czmodel 5.0.0

A conversion tool for TensorFlow or ONNX ANNs to CZANN

This project provides simple-to-use conversion tools to generate a CZANN file from a TensorFlow or ONNX model that resides in memory or on disk to be usable in the ZEN Intellesis module starting with ZEN blue >=3.2 and ZEN Core >3.0.

Please check the following compatibility matrix for ZEN Blue/Core and the respective version (self.version) of the CZANN Model Specification JSON Meta data file (see CZANN Model Specification below). Version compatibility is defined via the Semantic Versioning Specification (SemVer).

Model (legacy)/JSON	ZEN Blue	ZEN Core
1.1.0	>= 3.5	>= 3.4
1.0.0	>= 3.5	>= 3.4
3.1.0 (legacy)	>= 3.4	>= 3.3
3.0.0 (legacy)	>= 3.2	>= 3.1

If you encounter a version mismatch when importing a model into ZEN, please check for the correct version of this package.

Structure of repo

This repo is divided into 3 separate packages -: core, tensorflow, pytorch.

• Core - Provides base functionality, no dependency on Tensorflow or Pytorch required.
• Tensorflow - Provides Tensorflow-specific functionalities, and converters based on Tensorflow-logics.
• PyTorch - Provides PyTorch-specific functionalities, and converters based on PyTorch-logics.

Installation

The library provides a base package and extras for export functionalities that require specific dependencies -:

• pip install czmodel - This would only install base dependencies, no Tensorflow-/Pytorch-specific packages.
• pip install czmodel[tensorflow] - This would install base and Tensorflow-specific packages.
• pip install czmodel[pytorch] - This would install base and Pytorch-specific packages.

Samples

For czmodel[pytorch]:

• For single-class semantic segmentation: 

• For regression: 

For czmodel[tensorflow]:

• For single-class semantic segmentation: 

• For regression: 

System setup

The current version of this toolbox only requires a fresh Python 3.x installation. It was tested with Python 3.7 on Windows.

Model conversion

The toolbox provides a convert module that features all supported conversion strategies. It currently supports converting Keras / PyTorch models in memory or stored on disk with a corresponding metadata JSON file (see CZANN Model Specification below).

Keras / PyTorch models in memory

The toolbox also provides functionality that can be imported e.g. in the training script used to fit a Keras / PyTorch model. It provides different converters to target specific versions of the export format. Currently, there are two converters available:

• DefaultConverter: Exports a .czann file complying with the specification below.
• LegacyConverter (Only for segmentation): Exports a .czmodel file (version 3.1.0 of the legacy ANN-based segmentation models in ZEN).

The converters are accessible by running:

For Keras model:

from czmodel.tensorflow.convert import DefaultConverter, LegacyConverter

For PyTorch model:

from czmodel.pytorch.convert import DefaultConverter, LegacyConverter

Every converter provides a convert_from_model_spec function that uses a model specification object to convert a model to the corresponding export format. It accepts a tensorflow.keras.Model / torch.nn.Module that will be exported to ONNX (For Keras model, in case of failure it will be exported to SavedModel) format and at the same time wrapped into a .czann/.czmodel file that can be imported and used by Intellesis.
To provide the meta data, the toolbox provides a ModelSpec class that must be filled with the model, a ModelMetadata instance containing the information required by the specification (see Model Metadata below), and optionally a license file.

A CZANN/CZMODEL can be created from a Keras / PyTorch model with the following three steps.

1. Create a model meta data class

To export a CZANN, meta information is needed that must be provided through a ModelMetadata instance.

For segmentation:

from czmodel.core.model_metadata import ModelMetadata, ModelType

model_metadata = ModelMetadata(
    input_shape=[1024, 1024, 3],
    output_shape=[1024, 1024, 5],
    model_type=ModelType.SINGLE_CLASS_SEMANTIC_SEGMENTATION,
    classes=["class1", "class2", "class3", "class4", "class5"],
    model_name="ModelName",
    min_overlap=[90, 90]
)

For regression:

from czmodel.core.model_metadata import ModelMetadata, ModelType

model_metadata = ModelMetadata(
    input_shape=[1024, 1024, 3],
    output_shape=[1024, 1024, 3],
    model_type=ModelType.REGRESSION,
    model_name="ModelName",
    min_overlap=[90, 90]
)

For legacy CZMODEL models the legacy ModelMetadata must be used:

from czmodel.core.legacy_model_metadata import ModelMetadata as LegacyModelMetadata

model_metadata_legacy = LegacyModelMetadata(
    name="Simple_Nuclei_SegmentationModel_Legacy",
    classes=["class1", "class2"],
    pixel_types="Bgr24",
    color_handling="ConvertToMonochrome",
    border_size=90,
)

2 .Creating a model specification

The model and its corresponding metadata are now wrapped into a ModelSpec object.

from czmodel.tensorflow.model_spec import ModelSpec # for czmodel[tensorflow]
#from czmodel.pytorch.model_spec import ModelSpec   # for czmodel[pytorch]

model_spec = ModelSpec(
    model=model,
    model_metadata=model_metadata,
    license_file="C:\\some\\path\\to\\a\\LICENSE.txt"
)

The corresponding model spec for legacy models is instantiated analogously.

from czmodel.tensorflow.legacy_model_spec import ModelSpec as LegacyModelSpec  # for czmodel[tensorflow]
#from czmodel.pytorch.legacy_model_spec import ModelSpec as LegacyModelSpec    # for czmodel[pytorch]

legacy_model_spec = LegacyModelSpec(
    model=model,
    model_metadata=model_metadata_legacy,
    license_file="C:\\some\\path\\to\\a\\LICENSE.txt"
)

3. Converting the model

The actual model conversion is finally performed with the ModelSpec object and the output path and name of the CZANN.

from czmodel.tensorflow.convert import DefaultConverter as TensorflowDefaultConverter  # for czmodel[tensorflow]

TensorflowDefaultConverter().convert_from_model_spec(model_spec=model_spec, output_path='some/path', output_name='some_file_name')


from czmodel.pytorch.convert import DefaultConverter as PytorchDefaultConverter  # for czmodel[pytorch]

PytorchDefaultConverter().convert_from_model_spec(model_spec=model_spec, output_path='some/path', output_name='some_file_name', input_shape=(3, 1024, 1024))

For legacy models the interface is similar.

from czmodel.tensorflow.convert import LegacyConverter as TensorflowDefaultConverter  # for czmodel[tensorflow]

TensorflowDefaultConverter().convert_from_model_spec(model_spec=legacy_model_spec, output_path='some/path',       
                                          output_name='some_file_name')


from czmodel.pytorch.convert import LegacyConverter as PytorchLegacyConverter  # for czmodel[pytorch]

PytorchLegacyConverter().convert_from_model_spec(model_spec=legacy_model_spec, output_path='some/path',
                                          output_name='some_file_name', input_shape=(3, 1024, 1024))

Exported TensorFlow / PyTorch models

Not all TensorFlow / PyTorch models can be converted. You can convert a model exported from TensorFlow / PyTorch if the model and the provided meta data comply with the CZANN Model Specification below.

The actual conversion is triggered by either calling:

from czmodel.tensorflow.convert import DefaultConverter as TensorflowDefaultConverter  # for czmodel[tensorflow]

TensorflowDefaultConverter().convert_from_json_spec('Path to JSON file', 'Output path', 'Model Name')


from czmodel.pytorch.convert import DefaultConverter as PytorchDefaultConverter  # for czmodel[pytorch]

PytorchDefaultConverter().convert_from_json_spec('Path to JSON file', 'Output path', (3, 1024, 1024), 'Model Name')

or by using the command line interface of the savedmodel2czann script (only for Keras model):

savedmodel2ann path/to/model_spec.json output/path/ output_name --license_file path/to/license_file.txt

Adding pre- and post-processing layers (only for Keras model)

Both, convert_from_json_spec and convert_from_model_spec in the converter classes accept the following optional parameters:

• spatial_dims: Set new spatial dimensions for the new input node of the model. This parameter is expected to contain the new height and width in that order. Note: The spatial input dimensions can only be changed in ANN architectures that are invariant to the spatial dimensions of the input, e.g. FCNs.
• preprocessing: One or more pre-processing layers that will be prepended to the deployed model. A pre-processing layer must be derived from the tensorflow.keras.layers.Layer class.
• postprocessing: One or more post-processing layers that will be appended to the deployed model. A post-processing layer must be derived from the tensorflow.keras.layers.Layer class.

While ANN models are often trained on images in RGB(A) space, the ZEN infrastructure requires models inside a CZANN to expect inputs in BGR(A) color space. This toolbox offers pre-processing layers to convert the color space before passing the input to the model to be actually deployed. The following code shows how to add a BGR to RGB conversion layer to a model and set its spatial input dimensions to 512x512.

from czmodel.tensorflow.util.transforms import TransposeChannels
from czmodel.tensorflow.convert import DefaultConverter

# Define dimensions and pre-processing
spatial_dims = 512, 512  # Optional: Target spatial dimensions of the model
preprocessing = [TransposeChannels(order=(2, 1,
                                          0))]  # Optional: Pre-Processing layers to be prepended to the model. Can be a single layer, a list of layers or None.
postprocessing = None  # Optional: Post-Processing layers to be appended to the model. Can be a single layer, a list of layers or None.

# Perform conversion
DefaultConverter().convert_from_model_spec(
    model_spec=model_spec,
    output_path='some/path',
    output_name='some_file_name',
    spatial_dims=spatial_dims,
    preprocessing=preprocessing,
    postprocessing=postprocessing
)

Additionally, the toolbox offers a SigmoidToSoftmaxScores layer that can be appended through the postprocessing parameter to convert the output of a model with sigmoid output activation to the output that would be produced by an equivalent model with softmax activation.

Unpacking CZANN/CZSEG files

The czmodel library offers functionality to unpack existing CZANN/CZSEG models. For a given .czann or .czseg model it is possible to extract the underlying ANN model to a specified folder and retrieve the corresponding meta-data as instances of the meta-data classes defined in the czmodel library.

For CZANN files:

from czmodel.tensorflow.convert import DefaultConverter  # for czmodel[tensorflow]
#from czmodel.pytorch.convert import DefaultConverter    # for czmodel[pytorch]
from pathlib import Path

model_metadata, model_path = DefaultConverter().unpack_model(model_file='Path of the .czann file',
                                                             target_dir=Path('Output Path'))

For CZSEG/CZMODEL files:

from czmodel.tensorflow.convert import LegacyConverter  # for czmodel[tensorflow]
#from czmodel.pytorch.convert import DefaultConverter   # for czmodel[pytorch]
from pathlib import Path

model_metadata, model_path = LegacyConverter().unpack_model(model_file='Path of the .czseg/.czann file',
                                                            target_dir=Path('Output Path'))

CZANN Model Specification

This section specifies the requirements for an artificial neural network (ANN) model and the additionally required metadata to enable execution of the model inside the ZEN Intellesis infrastructure starting with ZEN blue >=3.2 and ZEN Core >3.0.

The model format currently allows to bundle models for semantic segmentation, instance segmentation, object detection, classification and regression and is defined as a ZIP archive with the file extension .czann containing the following files with the respective filenames:

• JSON Meta data file. (filename: model.json)
• Model in ONNX/TensorFlow SavedModel format. In case of SavedModel format the folder representing the model must be zipped to a single file. (filename: model.model)
• Optionally: A license file for the contained model. (filename: license.txt)

The meta data file must comply with the following specification:

{
    "$schema": "http://iglucentral.com/schemas/com.snowplowanalytics.self-desc/schema/jsonschema/1-0-0#",
    "$id": "http://127.0.0.1/model_format.schema.json",
    "title": "Exchange format for ANN models",
    "description": "A format that defines the meta information for exchanging ANN models. Any future versions of this specification should be evaluated through https://docs.snowplowanalytics.com/docs/pipeline-components-and-applications/iglu/igluctl-0-7-2/#lint-1 with --skip-checks numericMinMax,stringLength,optionalNull and https://www.json-buddy.com/json-schema-analyzer.htm.",
    "type": "object",
    "self": {
        "vendor": "com.zeiss",
        "name": "model-format",
        "format": "jsonschema",
        "version": "1-1-0"
    },
    "properties": {
        "Id": {
            "description": "Universally unique identifier of 128 bits for the model.",
            "type": "string"
        },
        "Type": {
            "description": "The type of problem addressed by the model.",
            "type": "string",
            "enum": ["SingleClassInstanceSegmentation", "MultiClassInstanceSegmentation", "SingleClassSemanticSegmentation", "MultiClassSemanticSegmentation", "SingleClassClassification", "MultiClassClassification", "ObjectDetection", "Regression"]
        },
        "MinOverlap": {
            "description": "The minimum overlap of tiles for each dimension in pixels. Must be divisible by two. In tiling strategies that consider tile borders instead of overlaps the minimum overlap is twice the border size.",
            "type": "array",
            "items": {
                "description": "The overlap of a single spatial dimension",
                "type": "integer",
                "minimum": 0
            },
            "minItems": 1
        },
        "Classes": {
            "description": "The class names corresponding to the last output dimension of the prediction. If the last dimension of the prediction has shape n the provided list must be of length n",
            "type": "array",
            "items": {
                "description": "A name describing a class for segmentation and classification tasks",
                "type": "string"
            },
            "minItems": 2
        },
        "ModelName": {
            "description": "The name of exported neural network model in ONNX (file) or TensorFlow SavedModel (folder) format in the same ZIP archive as the meta data file. In the case of ONNX the model must use ONNX opset version 12. In the case of TensorFlow SavedModel all operations in the model must be supported by TensorFlow 2.0.0. The model must contain exactly one input node which must comply with the input shape defined in the InputShape parameter and must have a batch dimension as its first dimension that is either 1 or undefined.",
            "type": "string"
        },
        "InputShape": {
            "description": "The shape of an input image. A typical 2D model has an input of shape [h, w, c] where h and w are the spatial dimensions and c is the number of channels. A 3D model is expected to have an input shape of [z, h, w, c] that contains an additional dimension z which represents the third spatial dimension. The batch dimension is not specified here. The input of the model must be of type float32 in the range [0..1].",
            "type": "array",
            "items": {
                "description": "The size of a single dimension",
                "type": "integer",
                "minimum": 1
            },
            "minItems": 3,
            "maxItems": 4
        },
        "OutputShape": {
            "description": "The shape of the output image. A typical 2D model has an input of shape [h, w, c] where h and w are the spatial dimensions and c is the number of classes. A 3D model is expected to have an input shape of [z, h, w, c] that contains an additional dimension z which represents the third spatial dimension. The batch dimension is not specified here. If the output of the model represents an image, it must be of type float32 in the range [0..1].",
            "type": "array",
            "items": {
                "description": "The size of a single dimension",
                "type": "integer",
                "minimum": 1
            },
            "minItems": 3,
            "maxItems": 4
        },
        "Scaling": {
            "description": "The extent of a pixel in x- and y-direction (in that order) in units of m.",
            "type": "array",
            "items": {
                "description": "The extent of a pixel in a single dimension in units of m",
                "type": "number"
            },
            "minItems": 2,
            "maxItems": 2
        }
    },
    "required": ["Id", "Type", "InputShape", "OutputShape"]
}

Json files can contain escape sequences and \-characters in paths must be escaped with \\.

The following code snippet shows an example for a valid metadata file:

For single-class semantic segmentation:

{
  "Id": "b511d295-91ff-46ca-bb60-b2e26c393809",
  "Type": "SingleClassSemanticSegmentation",
  "Classes": ["class1", "class2", "class3", "class4", "class5"],
  "InputShape": [1024, 1024, 3],
  "OutputShape": [1024, 1024, 5]
}

For regression:

{
  "Id": "064587eb-d5a1-4434-82fc-2fbc9f5871f9",
  "Type": "Regression",
  "InputShape": [1024, 1024, 3],
  "OutputShape": [1024, 1024, 3]
}