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Fix the bug of compile model to tensorrt
Closes #491 Signed-off-by: zhangkaili <zhang.kaili@zte.com.cn>
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86 changes: 43 additions & 43 deletions
86
model_compiler/src/model_compiler/compilers/keras_model_file_to_tvm_model.py
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Original file line number | Diff line number | Diff line change |
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# Copyright 2019 ZTE corporation. All Rights Reserved. | ||
# SPDX-License-Identifier: Apache-2.0 | ||
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import tensorflow as tf | ||
import tvm | ||
import tvm.relay as relay | ||
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from . import repository | ||
from ..models.sources.keras_model_file import KerasModelFile | ||
from ..models.targets.tvm_model import TvmModel, Input, Output | ||
from ..keras_util import Config, get_inputs, get_outputs, DataFormat | ||
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def _get_shape_dict(model_inputs, max_batch_size): | ||
shape_dict = {} | ||
for input_tensor, data_format in model_inputs: | ||
tensor_shape = list(input_tensor.shape) | ||
tensor_shape.pop(0) | ||
tensor_shape.insert(0, max_batch_size) | ||
if data_format == DataFormat.CHANNELS_LAST: | ||
tensor_shape[1], tensor_shape[3] = tensor_shape[3], tensor_shape[1] | ||
shape_dict[input_tensor.name] = tensor_shape | ||
return shape_dict | ||
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@repository.REPOSITORY.register(source_type=KerasModelFile, target_type=TvmModel, config_type=Config) | ||
def compile_source(source: KerasModelFile, config: Config) -> TvmModel: | ||
tf.keras.backend.set_learning_phase(0) | ||
source_model = tf.keras.models.load_model(source.model_path, compile=False) | ||
model_inputs = get_inputs(source_model, config.input_nodes) | ||
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shape_dict = _get_shape_dict(model_inputs, config.max_batch_size) | ||
model, params = relay.frontend.from_keras(source_model, shape_dict) | ||
compiled_lib = relay.build(model, tvm.target.create("llvm"), params=params) | ||
return TvmModel(tvm_model=compiled_lib, | ||
model_inputs=[Input(name=tensor.name, | ||
shape=shape_dict[tensor.name], | ||
data_type=tensor.dtype.as_datatype_enum, | ||
data_format=DataFormat.CHANNELS_FIRST) for tensor, _ in model_inputs], | ||
model_outputs=[Output(name=tensor.name, | ||
shape=list(tensor.shape), | ||
data_type=tensor.dtype.as_datatype_enum) | ||
for tensor in get_outputs(source_model, config.output_nodes)]) | ||
# # Copyright 2019 ZTE corporation. All Rights Reserved. | ||
# # SPDX-License-Identifier: Apache-2.0 | ||
# | ||
# import tensorflow as tf | ||
# import tvm | ||
# import tvm.relay as relay | ||
# | ||
# from . import repository | ||
# from ..models.sources.keras_model_file import KerasModelFile | ||
# from ..models.targets.tvm_model import TvmModel, Input, Output | ||
# from ..keras_util import Config, get_inputs, get_outputs, DataFormat | ||
# | ||
# | ||
# def _get_shape_dict(model_inputs, max_batch_size): | ||
# shape_dict = {} | ||
# for input_tensor, data_format in model_inputs: | ||
# tensor_shape = list(input_tensor.shape) | ||
# tensor_shape.pop(0) | ||
# tensor_shape.insert(0, max_batch_size) | ||
# if data_format == DataFormat.CHANNELS_LAST: | ||
# tensor_shape[1], tensor_shape[3] = tensor_shape[3], tensor_shape[1] | ||
# shape_dict[input_tensor.name] = tensor_shape | ||
# return shape_dict | ||
# | ||
# | ||
# @repository.REPOSITORY.register(source_type=KerasModelFile, target_type=TvmModel, config_type=Config) | ||
# def compile_source(source: KerasModelFile, config: Config) -> TvmModel: | ||
# tf.keras.backend.set_learning_phase(0) | ||
# source_model = tf.keras.models.load_model(source.model_path, compile=False) | ||
# model_inputs = get_inputs(source_model, config.input_nodes) | ||
# | ||
# shape_dict = _get_shape_dict(model_inputs, config.max_batch_size) | ||
# model, params = relay.frontend.from_keras(source_model, shape_dict) | ||
# compiled_lib = relay.build(model, tvm.target.create("llvm"), params=params) | ||
# return TvmModel(tvm_model=compiled_lib, | ||
# model_inputs=[Input(name=tensor.name, | ||
# shape=shape_dict[tensor.name], | ||
# data_type=tensor.dtype.as_datatype_enum, | ||
# data_format=DataFormat.CHANNELS_FIRST) for tensor, _ in model_inputs], | ||
# model_outputs=[Output(name=tensor.name, | ||
# shape=list(tensor.shape), | ||
# data_type=tensor.dtype.as_datatype_enum) | ||
# for tensor in get_outputs(source_model, config.output_nodes)]) |
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