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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 |
|---|---|---|
| @@ -1,43 +1,43 @@ | ||
| # 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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106 changes: 53 additions & 53 deletions
106
model_compiler/src/model_compiler/models/targets/tvm_model.py
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -1,53 +1,53 @@ | ||
| # Copyright 2019 ZTE corporation. All Rights Reserved. | ||
| # SPDX-License-Identifier: Apache-2.0 | ||
|
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| import os | ||
| from typing import Any, NamedTuple, Optional, Sequence, Tuple | ||
| import tvm | ||
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| from .. import data_format, repository | ||
| from ..data_format import DataFormat | ||
| from ...protos.generated.model_config_pb2 import ModelInput, ModelOutput | ||
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| class Input(NamedTuple): | ||
| name: str | ||
| shape: list | ||
| data_type: Any | ||
| data_format: Optional[DataFormat] = None | ||
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| class Output(NamedTuple): | ||
| name: str | ||
| shape: list | ||
| data_type: Any | ||
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| @repository.REPOSITORY.register_target_model('tvm') | ||
| class TvmModel(NamedTuple): | ||
| tvm_model: tvm.relay.backend.graph_runtime_factory.GraphRuntimeFactoryModule | ||
| model_inputs: Sequence[Input] | ||
| model_outputs: Sequence[Output] | ||
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| def get_inputs(self) -> Sequence[ModelInput]: | ||
| return [ModelInput(name=name, | ||
| data_type=data_type, | ||
| format=data_format.as_model_config_data_format(input_format), | ||
| dims=[-1 if dim is None else dim for dim in shape[1:]]) | ||
| for name, shape, data_type, input_format in self.model_inputs] | ||
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| def get_outputs(self) -> Sequence[ModelOutput]: | ||
| return [ModelOutput(name=name, | ||
| data_type=data_type, | ||
| dims=[-1 if dim is None else dim for dim in shape[1:]]) | ||
| for name, shape, data_type in self.model_outputs] | ||
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| def save(self, path: str) -> None: | ||
| os.makedirs(path, exist_ok=True) | ||
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| with open(os.path.join(path, 'model.so'), 'wb') as file: | ||
| self.tvm_model.export_library(file.name) | ||
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| @staticmethod | ||
| def get_platform() -> Tuple[str, str]: | ||
| return 'tvm', tvm.__version__ | ||
| # # Copyright 2019 ZTE corporation. All Rights Reserved. | ||
| # # SPDX-License-Identifier: Apache-2.0 | ||
| # | ||
| # import os | ||
| # from typing import Any, NamedTuple, Optional, Sequence, Tuple | ||
| # import tvm | ||
| # | ||
| # from .. import data_format, repository | ||
| # from ..data_format import DataFormat | ||
| # from ...protos.generated.model_config_pb2 import ModelInput, ModelOutput | ||
| # | ||
| # | ||
| # class Input(NamedTuple): | ||
| # name: str | ||
| # shape: list | ||
| # data_type: Any | ||
| # data_format: Optional[DataFormat] = None | ||
| # | ||
| # | ||
| # class Output(NamedTuple): | ||
| # name: str | ||
| # shape: list | ||
| # data_type: Any | ||
| # | ||
| # | ||
| # @repository.REPOSITORY.register_target_model('tvm') | ||
| # class TvmModel(NamedTuple): | ||
| # tvm_model: tvm.relay.backend.graph_runtime_factory.GraphRuntimeFactoryModule | ||
| # model_inputs: Sequence[Input] | ||
| # model_outputs: Sequence[Output] | ||
| # | ||
| # def get_inputs(self) -> Sequence[ModelInput]: | ||
| # return [ModelInput(name=name, | ||
| # data_type=data_type, | ||
| # format=data_format.as_model_config_data_format(input_format), | ||
| # dims=[-1 if dim is None else dim for dim in shape[1:]]) | ||
| # for name, shape, data_type, input_format in self.model_inputs] | ||
| # | ||
| # def get_outputs(self) -> Sequence[ModelOutput]: | ||
| # return [ModelOutput(name=name, | ||
| # data_type=data_type, | ||
| # dims=[-1 if dim is None else dim for dim in shape[1:]]) | ||
| # for name, shape, data_type in self.model_outputs] | ||
| # | ||
| # def save(self, path: str) -> None: | ||
| # os.makedirs(path, exist_ok=True) | ||
| # | ||
| # with open(os.path.join(path, 'model.so'), 'wb') as file: | ||
| # self.tvm_model.export_library(file.name) | ||
| # | ||
| # @staticmethod | ||
| # def get_platform() -> Tuple[str, str]: | ||
| # return 'tvm', tvm.__version__ |
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