in enumerate(self.layer):
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if self.layer[i]["op"] == "ADD":
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print(
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"op:",
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layer["op"],
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",input_idx:",
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layer["input_idx"],
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",input2_idx:",
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layer["input2_idx"],
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"output_idx:",
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layer["output_idx"],
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)
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else:
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print(
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"op:",
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layer["op"],
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",input_idx:",
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layer["input_idx"],
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"output_idx:",
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layer["output_idx"],
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)
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def parseOperatorInfo(self):
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operators_len = self.subgraph.OperatorsLength()
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skip_next_ops = 0
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for i in range(operators_len):
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if skip_next_ops > 0:
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skip_next_ops -= 1
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continue
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op = self.subgraph.Operators(i)
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if i + 2 < operators_len - 2:
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next_op = self.subgraph.Operators(i + 1)
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next_next_op = self.subgraph.Operators(i + 2)
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three_op_sequence = [op, next_op, next_next_op]
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if self.checkIfRequireSEelementmult(three_op_sequence):
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logging.info("found SE block")
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skip_next_ops = 2
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# -> MEAN -> MEAN -> PWCONV -> PWCONV -> | ADD -> MUL -> |
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# DWCONV | -> MUL |
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# | SEelementmult |
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SEelementmult_op = TF_Parser.parse_SEelement(three_op_sequence, self.model, self.layer)
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self.layer.append(SEelementmult_op)
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continue
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# parse the op
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self._handleOperator(op)
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# handle one op and parse it into layers[] for supported operators
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def _handleOperator(self, op):
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op_code_str = getOpCodeStr(op, self.model)
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if op_code_str == "CONV_2D":
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self.layer.append(TF_Parser.parse_conv2d(op, self.model, self.tmpPADIndice))
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self.tmpPADIndice = None
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elif op_code_str == "ADD":
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self.layer.append(TF_Parser.parse_add(op, self.model))
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elif op_code_str == "AVERAGE_POOL_2D":
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self.layer.append(TF_Parser.parse_avgpool(op, self.model))
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elif op_code_str == "DEPTHWISE_CONV_2D":
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self.layer.append(TF_Parser.parse_conv2d(op, self.model, self.tmpPADIndice))
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self.tmpPADIndice = None
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elif op_code_str == "PAD":
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self._convert_PAD(op)
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elif op_code_str == "RESIZE_NEAREST_NEIGHBOR":
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self.layer.append(TF_Parser.parse_upsample(op, self.model))
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elif op_code_str == "MAX_POOL_2D":
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self.layer.append(TF_Parser.parse_maxpool(op, self.model))
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elif op_code_str in "MEAN":
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ret_op = TF_Parser.parse_mead1dto2d(op, self.model, self.average_1D_to_2D_holder)
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if ret_op is not None:
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# TODO: This only handle a specific graph: TRANSPOSE -> MEAN -> MEANS
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if self.skip_transpose is not None:
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ret_op.params["input_idx"] = self.skip_transpose.input_idx
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ret_op.input_tensors[0].graph_idx = self.skip_transpose.input_idx
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self.layer.append(ret_op)
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elif op_code_str == "TRANSPOSE":
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self._convert_TRANSPOSE(op)
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elif op_code_str == "FULLY_CONNECTED":
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self.layer.append(TF_Parser.parse_fc(op, self.model))
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elif op_code_str in SKIP_OPs:
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pass
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else:
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raise NotImplementedError(f"Unsupported {op_code_str}")
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# -> MEAN -> MEAN -> PWCONV -> PWCONV -> | ADD -> MUL -> |
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# DWCONV | -> MUL |
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# | Fuse Target |
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def checkIfRequireSEelementmult(self, three_op_sequence):
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if (
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getOpCodeStr(three_op_sequence[0], self.model) == "ADD"
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and getOpCodeStr(three_op_sequence[1], self.model) == "MUL"
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and getOpCodeStr(three_op_sequence[2], self.model) == "MUL"
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):
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return True
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return False
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def _convert_PAD(self, op):
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# get input, weight, and output tensors
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input_tensors = get_input_tensors(op, self.model)
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input_tensor = input_tensors[0]
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output_tensors = get_output_tensors(op, self.model)
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assert len(output_tensors) == 1, "output tensors length should be 1"
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output_tensor = output_tensors[0]
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# fuse pad into conv
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self.tmpPADIndice = PAD_tensorIndice(input_tensor.tensor_idx, output_tensor.tensor_idx)
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def _convert_TRANSPOSE(self, op):
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# get input, weight, and output tensors
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input_tensors = get_input_tensors(op, self.model)
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input_tensor = input_tensors[0]
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output_tensors = get_output_tensors(op, self.model)
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assert len(output_tensors) == 1, "output tensors length should be 1"
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output_tensor = output_tensors[0]
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# fuse pad into conv
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self.skip_transpose = PAD_tensorIndice(input_tensor.tensor_idx, output_tensor.tensor_idx)
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class PAD_tensorIndice(object):
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def __init__(self, input_idx, output_idx):
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self.input_idx = input_idx
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self.output_idx = output_idx
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