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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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126 changes: 63 additions & 63 deletions
126
model_compiler/src/model_compiler/compilers/caffe_model_file_to_onnx_model.py
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@@ -1,65 +1,65 @@ | ||
# Copyright 2019 ZTE corporation. All Rights Reserved. | ||
# SPDX-License-Identifier: Apache-2.0 | ||
# # Copyright 2019 ZTE corporation. All Rights Reserved. | ||
# # SPDX-License-Identifier: Apache-2.0 | ||
# | ||
# import os | ||
# from typing import Any, Mapping, NamedTuple, Optional, Sequence, List | ||
# import numpy as np | ||
# from caffe2.proto import caffe2_pb2 | ||
# from . import repository | ||
# from .. import utilities | ||
# from ..models.data_format import DataFormat | ||
# from ..models.data_type import DataType | ||
# from ..models.irs.onnx_model import OnnxModel | ||
# from ..models.sources.caffe_model_file import CaffeModelFile | ||
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import os | ||
from typing import Any, Mapping, NamedTuple, Optional, Sequence, List | ||
import numpy as np | ||
import caffe2.python.onnx.frontend | ||
from caffe2.proto import caffe2_pb2 | ||
from . import repository | ||
from .. import utilities | ||
from ..models.data_format import DataFormat | ||
from ..models.data_type import DataType | ||
from ..models.irs.onnx_model import OnnxModel | ||
from ..models.sources.caffe_model_file import CaffeModelFile | ||
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class Config(NamedTuple): | ||
input_names: Sequence[str] | ||
input_formats: Sequence[Optional[DataFormat]] | ||
input_shapes: List[List] | ||
input_type: np.dtype | ||
max_batch_size: int | ||
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@staticmethod | ||
def from_json(value: Mapping[str, Any]) -> 'Config': | ||
return Config(input_names=value['input_names'], | ||
input_formats=utilities.get_data_formats(value.get('input_formats')), | ||
input_shapes=utilities.get_input_shapes(value['input_shapes']), | ||
input_type=DataType.from_caffe_data_type(value['data_type']).to_onnx_data_type(), | ||
max_batch_size=value['max_batch_size']) | ||
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@staticmethod | ||
def from_env(env: Mapping[str, str]) -> 'Config': | ||
return Config(input_names=env['INPUT_NAMES'].split(','), | ||
input_formats=utilities.get_data_formats(utilities.split_by(env.get('INPUT_FORMATS'), ',')), | ||
input_shapes=utilities.get_input_shapes( | ||
utilities.get_input_shapes_from_env(env.get('INPUT_SHAPES')) | ||
), | ||
input_type=DataType.from_caffe_data_type(env['DATA_TYPE']).to_onnx_data_type(), | ||
max_batch_size=int(env['MAX_BATCH_SIZE'])) | ||
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def parse_caffe_net(net, pb_path): | ||
with open(pb_path, 'rb') as file: | ||
net.ParseFromString(file.read()) | ||
return net | ||
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@repository.REPOSITORY.register(source_type=CaffeModelFile, target_type=OnnxModel, config_type=Config) | ||
def compile_source(source: CaffeModelFile, config: Config) -> OnnxModel: | ||
predict_net = parse_caffe_net(caffe2_pb2.NetDef(), os.path.join(source.model_path, 'predict_net.pb')) | ||
predict_net.name = "model" if predict_net.name == "" else predict_net.name # pylint: disable=no-member | ||
init_net = parse_caffe_net(caffe2_pb2.NetDef(), os.path.join(source.model_path, 'init_net.pb')) | ||
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value_info = {} | ||
for i, input_shape in enumerate(config.input_shapes): | ||
input_shape.insert(0, config.max_batch_size) | ||
value_info[config.input_names[i]] = (config.input_type, input_shape) | ||
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onnx_model = caffe2.python.onnx.frontend.caffe2_net_to_onnx_model(predict_net, init_net, value_info) | ||
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graph = onnx_model.graph # pylint: disable=no-member | ||
return OnnxModel(model_proto=onnx_model, | ||
input_data_formats=utilities.get_onnx_model_input_data_formats(graph, | ||
config.input_formats)) | ||
# class Config(NamedTuple): | ||
# input_names: Sequence[str] | ||
# input_formats: Sequence[Optional[DataFormat]] | ||
# input_shapes: List[List] | ||
# input_type: np.dtype | ||
# max_batch_size: int | ||
# | ||
# @staticmethod | ||
# def from_json(value: Mapping[str, Any]) -> 'Config': | ||
# return Config(input_names=value['input_names'], | ||
# input_formats=utilities.get_data_formats(value.get('input_formats')), | ||
# input_shapes=utilities.get_input_shapes(value['input_shapes']), | ||
# input_type=DataType.from_caffe_data_type(value['data_type']).to_onnx_data_type(), | ||
# max_batch_size=value['max_batch_size']) | ||
# | ||
# @staticmethod | ||
# def from_env(env: Mapping[str, str]) -> 'Config': | ||
# return Config(input_names=env['INPUT_NAMES'].split(','), | ||
# input_formats=utilities.get_data_formats(utilities.split_by(env.get('INPUT_FORMATS'), ',')), | ||
# input_shapes=utilities.get_input_shapes( | ||
# utilities.get_input_shapes_from_env(env.get('INPUT_SHAPES')) | ||
# ), | ||
# input_type=DataType.from_caffe_data_type(env['DATA_TYPE']).to_onnx_data_type(), | ||
# max_batch_size=int(env['MAX_BATCH_SIZE'])) | ||
# | ||
# | ||
# def parse_caffe_net(net, pb_path): | ||
# with open(pb_path, 'rb') as file: | ||
# net.ParseFromString(file.read()) | ||
# return net | ||
# | ||
# | ||
# @repository.REPOSITORY.register(source_type=CaffeModelFile, target_type=OnnxModel, config_type=Config) | ||
# def compile_source(source: CaffeModelFile, config: Config) -> OnnxModel: | ||
# predict_net = parse_caffe_net(caffe2_pb2.NetDef(), os.path.join(source.model_path, 'predict_net.pb')) | ||
# predict_net.name = "model" if predict_net.name == "" else predict_net.name # pylint: disable=no-member | ||
# init_net = parse_caffe_net(caffe2_pb2.NetDef(), os.path.join(source.model_path, 'init_net.pb')) | ||
# | ||
# value_info = {} | ||
# for i, input_shape in enumerate(config.input_shapes): | ||
# input_shape.insert(0, config.max_batch_size) | ||
# value_info[config.input_names[i]] = (config.input_type, input_shape) | ||
# | ||
# from caffe2.python.onnx.frontend import caffe2_net_to_onnx_model | ||
# onnx_model = caffe2_net_to_onnx_model(predict_net, init_net, value_info) | ||
# | ||
# graph = onnx_model.graph # pylint: disable=no-member | ||
# return OnnxModel(model_proto=onnx_model, | ||
# input_data_formats=utilities.get_onnx_model_input_data_formats(graph, | ||
# config.input_formats)) |
86 changes: 43 additions & 43 deletions
86
model_compiler/src/model_compiler/compilers/keras_model_file_to_tvm_model.py
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@@ -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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