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/*
* Copyright 2019-2024 Amazon.com, Inc. or its affiliates. All Rights Reserved.
*
* Licensed under the Apache License, Version 2.0 (the "License"). You may not use this file except in compliance with
* the License. A copy of the License is located at
*
* http://aws.amazon.com/apache2.0
*
* or in the "license" file accompanying this file. This file is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR
* CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions
* and limitations under the License.
*/
package com.amazonaws.services.sagemaker.model;
import java.io.Serializable;
import javax.annotation.Generated;
import com.amazonaws.protocol.StructuredPojo;
import com.amazonaws.protocol.ProtocolMarshaller;
/**
*
* Contains information about the location of input model artifacts, the name and shape of the expected data inputs, and
* the framework in which the model was trained.
*
*
* @see AWS API
* Documentation
*/
@Generated("com.amazonaws:aws-java-sdk-code-generator")
public class InputConfig implements Serializable, Cloneable, StructuredPojo {
/**
*
* The S3 path where the model artifacts, which result from model training, are stored. This path must point to a
* single gzip compressed tar archive (.tar.gz suffix).
*
*/
private String s3Uri;
/**
*
* Specifies the name and shape of the expected data inputs for your trained model with a JSON dictionary form. The
* data inputs are Framework specific.
*
*
* -
*
* TensorFlow: You must specify the name and shape (NHWC format) of the expected data inputs using a
* dictionary format for your trained model. The dictionary formats required for the console and CLI are different.
*
*
* -
*
* Examples for one input:
*
*
* -
*
* If using the console, {"input":[1,1024,1024,3]}
*
*
* -
*
* If using the CLI, {\"input\":[1,1024,1024,3]}
*
*
*
*
* -
*
* Examples for two inputs:
*
*
* -
*
* If using the console, {"data1": [1,28,28,1], "data2":[1,28,28,1]}
*
*
* -
*
* If using the CLI, {\"data1\": [1,28,28,1], \"data2\":[1,28,28,1]}
*
*
*
*
*
*
* -
*
* KERAS: You must specify the name and shape (NCHW format) of expected data inputs using a dictionary
* format for your trained model. Note that while Keras model artifacts should be uploaded in NHWC (channel-last)
* format, DataInputConfig should be specified in NCHW (channel-first) format. The dictionary formats
* required for the console and CLI are different.
*
*
* -
*
* Examples for one input:
*
*
* -
*
* If using the console, {"input_1":[1,3,224,224]}
*
*
* -
*
* If using the CLI, {\"input_1\":[1,3,224,224]}
*
*
*
*
* -
*
* Examples for two inputs:
*
*
* -
*
* If using the console, {"input_1": [1,3,224,224], "input_2":[1,3,224,224]}
*
*
* -
*
* If using the CLI, {\"input_1\": [1,3,224,224], \"input_2\":[1,3,224,224]}
*
*
*
*
*
*
* -
*
* MXNET/ONNX/DARKNET: You must specify the name and shape (NCHW format) of the expected data inputs in
* order using a dictionary format for your trained model. The dictionary formats required for the console and CLI
* are different.
*
*
* -
*
* Examples for one input:
*
*
* -
*
* If using the console, {"data":[1,3,1024,1024]}
*
*
* -
*
* If using the CLI, {\"data\":[1,3,1024,1024]}
*
*
*
*
* -
*
* Examples for two inputs:
*
*
* -
*
* If using the console, {"var1": [1,1,28,28], "var2":[1,1,28,28]}
*
*
* -
*
* If using the CLI, {\"var1\": [1,1,28,28], \"var2\":[1,1,28,28]}
*
*
*
*
*
*
* -
*
* PyTorch: You can either specify the name and shape (NCHW format) of expected data inputs in order
* using a dictionary format for your trained model or you can specify the shape only using a list format. The
* dictionary formats required for the console and CLI are different. The list formats for the console and CLI are
* the same.
*
*
* -
*
* Examples for one input in dictionary format:
*
*
* -
*
* If using the console, {"input0":[1,3,224,224]}
*
*
* -
*
* If using the CLI, {\"input0\":[1,3,224,224]}
*
*
*
*
* -
*
* Example for one input in list format: [[1,3,224,224]]
*
*
* -
*
* Examples for two inputs in dictionary format:
*
*
* -
*
* If using the console, {"input0":[1,3,224,224], "input1":[1,3,224,224]}
*
*
* -
*
* If using the CLI, {\"input0\":[1,3,224,224], \"input1\":[1,3,224,224]}
*
*
*
*
* -
*
* Example for two inputs in list format: [[1,3,224,224], [1,3,224,224]]
*
*
*
*
* -
*
* XGBOOST: input data name and shape are not needed.
*
*
*
*
* DataInputConfig supports the following parameters for CoreML TargetDevice
* (ML Model format):
*
*
* -
*
* shape: Input shape, for example {"input_1": {"shape": [1,224,224,3]}}. In addition to
* static input shapes, CoreML converter supports Flexible input shapes:
*
*
* -
*
* Range Dimension. You can use the Range Dimension feature if you know the input shape will be within some specific
* interval in that dimension, for example: {"input_1": {"shape": ["1..10", 224, 224, 3]}}
*
*
* -
*
* Enumerated shapes. Sometimes, the models are trained to work only on a select set of inputs. You can enumerate
* all supported input shapes, for example:
* {"input_1": {"shape": [[1, 224, 224, 3], [1, 160, 160, 3]]}}
*
*
*
*
* -
*
* default_shape: Default input shape. You can set a default shape during conversion for both Range
* Dimension and Enumerated Shapes. For example
* {"input_1": {"shape": ["1..10", 224, 224, 3], "default_shape": [1, 224, 224, 3]}}
*
*
* -
*
* type: Input type. Allowed values: Image and Tensor. By default, the
* converter generates an ML Model with inputs of type Tensor (MultiArray). User can set input type to be Image.
* Image input type requires additional input parameters such as bias and scale.
*
*
* -
*
* bias: If the input type is an Image, you need to provide the bias vector.
*
*
* -
*
* scale: If the input type is an Image, you need to provide a scale factor.
*
*
*
*
* CoreML ClassifierConfig parameters can be specified using OutputConfig
* CompilerOptions. CoreML converter supports Tensorflow and PyTorch models. CoreML conversion
* examples:
*
*
* -
*
* Tensor type input:
*
*
* -
*
* "DataInputConfig": {"input_1": {"shape": [[1,224,224,3], [1,160,160,3]], "default_shape": [1,224,224,3]}}
*
*
*
*
* -
*
* Tensor type input without input name (PyTorch):
*
*
* -
*
* "DataInputConfig": [{"shape": [[1,3,224,224], [1,3,160,160]], "default_shape": [1,3,224,224]}]
*
*
*
*
* -
*
* Image type input:
*
*
* -
*
* "DataInputConfig": {"input_1": {"shape": [[1,224,224,3], [1,160,160,3]], "default_shape": [1,224,224,3], "type": "Image", "bias": [-1,-1,-1], "scale": 0.007843137255}}
*
*
* -
*
* "CompilerOptions": {"class_labels": "imagenet_labels_1000.txt"}
*
*
*
*
* -
*
* Image type input without input name (PyTorch):
*
*
* -
*
* "DataInputConfig": [{"shape": [[1,3,224,224], [1,3,160,160]], "default_shape": [1,3,224,224], "type": "Image", "bias": [-1,-1,-1], "scale": 0.007843137255}]
*
*
* -
*
* "CompilerOptions": {"class_labels": "imagenet_labels_1000.txt"}
*
*
*
*
*
*
* Depending on the model format, DataInputConfig requires the following parameters for
* ml_eia2 OutputConfig:TargetDevice.
*
*
* -
*
* For TensorFlow models saved in the SavedModel format, specify the input names from signature_def_key
* and the input model shapes for DataInputConfig. Specify the signature_def_key in OutputConfig:CompilerOptions if the model does not use TensorFlow's default signature def
* key. For example:
*
*
* -
*
* "DataInputConfig": {"inputs": [1, 224, 224, 3]}
*
*
* -
*
* "CompilerOptions": {"signature_def_key": "serving_custom"}
*
*
*
*
* -
*
* For TensorFlow models saved as a frozen graph, specify the input tensor names and shapes in
* DataInputConfig and the output tensor names for output_names in OutputConfig:CompilerOptions . For example:
*
*
* -
*
* "DataInputConfig": {"input_tensor:0": [1, 224, 224, 3]}
*
*
* -
*
* "CompilerOptions": {"output_names": ["output_tensor:0"]}
*
*
*
*
*
*/
private String dataInputConfig;
/**
*
* Identifies the framework in which the model was trained. For example: TENSORFLOW.
*
*/
private String framework;
/**
*
* Specifies the framework version to use. This API field is only supported for the MXNet, PyTorch, TensorFlow and
* TensorFlow Lite frameworks.
*
*
* For information about framework versions supported for cloud targets and edge devices, see Cloud Supported Instance Types
* and Frameworks and Edge Supported
* Frameworks.
*
*/
private String frameworkVersion;
/**
*
* The S3 path where the model artifacts, which result from model training, are stored. This path must point to a
* single gzip compressed tar archive (.tar.gz suffix).
*
*
* @param s3Uri
* The S3 path where the model artifacts, which result from model training, are stored. This path must point
* to a single gzip compressed tar archive (.tar.gz suffix).
*/
public void setS3Uri(String s3Uri) {
this.s3Uri = s3Uri;
}
/**
*
* The S3 path where the model artifacts, which result from model training, are stored. This path must point to a
* single gzip compressed tar archive (.tar.gz suffix).
*
*
* @return The S3 path where the model artifacts, which result from model training, are stored. This path must point
* to a single gzip compressed tar archive (.tar.gz suffix).
*/
public String getS3Uri() {
return this.s3Uri;
}
/**
*
* The S3 path where the model artifacts, which result from model training, are stored. This path must point to a
* single gzip compressed tar archive (.tar.gz suffix).
*
*
* @param s3Uri
* The S3 path where the model artifacts, which result from model training, are stored. This path must point
* to a single gzip compressed tar archive (.tar.gz suffix).
* @return Returns a reference to this object so that method calls can be chained together.
*/
public InputConfig withS3Uri(String s3Uri) {
setS3Uri(s3Uri);
return this;
}
/**
*
* Specifies the name and shape of the expected data inputs for your trained model with a JSON dictionary form. The
* data inputs are Framework specific.
*
*
* -
*
* TensorFlow: You must specify the name and shape (NHWC format) of the expected data inputs using a
* dictionary format for your trained model. The dictionary formats required for the console and CLI are different.
*
*
* -
*
* Examples for one input:
*
*
* -
*
* If using the console, {"input":[1,1024,1024,3]}
*
*
* -
*
* If using the CLI, {\"input\":[1,1024,1024,3]}
*
*
*
*
* -
*
* Examples for two inputs:
*
*
* -
*
* If using the console, {"data1": [1,28,28,1], "data2":[1,28,28,1]}
*
*
* -
*
* If using the CLI, {\"data1\": [1,28,28,1], \"data2\":[1,28,28,1]}
*
*
*
*
*
*
* -
*
* KERAS: You must specify the name and shape (NCHW format) of expected data inputs using a dictionary
* format for your trained model. Note that while Keras model artifacts should be uploaded in NHWC (channel-last)
* format, DataInputConfig should be specified in NCHW (channel-first) format. The dictionary formats
* required for the console and CLI are different.
*
*
* -
*
* Examples for one input:
*
*
* -
*
* If using the console, {"input_1":[1,3,224,224]}
*
*
* -
*
* If using the CLI, {\"input_1\":[1,3,224,224]}
*
*
*
*
* -
*
* Examples for two inputs:
*
*
* -
*
* If using the console, {"input_1": [1,3,224,224], "input_2":[1,3,224,224]}
*
*
* -
*
* If using the CLI, {\"input_1\": [1,3,224,224], \"input_2\":[1,3,224,224]}
*
*
*
*
*
*
* -
*
* MXNET/ONNX/DARKNET: You must specify the name and shape (NCHW format) of the expected data inputs in
* order using a dictionary format for your trained model. The dictionary formats required for the console and CLI
* are different.
*
*
* -
*
* Examples for one input:
*
*
* -
*
* If using the console, {"data":[1,3,1024,1024]}
*
*
* -
*
* If using the CLI, {\"data\":[1,3,1024,1024]}
*
*
*
*
* -
*
* Examples for two inputs:
*
*
* -
*
* If using the console, {"var1": [1,1,28,28], "var2":[1,1,28,28]}
*
*
* -
*
* If using the CLI, {\"var1\": [1,1,28,28], \"var2\":[1,1,28,28]}
*
*
*
*
*
*
* -
*
* PyTorch: You can either specify the name and shape (NCHW format) of expected data inputs in order
* using a dictionary format for your trained model or you can specify the shape only using a list format. The
* dictionary formats required for the console and CLI are different. The list formats for the console and CLI are
* the same.
*
*
* -
*
* Examples for one input in dictionary format:
*
*
* -
*
* If using the console, {"input0":[1,3,224,224]}
*
*
* -
*
* If using the CLI, {\"input0\":[1,3,224,224]}
*
*
*
*
* -
*
* Example for one input in list format: [[1,3,224,224]]
*
*
* -
*
* Examples for two inputs in dictionary format:
*
*
* -
*
* If using the console, {"input0":[1,3,224,224], "input1":[1,3,224,224]}
*
*
* -
*
* If using the CLI, {\"input0\":[1,3,224,224], \"input1\":[1,3,224,224]}
*
*
*
*
* -
*
* Example for two inputs in list format: [[1,3,224,224], [1,3,224,224]]
*
*
*
*
* -
*
* XGBOOST: input data name and shape are not needed.
*
*
*
*
* DataInputConfig supports the following parameters for CoreML TargetDevice
* (ML Model format):
*
*
* -
*
* shape: Input shape, for example {"input_1": {"shape": [1,224,224,3]}}. In addition to
* static input shapes, CoreML converter supports Flexible input shapes:
*
*
* -
*
* Range Dimension. You can use the Range Dimension feature if you know the input shape will be within some specific
* interval in that dimension, for example: {"input_1": {"shape": ["1..10", 224, 224, 3]}}
*
*
* -
*
* Enumerated shapes. Sometimes, the models are trained to work only on a select set of inputs. You can enumerate
* all supported input shapes, for example:
* {"input_1": {"shape": [[1, 224, 224, 3], [1, 160, 160, 3]]}}
*
*
*
*
* -
*
* default_shape: Default input shape. You can set a default shape during conversion for both Range
* Dimension and Enumerated Shapes. For example
* {"input_1": {"shape": ["1..10", 224, 224, 3], "default_shape": [1, 224, 224, 3]}}
*
*
* -
*
* type: Input type. Allowed values: Image and Tensor. By default, the
* converter generates an ML Model with inputs of type Tensor (MultiArray). User can set input type to be Image.
* Image input type requires additional input parameters such as bias and scale.
*
*
* -
*
* bias: If the input type is an Image, you need to provide the bias vector.
*
*
* -
*
* scale: If the input type is an Image, you need to provide a scale factor.
*
*
*
*
* CoreML ClassifierConfig parameters can be specified using OutputConfig
* CompilerOptions. CoreML converter supports Tensorflow and PyTorch models. CoreML conversion
* examples:
*
*
* -
*
* Tensor type input:
*
*
* -
*
* "DataInputConfig": {"input_1": {"shape": [[1,224,224,3], [1,160,160,3]], "default_shape": [1,224,224,3]}}
*
*
*
*
* -
*
* Tensor type input without input name (PyTorch):
*
*
* -
*
* "DataInputConfig": [{"shape": [[1,3,224,224], [1,3,160,160]], "default_shape": [1,3,224,224]}]
*
*
*
*
* -
*
* Image type input:
*
*
* -
*
* "DataInputConfig": {"input_1": {"shape": [[1,224,224,3], [1,160,160,3]], "default_shape": [1,224,224,3], "type": "Image", "bias": [-1,-1,-1], "scale": 0.007843137255}}
*
*
* -
*
* "CompilerOptions": {"class_labels": "imagenet_labels_1000.txt"}
*
*
*
*
* -
*
* Image type input without input name (PyTorch):
*
*
* -
*
* "DataInputConfig": [{"shape": [[1,3,224,224], [1,3,160,160]], "default_shape": [1,3,224,224], "type": "Image", "bias": [-1,-1,-1], "scale": 0.007843137255}]
*
*
* -
*
* "CompilerOptions": {"class_labels": "imagenet_labels_1000.txt"}
*
*
*
*
*
*
* Depending on the model format, DataInputConfig requires the following parameters for
* ml_eia2 OutputConfig:TargetDevice.
*
*
* -
*
* For TensorFlow models saved in the SavedModel format, specify the input names from signature_def_key
* and the input model shapes for DataInputConfig. Specify the signature_def_key in OutputConfig:CompilerOptions if the model does not use TensorFlow's default signature def
* key. For example:
*
*
* -
*
* "DataInputConfig": {"inputs": [1, 224, 224, 3]}
*
*
* -
*
* "CompilerOptions": {"signature_def_key": "serving_custom"}
*
*
*
*
* -
*
* For TensorFlow models saved as a frozen graph, specify the input tensor names and shapes in
* DataInputConfig and the output tensor names for output_names in OutputConfig:CompilerOptions . For example:
*
*
* -
*
* "DataInputConfig": {"input_tensor:0": [1, 224, 224, 3]}
*
*
* -
*
* "CompilerOptions": {"output_names": ["output_tensor:0"]}
*
*
*
*
*
*
* @param dataInputConfig
* Specifies the name and shape of the expected data inputs for your trained model with a JSON dictionary
* form. The data inputs are Framework specific.
*
* -
*
* TensorFlow: You must specify the name and shape (NHWC format) of the expected data inputs
* using a dictionary format for your trained model. The dictionary formats required for the console and CLI
* are different.
*
*
* -
*
* Examples for one input:
*
*
* -
*
* If using the console, {"input":[1,1024,1024,3]}
*
*
* -
*
* If using the CLI, {\"input\":[1,1024,1024,3]}
*
*
*
*
* -
*
* Examples for two inputs:
*
*
* -
*
* If using the console, {"data1": [1,28,28,1], "data2":[1,28,28,1]}
*
*
* -
*
* If using the CLI, {\"data1\": [1,28,28,1], \"data2\":[1,28,28,1]}
*
*
*
*
*
*
* -
*
* KERAS: You must specify the name and shape (NCHW format) of expected data inputs using a
* dictionary format for your trained model. Note that while Keras model artifacts should be uploaded in NHWC
* (channel-last) format, DataInputConfig should be specified in NCHW (channel-first) format.
* The dictionary formats required for the console and CLI are different.
*
*
* -
*
* Examples for one input:
*
*
* -
*
* If using the console, {"input_1":[1,3,224,224]}
*
*
* -
*
* If using the CLI, {\"input_1\":[1,3,224,224]}
*
*
*
*
* -
*
* Examples for two inputs:
*
*
* -
*
* If using the console, {"input_1": [1,3,224,224], "input_2":[1,3,224,224]}
*
*
* -
*
* If using the CLI, {\"input_1\": [1,3,224,224], \"input_2\":[1,3,224,224]}
*
*
*
*
*
*
* -
*
* MXNET/ONNX/DARKNET: You must specify the name and shape (NCHW format) of the expected data
* inputs in order using a dictionary format for your trained model. The dictionary formats required for the
* console and CLI are different.
*
*
* -
*
* Examples for one input:
*
*
* -
*
* If using the console, {"data":[1,3,1024,1024]}
*
*
* -
*
* If using the CLI, {\"data\":[1,3,1024,1024]}
*
*
*
*
* -
*
* Examples for two inputs:
*
*
* -
*
* If using the console, {"var1": [1,1,28,28], "var2":[1,1,28,28]}
*
*
* -
*
* If using the CLI, {\"var1\": [1,1,28,28], \"var2\":[1,1,28,28]}
*
*
*
*
*
*
* -
*
* PyTorch: You can either specify the name and shape (NCHW format) of expected data inputs in
* order using a dictionary format for your trained model or you can specify the shape only using a list
* format. The dictionary formats required for the console and CLI are different. The list formats for the
* console and CLI are the same.
*
*
* -
*
* Examples for one input in dictionary format:
*
*
* -
*
* If using the console, {"input0":[1,3,224,224]}
*
*
* -
*
* If using the CLI, {\"input0\":[1,3,224,224]}
*
*
*
*
* -
*
* Example for one input in list format: [[1,3,224,224]]
*
*
* -
*
* Examples for two inputs in dictionary format:
*
*
* -
*
* If using the console, {"input0":[1,3,224,224], "input1":[1,3,224,224]}
*
*
* -
*
* If using the CLI, {\"input0\":[1,3,224,224], \"input1\":[1,3,224,224]}
*
*
*
*
* -
*
* Example for two inputs in list format: [[1,3,224,224], [1,3,224,224]]
*
*
*
*
* -
*
* XGBOOST: input data name and shape are not needed.
*
*
*
*
* DataInputConfig supports the following parameters for CoreML
* TargetDevice (ML Model format):
*
*
* -
*
* shape: Input shape, for example {"input_1": {"shape": [1,224,224,3]}}. In
* addition to static input shapes, CoreML converter supports Flexible input shapes:
*
*
* -
*
* Range Dimension. You can use the Range Dimension feature if you know the input shape will be within some
* specific interval in that dimension, for example:
* {"input_1": {"shape": ["1..10", 224, 224, 3]}}
*
*
* -
*
* Enumerated shapes. Sometimes, the models are trained to work only on a select set of inputs. You can
* enumerate all supported input shapes, for example:
* {"input_1": {"shape": [[1, 224, 224, 3], [1, 160, 160, 3]]}}
*
*
*
*
* -
*
* default_shape: Default input shape. You can set a default shape during conversion for both
* Range Dimension and Enumerated Shapes. For example
* {"input_1": {"shape": ["1..10", 224, 224, 3], "default_shape": [1, 224, 224, 3]}}
*
*
* -
*
* type: Input type. Allowed values: Image and Tensor. By default, the
* converter generates an ML Model with inputs of type Tensor (MultiArray). User can set input type to be
* Image. Image input type requires additional input parameters such as bias and
* scale.
*
*
* -
*
* bias: If the input type is an Image, you need to provide the bias vector.
*
*
* -
*
* scale: If the input type is an Image, you need to provide a scale factor.
*
*
*
*
* CoreML ClassifierConfig parameters can be specified using OutputConfig
* CompilerOptions. CoreML converter supports Tensorflow and PyTorch models. CoreML conversion
* examples:
*
*
* -
*
* Tensor type input:
*
*
* -
*
* "DataInputConfig": {"input_1": {"shape": [[1,224,224,3], [1,160,160,3]], "default_shape": [1,224,224,3]}}
*
*
*
*
* -
*
* Tensor type input without input name (PyTorch):
*
*
* -
*
* "DataInputConfig": [{"shape": [[1,3,224,224], [1,3,160,160]], "default_shape": [1,3,224,224]}]
*
*
*
*
* -
*
* Image type input:
*
*
* -
*
* "DataInputConfig": {"input_1": {"shape": [[1,224,224,3], [1,160,160,3]], "default_shape": [1,224,224,3], "type": "Image", "bias": [-1,-1,-1], "scale": 0.007843137255}}
*
*
* -
*
* "CompilerOptions": {"class_labels": "imagenet_labels_1000.txt"}
*
*
*
*
* -
*
* Image type input without input name (PyTorch):
*
*
* -
*
* "DataInputConfig": [{"shape": [[1,3,224,224], [1,3,160,160]], "default_shape": [1,3,224,224], "type": "Image", "bias": [-1,-1,-1], "scale": 0.007843137255}]
*
*
* -
*
* "CompilerOptions": {"class_labels": "imagenet_labels_1000.txt"}
*
*
*
*
*
*
* Depending on the model format, DataInputConfig requires the following parameters for
* ml_eia2 OutputConfig:TargetDevice.
*
*
* -
*
* For TensorFlow models saved in the SavedModel format, specify the input names from
* signature_def_key and the input model shapes for DataInputConfig. Specify the
* signature_def_key in OutputConfig:CompilerOptions if the model does not use TensorFlow's default signature
* def key. For example:
*
*
* -
*
* "DataInputConfig": {"inputs": [1, 224, 224, 3]}
*
*
* -
*
* "CompilerOptions": {"signature_def_key": "serving_custom"}
*
*
*
*
* -
*
* For TensorFlow models saved as a frozen graph, specify the input tensor names and shapes in
* DataInputConfig and the output tensor names for output_names in OutputConfig:CompilerOptions . For example:
*
*
* -
*
* "DataInputConfig": {"input_tensor:0": [1, 224, 224, 3]}
*
*
* -
*
* "CompilerOptions": {"output_names": ["output_tensor:0"]}
*
*
*
*
*/
public void setDataInputConfig(String dataInputConfig) {
this.dataInputConfig = dataInputConfig;
}
/**
*
* Specifies the name and shape of the expected data inputs for your trained model with a JSON dictionary form. The
* data inputs are Framework specific.
*
*
* -
*
* TensorFlow: You must specify the name and shape (NHWC format) of the expected data inputs using a
* dictionary format for your trained model. The dictionary formats required for the console and CLI are different.
*
*
* -
*
* Examples for one input:
*
*
* -
*
* If using the console, {"input":[1,1024,1024,3]}
*
*
* -
*
* If using the CLI, {\"input\":[1,1024,1024,3]}
*
*
*
*
* -
*
* Examples for two inputs:
*
*
* -
*
* If using the console, {"data1": [1,28,28,1], "data2":[1,28,28,1]}
*
*
* -
*
* If using the CLI, {\"data1\": [1,28,28,1], \"data2\":[1,28,28,1]}
*
*
*
*
*
*
* -
*
* KERAS: You must specify the name and shape (NCHW format) of expected data inputs using a dictionary
* format for your trained model. Note that while Keras model artifacts should be uploaded in NHWC (channel-last)
* format, DataInputConfig should be specified in NCHW (channel-first) format. The dictionary formats
* required for the console and CLI are different.
*
*
* -
*
* Examples for one input:
*
*
* -
*
* If using the console, {"input_1":[1,3,224,224]}
*
*
* -
*
* If using the CLI, {\"input_1\":[1,3,224,224]}
*
*
*
*
* -
*
* Examples for two inputs:
*
*
* -
*
* If using the console, {"input_1": [1,3,224,224], "input_2":[1,3,224,224]}
*
*
* -
*
* If using the CLI, {\"input_1\": [1,3,224,224], \"input_2\":[1,3,224,224]}
*
*
*
*
*
*
* -
*
* MXNET/ONNX/DARKNET: You must specify the name and shape (NCHW format) of the expected data inputs in
* order using a dictionary format for your trained model. The dictionary formats required for the console and CLI
* are different.
*
*
* -
*
* Examples for one input:
*
*
* -
*
* If using the console, {"data":[1,3,1024,1024]}
*
*
* -
*
* If using the CLI, {\"data\":[1,3,1024,1024]}
*
*
*
*
* -
*
* Examples for two inputs:
*
*
* -
*
* If using the console, {"var1": [1,1,28,28], "var2":[1,1,28,28]}
*
*
* -
*
* If using the CLI, {\"var1\": [1,1,28,28], \"var2\":[1,1,28,28]}
*
*
*
*
*
*
* -
*
* PyTorch: You can either specify the name and shape (NCHW format) of expected data inputs in order
* using a dictionary format for your trained model or you can specify the shape only using a list format. The
* dictionary formats required for the console and CLI are different. The list formats for the console and CLI are
* the same.
*
*
* -
*
* Examples for one input in dictionary format:
*
*
* -
*
* If using the console, {"input0":[1,3,224,224]}
*
*
* -
*
* If using the CLI, {\"input0\":[1,3,224,224]}
*
*
*
*
* -
*
* Example for one input in list format: [[1,3,224,224]]
*
*
* -
*
* Examples for two inputs in dictionary format:
*
*
* -
*
* If using the console, {"input0":[1,3,224,224], "input1":[1,3,224,224]}
*
*
* -
*
* If using the CLI, {\"input0\":[1,3,224,224], \"input1\":[1,3,224,224]}
*
*
*
*
* -
*
* Example for two inputs in list format: [[1,3,224,224], [1,3,224,224]]
*
*
*
*
* -
*
* XGBOOST: input data name and shape are not needed.
*
*
*
*
* DataInputConfig supports the following parameters for CoreML TargetDevice
* (ML Model format):
*
*
* -
*
* shape: Input shape, for example {"input_1": {"shape": [1,224,224,3]}}. In addition to
* static input shapes, CoreML converter supports Flexible input shapes:
*
*
* -
*
* Range Dimension. You can use the Range Dimension feature if you know the input shape will be within some specific
* interval in that dimension, for example: {"input_1": {"shape": ["1..10", 224, 224, 3]}}
*
*
* -
*
* Enumerated shapes. Sometimes, the models are trained to work only on a select set of inputs. You can enumerate
* all supported input shapes, for example:
* {"input_1": {"shape": [[1, 224, 224, 3], [1, 160, 160, 3]]}}
*
*
*
*
* -
*
* default_shape: Default input shape. You can set a default shape during conversion for both Range
* Dimension and Enumerated Shapes. For example
* {"input_1": {"shape": ["1..10", 224, 224, 3], "default_shape": [1, 224, 224, 3]}}
*
*
* -
*
* type: Input type. Allowed values: Image and Tensor. By default, the
* converter generates an ML Model with inputs of type Tensor (MultiArray). User can set input type to be Image.
* Image input type requires additional input parameters such as bias and scale.
*
*
* -
*
* bias: If the input type is an Image, you need to provide the bias vector.
*
*
* -
*
* scale: If the input type is an Image, you need to provide a scale factor.
*
*
*
*
* CoreML ClassifierConfig parameters can be specified using OutputConfig
* CompilerOptions. CoreML converter supports Tensorflow and PyTorch models. CoreML conversion
* examples:
*
*
* -
*
* Tensor type input:
*
*
* -
*
* "DataInputConfig": {"input_1": {"shape": [[1,224,224,3], [1,160,160,3]], "default_shape": [1,224,224,3]}}
*
*
*
*
* -
*
* Tensor type input without input name (PyTorch):
*
*
* -
*
* "DataInputConfig": [{"shape": [[1,3,224,224], [1,3,160,160]], "default_shape": [1,3,224,224]}]
*
*
*
*
* -
*
* Image type input:
*
*
* -
*
* "DataInputConfig": {"input_1": {"shape": [[1,224,224,3], [1,160,160,3]], "default_shape": [1,224,224,3], "type": "Image", "bias": [-1,-1,-1], "scale": 0.007843137255}}
*
*
* -
*
* "CompilerOptions": {"class_labels": "imagenet_labels_1000.txt"}
*
*
*
*
* -
*
* Image type input without input name (PyTorch):
*
*
* -
*
* "DataInputConfig": [{"shape": [[1,3,224,224], [1,3,160,160]], "default_shape": [1,3,224,224], "type": "Image", "bias": [-1,-1,-1], "scale": 0.007843137255}]
*
*
* -
*
* "CompilerOptions": {"class_labels": "imagenet_labels_1000.txt"}
*
*
*
*
*
*
* Depending on the model format, DataInputConfig requires the following parameters for
* ml_eia2 OutputConfig:TargetDevice.
*
*
* -
*
* For TensorFlow models saved in the SavedModel format, specify the input names from signature_def_key
* and the input model shapes for DataInputConfig. Specify the signature_def_key in OutputConfig:CompilerOptions if the model does not use TensorFlow's default signature def
* key. For example:
*
*
* -
*
* "DataInputConfig": {"inputs": [1, 224, 224, 3]}
*
*
* -
*
* "CompilerOptions": {"signature_def_key": "serving_custom"}
*
*
*
*
* -
*
* For TensorFlow models saved as a frozen graph, specify the input tensor names and shapes in
* DataInputConfig and the output tensor names for output_names in OutputConfig:CompilerOptions . For example:
*
*
* -
*
* "DataInputConfig": {"input_tensor:0": [1, 224, 224, 3]}
*
*
* -
*
* "CompilerOptions": {"output_names": ["output_tensor:0"]}
*
*
*
*
*
*
* @return Specifies the name and shape of the expected data inputs for your trained model with a JSON dictionary
* form. The data inputs are Framework specific.
*
* -
*
* TensorFlow: You must specify the name and shape (NHWC format) of the expected data inputs
* using a dictionary format for your trained model. The dictionary formats required for the console and CLI
* are different.
*
*
* -
*
* Examples for one input:
*
*
* -
*
* If using the console, {"input":[1,1024,1024,3]}
*
*
* -
*
* If using the CLI, {\"input\":[1,1024,1024,3]}
*
*
*
*
* -
*
* Examples for two inputs:
*
*
* -
*
* If using the console, {"data1": [1,28,28,1], "data2":[1,28,28,1]}
*
*
* -
*
* If using the CLI, {\"data1\": [1,28,28,1], \"data2\":[1,28,28,1]}
*
*
*
*
*
*
* -
*
* KERAS: You must specify the name and shape (NCHW format) of expected data inputs using a
* dictionary format for your trained model. Note that while Keras model artifacts should be uploaded in
* NHWC (channel-last) format, DataInputConfig should be specified in NCHW (channel-first)
* format. The dictionary formats required for the console and CLI are different.
*
*
* -
*
* Examples for one input:
*
*
* -
*
* If using the console, {"input_1":[1,3,224,224]}
*
*
* -
*
* If using the CLI, {\"input_1\":[1,3,224,224]}
*
*
*
*
* -
*
* Examples for two inputs:
*
*
* -
*
* If using the console, {"input_1": [1,3,224,224], "input_2":[1,3,224,224]}
*
*
* -
*
* If using the CLI, {\"input_1\": [1,3,224,224], \"input_2\":[1,3,224,224]}
*
*
*
*
*
*
* -
*
* MXNET/ONNX/DARKNET: You must specify the name and shape (NCHW format) of the expected data
* inputs in order using a dictionary format for your trained model. The dictionary formats required for the
* console and CLI are different.
*
*
* -
*
* Examples for one input:
*
*
* -
*
* If using the console, {"data":[1,3,1024,1024]}
*
*
* -
*
* If using the CLI, {\"data\":[1,3,1024,1024]}
*
*
*
*
* -
*
* Examples for two inputs:
*
*
* -
*
* If using the console, {"var1": [1,1,28,28], "var2":[1,1,28,28]}
*
*
* -
*
* If using the CLI, {\"var1\": [1,1,28,28], \"var2\":[1,1,28,28]}
*
*
*
*
*
*
* -
*
* PyTorch: You can either specify the name and shape (NCHW format) of expected data inputs in
* order using a dictionary format for your trained model or you can specify the shape only using a list
* format. The dictionary formats required for the console and CLI are different. The list formats for the
* console and CLI are the same.
*
*
* -
*
* Examples for one input in dictionary format:
*
*
* -
*
* If using the console, {"input0":[1,3,224,224]}
*
*
* -
*
* If using the CLI, {\"input0\":[1,3,224,224]}
*
*
*
*
* -
*
* Example for one input in list format: [[1,3,224,224]]
*
*
* -
*
* Examples for two inputs in dictionary format:
*
*
* -
*
* If using the console, {"input0":[1,3,224,224], "input1":[1,3,224,224]}
*
*
* -
*
* If using the CLI, {\"input0\":[1,3,224,224], \"input1\":[1,3,224,224]}
*
*
*
*
* -
*
* Example for two inputs in list format: [[1,3,224,224], [1,3,224,224]]
*
*
*
*
* -
*
* XGBOOST: input data name and shape are not needed.
*
*
*
*
* DataInputConfig supports the following parameters for CoreML
* TargetDevice (ML Model format):
*
*
* -
*
* shape: Input shape, for example {"input_1": {"shape": [1,224,224,3]}}. In
* addition to static input shapes, CoreML converter supports Flexible input shapes:
*
*
* -
*
* Range Dimension. You can use the Range Dimension feature if you know the input shape will be within some
* specific interval in that dimension, for example:
* {"input_1": {"shape": ["1..10", 224, 224, 3]}}
*
*
* -
*
* Enumerated shapes. Sometimes, the models are trained to work only on a select set of inputs. You can
* enumerate all supported input shapes, for example:
* {"input_1": {"shape": [[1, 224, 224, 3], [1, 160, 160, 3]]}}
*
*
*
*
* -
*
* default_shape: Default input shape. You can set a default shape during conversion for both
* Range Dimension and Enumerated Shapes. For example
* {"input_1": {"shape": ["1..10", 224, 224, 3], "default_shape": [1, 224, 224, 3]}}
*
*
* -
*
* type: Input type. Allowed values: Image and Tensor. By default,
* the converter generates an ML Model with inputs of type Tensor (MultiArray). User can set input type to
* be Image. Image input type requires additional input parameters such as bias and
* scale.
*
*
* -
*
* bias: If the input type is an Image, you need to provide the bias vector.
*
*
* -
*
* scale: If the input type is an Image, you need to provide a scale factor.
*
*
*
*
* CoreML ClassifierConfig parameters can be specified using OutputConfig
* CompilerOptions. CoreML converter supports Tensorflow and PyTorch models. CoreML conversion
* examples:
*
*
* -
*
* Tensor type input:
*
*
* -
*
* "DataInputConfig": {"input_1": {"shape": [[1,224,224,3], [1,160,160,3]], "default_shape": [1,224,224,3]}}
*
*
*
*
* -
*
* Tensor type input without input name (PyTorch):
*
*
* -
*
* "DataInputConfig": [{"shape": [[1,3,224,224], [1,3,160,160]], "default_shape": [1,3,224,224]}]
*
*
*
*
* -
*
* Image type input:
*
*
* -
*
* "DataInputConfig": {"input_1": {"shape": [[1,224,224,3], [1,160,160,3]], "default_shape": [1,224,224,3], "type": "Image", "bias": [-1,-1,-1], "scale": 0.007843137255}}
*
*
* -
*
* "CompilerOptions": {"class_labels": "imagenet_labels_1000.txt"}
*
*
*
*
* -
*
* Image type input without input name (PyTorch):
*
*
* -
*
* "DataInputConfig": [{"shape": [[1,3,224,224], [1,3,160,160]], "default_shape": [1,3,224,224], "type": "Image", "bias": [-1,-1,-1], "scale": 0.007843137255}]
*
*
* -
*
* "CompilerOptions": {"class_labels": "imagenet_labels_1000.txt"}
*
*
*
*
*
*
* Depending on the model format, DataInputConfig requires the following parameters for
* ml_eia2 OutputConfig:TargetDevice.
*
*
* -
*
* For TensorFlow models saved in the SavedModel format, specify the input names from
* signature_def_key and the input model shapes for DataInputConfig. Specify the
* signature_def_key in OutputConfig:CompilerOptions if the model does not use TensorFlow's default signature
* def key. For example:
*
*
* -
*
* "DataInputConfig": {"inputs": [1, 224, 224, 3]}
*
*
* -
*
* "CompilerOptions": {"signature_def_key": "serving_custom"}
*
*
*
*
* -
*
* For TensorFlow models saved as a frozen graph, specify the input tensor names and shapes in
* DataInputConfig and the output tensor names for output_names in OutputConfig:CompilerOptions . For example:
*
*
* -
*
* "DataInputConfig": {"input_tensor:0": [1, 224, 224, 3]}
*
*
* -
*
* "CompilerOptions": {"output_names": ["output_tensor:0"]}
*
*
*
*
*/
public String getDataInputConfig() {
return this.dataInputConfig;
}
/**
*
* Specifies the name and shape of the expected data inputs for your trained model with a JSON dictionary form. The
* data inputs are Framework specific.
*
*
* -
*
* TensorFlow: You must specify the name and shape (NHWC format) of the expected data inputs using a
* dictionary format for your trained model. The dictionary formats required for the console and CLI are different.
*
*
* -
*
* Examples for one input:
*
*
* -
*
* If using the console, {"input":[1,1024,1024,3]}
*
*
* -
*
* If using the CLI, {\"input\":[1,1024,1024,3]}
*
*
*
*
* -
*
* Examples for two inputs:
*
*
* -
*
* If using the console, {"data1": [1,28,28,1], "data2":[1,28,28,1]}
*
*
* -
*
* If using the CLI, {\"data1\": [1,28,28,1], \"data2\":[1,28,28,1]}
*
*
*
*
*
*
* -
*
* KERAS: You must specify the name and shape (NCHW format) of expected data inputs using a dictionary
* format for your trained model. Note that while Keras model artifacts should be uploaded in NHWC (channel-last)
* format, DataInputConfig should be specified in NCHW (channel-first) format. The dictionary formats
* required for the console and CLI are different.
*
*
* -
*
* Examples for one input:
*
*
* -
*
* If using the console, {"input_1":[1,3,224,224]}
*
*
* -
*
* If using the CLI, {\"input_1\":[1,3,224,224]}
*
*
*
*
* -
*
* Examples for two inputs:
*
*
* -
*
* If using the console, {"input_1": [1,3,224,224], "input_2":[1,3,224,224]}
*
*
* -
*
* If using the CLI, {\"input_1\": [1,3,224,224], \"input_2\":[1,3,224,224]}
*
*
*
*
*
*
* -
*
* MXNET/ONNX/DARKNET: You must specify the name and shape (NCHW format) of the expected data inputs in
* order using a dictionary format for your trained model. The dictionary formats required for the console and CLI
* are different.
*
*
* -
*
* Examples for one input:
*
*
* -
*
* If using the console, {"data":[1,3,1024,1024]}
*
*
* -
*
* If using the CLI, {\"data\":[1,3,1024,1024]}
*
*
*
*
* -
*
* Examples for two inputs:
*
*
* -
*
* If using the console, {"var1": [1,1,28,28], "var2":[1,1,28,28]}
*
*
* -
*
* If using the CLI, {\"var1\": [1,1,28,28], \"var2\":[1,1,28,28]}
*
*
*
*
*
*
* -
*
* PyTorch: You can either specify the name and shape (NCHW format) of expected data inputs in order
* using a dictionary format for your trained model or you can specify the shape only using a list format. The
* dictionary formats required for the console and CLI are different. The list formats for the console and CLI are
* the same.
*
*
* -
*
* Examples for one input in dictionary format:
*
*
* -
*
* If using the console, {"input0":[1,3,224,224]}
*
*
* -
*
* If using the CLI, {\"input0\":[1,3,224,224]}
*
*
*
*
* -
*
* Example for one input in list format: [[1,3,224,224]]
*
*
* -
*
* Examples for two inputs in dictionary format:
*
*
* -
*
* If using the console, {"input0":[1,3,224,224], "input1":[1,3,224,224]}
*
*
* -
*
* If using the CLI, {\"input0\":[1,3,224,224], \"input1\":[1,3,224,224]}
*
*
*
*
* -
*
* Example for two inputs in list format: [[1,3,224,224], [1,3,224,224]]
*
*
*
*
* -
*
* XGBOOST: input data name and shape are not needed.
*
*
*
*
* DataInputConfig supports the following parameters for CoreML TargetDevice
* (ML Model format):
*
*
* -
*
* shape: Input shape, for example {"input_1": {"shape": [1,224,224,3]}}. In addition to
* static input shapes, CoreML converter supports Flexible input shapes:
*
*
* -
*
* Range Dimension. You can use the Range Dimension feature if you know the input shape will be within some specific
* interval in that dimension, for example: {"input_1": {"shape": ["1..10", 224, 224, 3]}}
*
*
* -
*
* Enumerated shapes. Sometimes, the models are trained to work only on a select set of inputs. You can enumerate
* all supported input shapes, for example:
* {"input_1": {"shape": [[1, 224, 224, 3], [1, 160, 160, 3]]}}
*
*
*
*
* -
*
* default_shape: Default input shape. You can set a default shape during conversion for both Range
* Dimension and Enumerated Shapes. For example
* {"input_1": {"shape": ["1..10", 224, 224, 3], "default_shape": [1, 224, 224, 3]}}
*
*
* -
*
* type: Input type. Allowed values: Image and Tensor. By default, the
* converter generates an ML Model with inputs of type Tensor (MultiArray). User can set input type to be Image.
* Image input type requires additional input parameters such as bias and scale.
*
*
* -
*
* bias: If the input type is an Image, you need to provide the bias vector.
*
*
* -
*
* scale: If the input type is an Image, you need to provide a scale factor.
*
*
*
*
* CoreML ClassifierConfig parameters can be specified using OutputConfig
* CompilerOptions. CoreML converter supports Tensorflow and PyTorch models. CoreML conversion
* examples:
*
*
* -
*
* Tensor type input:
*
*
* -
*
* "DataInputConfig": {"input_1": {"shape": [[1,224,224,3], [1,160,160,3]], "default_shape": [1,224,224,3]}}
*
*
*
*
* -
*
* Tensor type input without input name (PyTorch):
*
*
* -
*
* "DataInputConfig": [{"shape": [[1,3,224,224], [1,3,160,160]], "default_shape": [1,3,224,224]}]
*
*
*
*
* -
*
* Image type input:
*
*
* -
*
* "DataInputConfig": {"input_1": {"shape": [[1,224,224,3], [1,160,160,3]], "default_shape": [1,224,224,3], "type": "Image", "bias": [-1,-1,-1], "scale": 0.007843137255}}
*
*
* -
*
* "CompilerOptions": {"class_labels": "imagenet_labels_1000.txt"}
*
*
*
*
* -
*
* Image type input without input name (PyTorch):
*
*
* -
*
* "DataInputConfig": [{"shape": [[1,3,224,224], [1,3,160,160]], "default_shape": [1,3,224,224], "type": "Image", "bias": [-1,-1,-1], "scale": 0.007843137255}]
*
*
* -
*
* "CompilerOptions": {"class_labels": "imagenet_labels_1000.txt"}
*
*
*
*
*
*
* Depending on the model format, DataInputConfig requires the following parameters for
* ml_eia2 OutputConfig:TargetDevice.
*
*
* -
*
* For TensorFlow models saved in the SavedModel format, specify the input names from signature_def_key
* and the input model shapes for DataInputConfig. Specify the signature_def_key in OutputConfig:CompilerOptions if the model does not use TensorFlow's default signature def
* key. For example:
*
*
* -
*
* "DataInputConfig": {"inputs": [1, 224, 224, 3]}
*
*
* -
*
* "CompilerOptions": {"signature_def_key": "serving_custom"}
*
*
*
*
* -
*
* For TensorFlow models saved as a frozen graph, specify the input tensor names and shapes in
* DataInputConfig and the output tensor names for output_names in OutputConfig:CompilerOptions . For example:
*
*
* -
*
* "DataInputConfig": {"input_tensor:0": [1, 224, 224, 3]}
*
*
* -
*
* "CompilerOptions": {"output_names": ["output_tensor:0"]}
*
*
*
*
*
*
* @param dataInputConfig
* Specifies the name and shape of the expected data inputs for your trained model with a JSON dictionary
* form. The data inputs are Framework specific.
*
* -
*
* TensorFlow: You must specify the name and shape (NHWC format) of the expected data inputs
* using a dictionary format for your trained model. The dictionary formats required for the console and CLI
* are different.
*
*
* -
*
* Examples for one input:
*
*
* -
*
* If using the console, {"input":[1,1024,1024,3]}
*
*
* -
*
* If using the CLI, {\"input\":[1,1024,1024,3]}
*
*
*
*
* -
*
* Examples for two inputs:
*
*
* -
*
* If using the console, {"data1": [1,28,28,1], "data2":[1,28,28,1]}
*
*
* -
*
* If using the CLI, {\"data1\": [1,28,28,1], \"data2\":[1,28,28,1]}
*
*
*
*
*
*
* -
*
* KERAS: You must specify the name and shape (NCHW format) of expected data inputs using a
* dictionary format for your trained model. Note that while Keras model artifacts should be uploaded in NHWC
* (channel-last) format, DataInputConfig should be specified in NCHW (channel-first) format.
* The dictionary formats required for the console and CLI are different.
*
*
* -
*
* Examples for one input:
*
*
* -
*
* If using the console, {"input_1":[1,3,224,224]}
*
*
* -
*
* If using the CLI, {\"input_1\":[1,3,224,224]}
*
*
*
*
* -
*
* Examples for two inputs:
*
*
* -
*
* If using the console, {"input_1": [1,3,224,224], "input_2":[1,3,224,224]}
*
*
* -
*
* If using the CLI, {\"input_1\": [1,3,224,224], \"input_2\":[1,3,224,224]}
*
*
*
*
*
*
* -
*
* MXNET/ONNX/DARKNET: You must specify the name and shape (NCHW format) of the expected data
* inputs in order using a dictionary format for your trained model. The dictionary formats required for the
* console and CLI are different.
*
*
* -
*
* Examples for one input:
*
*
* -
*
* If using the console, {"data":[1,3,1024,1024]}
*
*
* -
*
* If using the CLI, {\"data\":[1,3,1024,1024]}
*
*
*
*
* -
*
* Examples for two inputs:
*
*
* -
*
* If using the console, {"var1": [1,1,28,28], "var2":[1,1,28,28]}
*
*
* -
*
* If using the CLI, {\"var1\": [1,1,28,28], \"var2\":[1,1,28,28]}
*
*
*
*
*
*
* -
*
* PyTorch: You can either specify the name and shape (NCHW format) of expected data inputs in
* order using a dictionary format for your trained model or you can specify the shape only using a list
* format. The dictionary formats required for the console and CLI are different. The list formats for the
* console and CLI are the same.
*
*
* -
*
* Examples for one input in dictionary format:
*
*
* -
*
* If using the console, {"input0":[1,3,224,224]}
*
*
* -
*
* If using the CLI, {\"input0\":[1,3,224,224]}
*
*
*
*
* -
*
* Example for one input in list format: [[1,3,224,224]]
*
*
* -
*
* Examples for two inputs in dictionary format:
*
*
* -
*
* If using the console, {"input0":[1,3,224,224], "input1":[1,3,224,224]}
*
*
* -
*
* If using the CLI, {\"input0\":[1,3,224,224], \"input1\":[1,3,224,224]}
*
*
*
*
* -
*
* Example for two inputs in list format: [[1,3,224,224], [1,3,224,224]]
*
*
*
*
* -
*
* XGBOOST: input data name and shape are not needed.
*
*
*
*
* DataInputConfig supports the following parameters for CoreML
* TargetDevice (ML Model format):
*
*
* -
*
* shape: Input shape, for example {"input_1": {"shape": [1,224,224,3]}}. In
* addition to static input shapes, CoreML converter supports Flexible input shapes:
*
*
* -
*
* Range Dimension. You can use the Range Dimension feature if you know the input shape will be within some
* specific interval in that dimension, for example:
* {"input_1": {"shape": ["1..10", 224, 224, 3]}}
*
*
* -
*
* Enumerated shapes. Sometimes, the models are trained to work only on a select set of inputs. You can
* enumerate all supported input shapes, for example:
* {"input_1": {"shape": [[1, 224, 224, 3], [1, 160, 160, 3]]}}
*
*
*
*
* -
*
* default_shape: Default input shape. You can set a default shape during conversion for both
* Range Dimension and Enumerated Shapes. For example
* {"input_1": {"shape": ["1..10", 224, 224, 3], "default_shape": [1, 224, 224, 3]}}
*
*
* -
*
* type: Input type. Allowed values: Image and Tensor. By default, the
* converter generates an ML Model with inputs of type Tensor (MultiArray). User can set input type to be
* Image. Image input type requires additional input parameters such as bias and
* scale.
*
*
* -
*
* bias: If the input type is an Image, you need to provide the bias vector.
*
*
* -
*
* scale: If the input type is an Image, you need to provide a scale factor.
*
*
*
*
* CoreML ClassifierConfig parameters can be specified using OutputConfig
* CompilerOptions. CoreML converter supports Tensorflow and PyTorch models. CoreML conversion
* examples:
*
*
* -
*
* Tensor type input:
*
*
* -
*
* "DataInputConfig": {"input_1": {"shape": [[1,224,224,3], [1,160,160,3]], "default_shape": [1,224,224,3]}}
*
*
*
*
* -
*
* Tensor type input without input name (PyTorch):
*
*
* -
*
* "DataInputConfig": [{"shape": [[1,3,224,224], [1,3,160,160]], "default_shape": [1,3,224,224]}]
*
*
*
*
* -
*
* Image type input:
*
*
* -
*
* "DataInputConfig": {"input_1": {"shape": [[1,224,224,3], [1,160,160,3]], "default_shape": [1,224,224,3], "type": "Image", "bias": [-1,-1,-1], "scale": 0.007843137255}}
*
*
* -
*
* "CompilerOptions": {"class_labels": "imagenet_labels_1000.txt"}
*
*
*
*
* -
*
* Image type input without input name (PyTorch):
*
*
* -
*
* "DataInputConfig": [{"shape": [[1,3,224,224], [1,3,160,160]], "default_shape": [1,3,224,224], "type": "Image", "bias": [-1,-1,-1], "scale": 0.007843137255}]
*
*
* -
*
* "CompilerOptions": {"class_labels": "imagenet_labels_1000.txt"}
*
*
*
*
*
*
* Depending on the model format, DataInputConfig requires the following parameters for
* ml_eia2 OutputConfig:TargetDevice.
*
*
* -
*
* For TensorFlow models saved in the SavedModel format, specify the input names from
* signature_def_key and the input model shapes for DataInputConfig. Specify the
* signature_def_key in OutputConfig:CompilerOptions if the model does not use TensorFlow's default signature
* def key. For example:
*
*
* -
*
* "DataInputConfig": {"inputs": [1, 224, 224, 3]}
*
*
* -
*
* "CompilerOptions": {"signature_def_key": "serving_custom"}
*
*
*
*
* -
*
* For TensorFlow models saved as a frozen graph, specify the input tensor names and shapes in
* DataInputConfig and the output tensor names for output_names in OutputConfig:CompilerOptions . For example:
*
*
* -
*
* "DataInputConfig": {"input_tensor:0": [1, 224, 224, 3]}
*
*
* -
*
* "CompilerOptions": {"output_names": ["output_tensor:0"]}
*
*
*
*
* @return Returns a reference to this object so that method calls can be chained together.
*/
public InputConfig withDataInputConfig(String dataInputConfig) {
setDataInputConfig(dataInputConfig);
return this;
}
/**
*
* Identifies the framework in which the model was trained. For example: TENSORFLOW.
*
*
* @param framework
* Identifies the framework in which the model was trained. For example: TENSORFLOW.
* @see Framework
*/
public void setFramework(String framework) {
this.framework = framework;
}
/**
*
* Identifies the framework in which the model was trained. For example: TENSORFLOW.
*
*
* @return Identifies the framework in which the model was trained. For example: TENSORFLOW.
* @see Framework
*/
public String getFramework() {
return this.framework;
}
/**
*
* Identifies the framework in which the model was trained. For example: TENSORFLOW.
*
*
* @param framework
* Identifies the framework in which the model was trained. For example: TENSORFLOW.
* @return Returns a reference to this object so that method calls can be chained together.
* @see Framework
*/
public InputConfig withFramework(String framework) {
setFramework(framework);
return this;
}
/**
*
* Identifies the framework in which the model was trained. For example: TENSORFLOW.
*
*
* @param framework
* Identifies the framework in which the model was trained. For example: TENSORFLOW.
* @return Returns a reference to this object so that method calls can be chained together.
* @see Framework
*/
public InputConfig withFramework(Framework framework) {
this.framework = framework.toString();
return this;
}
/**
*
* Specifies the framework version to use. This API field is only supported for the MXNet, PyTorch, TensorFlow and
* TensorFlow Lite frameworks.
*
*
* For information about framework versions supported for cloud targets and edge devices, see Cloud Supported Instance Types
* and Frameworks and Edge Supported
* Frameworks.
*
*
* @param frameworkVersion
* Specifies the framework version to use. This API field is only supported for the MXNet, PyTorch,
* TensorFlow and TensorFlow Lite frameworks.
*
* For information about framework versions supported for cloud targets and edge devices, see Cloud Supported Instance
* Types and Frameworks and Edge
* Supported Frameworks.
*/
public void setFrameworkVersion(String frameworkVersion) {
this.frameworkVersion = frameworkVersion;
}
/**
*
* Specifies the framework version to use. This API field is only supported for the MXNet, PyTorch, TensorFlow and
* TensorFlow Lite frameworks.
*
*
* For information about framework versions supported for cloud targets and edge devices, see Cloud Supported Instance Types
* and Frameworks and Edge Supported
* Frameworks.
*
*
* @return Specifies the framework version to use. This API field is only supported for the MXNet, PyTorch,
* TensorFlow and TensorFlow Lite frameworks.
*
* For information about framework versions supported for cloud targets and edge devices, see Cloud Supported Instance
* Types and Frameworks and Edge
* Supported Frameworks.
*/
public String getFrameworkVersion() {
return this.frameworkVersion;
}
/**
*
* Specifies the framework version to use. This API field is only supported for the MXNet, PyTorch, TensorFlow and
* TensorFlow Lite frameworks.
*
*
* For information about framework versions supported for cloud targets and edge devices, see Cloud Supported Instance Types
* and Frameworks and Edge Supported
* Frameworks.
*
*
* @param frameworkVersion
* Specifies the framework version to use. This API field is only supported for the MXNet, PyTorch,
* TensorFlow and TensorFlow Lite frameworks.
*
* For information about framework versions supported for cloud targets and edge devices, see Cloud Supported Instance
* Types and Frameworks and Edge
* Supported Frameworks.
* @return Returns a reference to this object so that method calls can be chained together.
*/
public InputConfig withFrameworkVersion(String frameworkVersion) {
setFrameworkVersion(frameworkVersion);
return this;
}
/**
* Returns a string representation of this object. This is useful for testing and debugging. Sensitive data will be
* redacted from this string using a placeholder value.
*
* @return A string representation of this object.
*
* @see java.lang.Object#toString()
*/
@Override
public String toString() {
StringBuilder sb = new StringBuilder();
sb.append("{");
if (getS3Uri() != null)
sb.append("S3Uri: ").append(getS3Uri()).append(",");
if (getDataInputConfig() != null)
sb.append("DataInputConfig: ").append(getDataInputConfig()).append(",");
if (getFramework() != null)
sb.append("Framework: ").append(getFramework()).append(",");
if (getFrameworkVersion() != null)
sb.append("FrameworkVersion: ").append(getFrameworkVersion());
sb.append("}");
return sb.toString();
}
@Override
public boolean equals(Object obj) {
if (this == obj)
return true;
if (obj == null)
return false;
if (obj instanceof InputConfig == false)
return false;
InputConfig other = (InputConfig) obj;
if (other.getS3Uri() == null ^ this.getS3Uri() == null)
return false;
if (other.getS3Uri() != null && other.getS3Uri().equals(this.getS3Uri()) == false)
return false;
if (other.getDataInputConfig() == null ^ this.getDataInputConfig() == null)
return false;
if (other.getDataInputConfig() != null && other.getDataInputConfig().equals(this.getDataInputConfig()) == false)
return false;
if (other.getFramework() == null ^ this.getFramework() == null)
return false;
if (other.getFramework() != null && other.getFramework().equals(this.getFramework()) == false)
return false;
if (other.getFrameworkVersion() == null ^ this.getFrameworkVersion() == null)
return false;
if (other.getFrameworkVersion() != null && other.getFrameworkVersion().equals(this.getFrameworkVersion()) == false)
return false;
return true;
}
@Override
public int hashCode() {
final int prime = 31;
int hashCode = 1;
hashCode = prime * hashCode + ((getS3Uri() == null) ? 0 : getS3Uri().hashCode());
hashCode = prime * hashCode + ((getDataInputConfig() == null) ? 0 : getDataInputConfig().hashCode());
hashCode = prime * hashCode + ((getFramework() == null) ? 0 : getFramework().hashCode());
hashCode = prime * hashCode + ((getFrameworkVersion() == null) ? 0 : getFrameworkVersion().hashCode());
return hashCode;
}
@Override
public InputConfig clone() {
try {
return (InputConfig) super.clone();
} catch (CloneNotSupportedException e) {
throw new IllegalStateException("Got a CloneNotSupportedException from Object.clone() " + "even though we're Cloneable!", e);
}
}
@com.amazonaws.annotation.SdkInternalApi
@Override
public void marshall(ProtocolMarshaller protocolMarshaller) {
com.amazonaws.services.sagemaker.model.transform.InputConfigMarshaller.getInstance().marshall(this, protocolMarshaller);
}
}