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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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54 changes: 27 additions & 27 deletions
54
model_compiler/src/model_compiler/compilers/keras_model_file_to_keras_model.py
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -1,27 +1,27 @@ | ||
| # Copyright 2019 ZTE corporation. All Rights Reserved. | ||
| # SPDX-License-Identifier: Apache-2.0 | ||
|
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| import tensorflow as tf | ||
| from tensorflow import keras | ||
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| from . import repository | ||
| from .. import utilities | ||
| from .. import keras_util | ||
| from ..models.irs.keras_model import KerasModel | ||
| from ..models.sources.keras_model_file import KerasModelFile | ||
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| @repository.REPOSITORY.register(source_type=KerasModelFile, target_type=KerasModel) | ||
| def compile_source(source: KerasModelFile) -> KerasModel: | ||
| with tf.Graph().as_default(): | ||
| if source.script_path: | ||
| with tf.compat.v1.Session(graph=tf.Graph(), config=utilities.get_tf_cpu_only_config()): | ||
| custom_objects = keras_util.get_custom_objects(source.script_path) | ||
| else: | ||
| custom_objects = None | ||
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| with tf.compat.v1.Session(config=utilities.get_tf_cpu_only_config()).as_default() as session: | ||
| keras.backend.set_learning_phase(0) | ||
| model = keras.models.load_model(source.model_path, custom_objects=custom_objects, compile=False) | ||
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| return KerasModel(model=model, session=session) | ||
| # # Copyright 2019 ZTE corporation. All Rights Reserved. | ||
| # # SPDX-License-Identifier: Apache-2.0 | ||
| # | ||
| # import tensorflow as tf | ||
| # from tensorflow import keras | ||
| # | ||
| # from . import repository | ||
| # from .. import utilities | ||
| # from .. import keras_util | ||
| # from ..models.irs.keras_model import KerasModel | ||
| # from ..models.sources.keras_model_file import KerasModelFile | ||
| # | ||
| # | ||
| # @repository.REPOSITORY.register(source_type=KerasModelFile, target_type=KerasModel) | ||
| # def compile_source(source: KerasModelFile) -> KerasModel: | ||
| # with tf.Graph().as_default(): | ||
| # if source.script_path: | ||
| # with tf.compat.v1.Session(graph=tf.Graph(), config=utilities.get_tf_cpu_only_config()): | ||
| # custom_objects = keras_util.get_custom_objects(source.script_path) | ||
| # else: | ||
| # custom_objects = None | ||
| # | ||
| # with tf.compat.v1.Session(config=utilities.get_tf_cpu_only_config()).as_default() as session: | ||
| # keras.backend.set_learning_phase(0) | ||
| # model = keras.models.load_model(source.model_path, custom_objects=custom_objects, compile=False) | ||
| # | ||
| # return KerasModel(model=model, session=session) |
48 changes: 24 additions & 24 deletions
48
model_compiler/src/model_compiler/compilers/keras_model_file_to_tflite_model.py
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -1,24 +1,24 @@ | ||
| # Copyright 2019 ZTE corporation. All Rights Reserved. | ||
| # SPDX-License-Identifier: Apache-2.0 | ||
|
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| import tensorflow as tf | ||
|
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| from . import repository | ||
| from ..models.sources.keras_model_file import KerasModelFile | ||
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| from ..models.targets.tflite_model import TfLiteModel | ||
| from .. import tflite_util | ||
| from .. import keras_util | ||
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| @repository.REPOSITORY.register(source_type=KerasModelFile, target_type=TfLiteModel, config_type=tflite_util.Config) | ||
| def compile_source(source: KerasModelFile, config: tflite_util.Config) -> TfLiteModel: | ||
| if source.script_path: | ||
| custom_objects = keras_util.get_custom_objects(source.script_path) | ||
| else: | ||
| custom_objects = None | ||
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| model = tf.keras.models.load_model(filepath=source.model_path, custom_objects=custom_objects, compile=False) | ||
| converter = tf.lite.TFLiteConverter.from_keras_model(model) | ||
| tflite_model = tflite_util.get_tflite_model(converter, config) | ||
| return TfLiteModel(tflite_model, config.input_formats) | ||
| # # Copyright 2019 ZTE corporation. All Rights Reserved. | ||
| # # SPDX-License-Identifier: Apache-2.0 | ||
| # | ||
| # import tensorflow as tf | ||
| # | ||
| # from . import repository | ||
| # from ..models.sources.keras_model_file import KerasModelFile | ||
| # | ||
| # from ..models.targets.tflite_model import TfLiteModel | ||
| # from .. import tflite_util | ||
| # from .. import keras_util | ||
| # | ||
| # | ||
| # @repository.REPOSITORY.register(source_type=KerasModelFile, target_type=TfLiteModel, config_type=tflite_util.Config) | ||
| # def compile_source(source: KerasModelFile, config: tflite_util.Config) -> TfLiteModel: | ||
| # if source.script_path: | ||
| # custom_objects = keras_util.get_custom_objects(source.script_path) | ||
| # else: | ||
| # custom_objects = None | ||
| # | ||
| # model = tf.keras.models.load_model(filepath=source.model_path, custom_objects=custom_objects, compile=False) | ||
| # converter = tf.lite.TFLiteConverter.from_keras_model(model) | ||
| # tflite_model = tflite_util.get_tflite_model(converter, config) | ||
| # return TfLiteModel(tflite_model, 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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| 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)]) |
38 changes: 19 additions & 19 deletions
38
model_compiler/src/model_compiler/compilers/keras_model_to_tf_model.py
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -1,19 +1,19 @@ | ||
| # Copyright 2019 ZTE corporation. All Rights Reserved. | ||
| # SPDX-License-Identifier: Apache-2.0 | ||
|
|
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| from . import repository | ||
| from .. import utilities | ||
| from ..models.irs.keras_model import KerasModel | ||
| from ..models.irs.tf_model import Input, TensorFlowModel | ||
| from ..keras_util import Config, get_inputs, get_outputs | ||
|
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| @repository.REPOSITORY.register(source_type=KerasModel, target_type=TensorFlowModel, config_type=Config) | ||
| def compile_source(source: KerasModel, config: Config) -> TensorFlowModel: | ||
| inputs = [Input(tensor=tensor, data_format=data_format) | ||
| for tensor, data_format in get_inputs(source.model, config.input_nodes)] | ||
| outputs = get_outputs(source.model, config.output_nodes) | ||
| utilities.judge_batch_size([model_input.tensor.shape for model_input in inputs], | ||
| [model_output.shape for model_output in outputs]) | ||
|
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| return TensorFlowModel(inputs=inputs, outputs=outputs, session=source.session) | ||
| # # Copyright 2019 ZTE corporation. All Rights Reserved. | ||
| # # SPDX-License-Identifier: Apache-2.0 | ||
| # | ||
| # from . import repository | ||
| # from .. import utilities | ||
| # from ..models.irs.keras_model import KerasModel | ||
| # from ..models.irs.tf_model import Input, TensorFlowModel | ||
| # from ..keras_util import Config, get_inputs, get_outputs | ||
| # | ||
| # | ||
| # @repository.REPOSITORY.register(source_type=KerasModel, target_type=TensorFlowModel, config_type=Config) | ||
| # def compile_source(source: KerasModel, config: Config) -> TensorFlowModel: | ||
| # inputs = [Input(tensor=tensor, data_format=data_format) | ||
| # for tensor, data_format in get_inputs(source.model, config.input_nodes)] | ||
| # outputs = get_outputs(source.model, config.output_nodes) | ||
| # utilities.judge_batch_size([model_input.tensor.shape for model_input in inputs], | ||
| # [model_output.shape for model_output in outputs]) | ||
| # | ||
| # return TensorFlowModel(inputs=inputs, outputs=outputs, session=source.session) |
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