"""
Read PMML files and make predictions

Example usage:
    python pmml.py predict \
        --model=examples/deepnetwork/VGG16/model.pmml \
        --input=test/assets/cat.jpg

    python pmml.py runserver \
        --model=examples/deepnetwork/VGG16/model.pmml

    python pmml.py build_torch_examples
"""
import json
import argparse
from lxml import etree
from scipy.misc import imread
from models.gpr.parser import GaussianProcessParser
from models.deepnetwork.core.intermediate import DeepNetwork
from models.deepnetwork.core.utils import strip_namespace
from models.deepnetwork.generate_models import build_models



def load_pmml(filename):
    """
    Load a PMML file
    The model type is determined read from the PMML file
    """
    tree = etree.iterparse(filename)
    root = strip_namespace(tree).root

    config = {}
    config["filename"] = filename

    header = root.find("Header")
    if "description" in header.attrib:
        config["description"] = header.attrib["description"]
    if "copyright" in header.attrib:
        config["copyright"] = header.attrib["copyright"]

    dnn = root.find("DeepNetwork")
    gpr = root.find("GaussianProcessModel")

    if dnn is not None:
        model = DeepNetwork(**config)
        model.load_pmml(root)
    elif gpr is not None:
        parser = GaussianProcessParser()
        model = parser.parse(filename)
    else:
        raise ValueError("Could not find a valud model in %s"%filename)
    return model



def build_examples():
    """
    Automatically build examples from publically available models
    """
    build_models([
        "VGG_16",
        "VGG_19",
        "RESNET_50",
        "MOBILENET",
        #"INCEPTION_V3",
        #"INCEPTION_RESNET",
        #"DENSENET_121",
        #"DENSENET_169",
        #"DENSENET_201"])
        ])



def predict(model, input_file):
    """
    Return a prediction from a model
    The input_file is either an image or a json file describing the input
    """
    if input_file.endswith(".json"):
        with open(input_file,"w") as fd:
            data = json.loads(input_file)
    else:
        data = imread(input_file)
    result = model.predict(data)
    print("Model predicted class: %s"%result)
    return result



parser = argparse.ArgumentParser(description='Main entry point for PMML package.')
parser.add_argument('operation', type=str, help='One of [predict, runserver, or build_examples]')
parser.add_argument('--model', help='The PMML file to load')
parser.add_argument('--input', default='', help='The path to the input file for testing')
parser.add_argument('--runserver', default=False, help='Run a server')



if __name__=="__main__":
    args = parser.parse_args()
    if args.operation.lower()=="build_examples":
        build_examples()

    elif args.operation.lower()=="predict":
        model = load_pmml(args.model)
        prediction = predict(model, args.input)

    elif args.operation.lower()=="runserver":
        model = load_pmml(args.model)

    else:
        raise ValueError("Unknown operation %s"%args.operation)

21cc384b2201d9ea5dc301853a59e26fb2f2de0b
<mask token>


def load_pmml(filename):
    """
    Load a PMML file
    The model type is determined read from the PMML file
    """
    tree = etree.iterparse(filename)
    root = strip_namespace(tree).root
    config = {}
    config['filename'] = filename
    header = root.find('Header')
    if 'description' in header.attrib:
        config['description'] = header.attrib['description']
    if 'copyright' in header.attrib:
        config['copyright'] = header.attrib['copyright']
    dnn = root.find('DeepNetwork')
    gpr = root.find('GaussianProcessModel')
    if dnn is not None:
        model = DeepNetwork(**config)
        model.load_pmml(root)
    elif gpr is not None:
        parser = GaussianProcessParser()
        model = parser.parse(filename)
    else:
        raise ValueError('Could not find a valud model in %s' % filename)
    return model


def build_examples():
    """
    Automatically build examples from publically available models
    """
    build_models(['VGG_16', 'VGG_19', 'RESNET_50', 'MOBILENET'])


def predict(model, input_file):
    """
    Return a prediction from a model
    The input_file is either an image or a json file describing the input
    """
    if input_file.endswith('.json'):
        with open(input_file, 'w') as fd:
            data = json.loads(input_file)
    else:
        data = imread(input_file)
    result = model.predict(data)
    print('Model predicted class: %s' % result)
    return result


<mask token>

<mask token>


def load_pmml(filename):
    """
    Load a PMML file
    The model type is determined read from the PMML file
    """
    tree = etree.iterparse(filename)
    root = strip_namespace(tree).root
    config = {}
    config['filename'] = filename
    header = root.find('Header')
    if 'description' in header.attrib:
        config['description'] = header.attrib['description']
    if 'copyright' in header.attrib:
        config['copyright'] = header.attrib['copyright']
    dnn = root.find('DeepNetwork')
    gpr = root.find('GaussianProcessModel')
    if dnn is not None:
        model = DeepNetwork(**config)
        model.load_pmml(root)
    elif gpr is not None:
        parser = GaussianProcessParser()
        model = parser.parse(filename)
    else:
        raise ValueError('Could not find a valud model in %s' % filename)
    return model


def build_examples():
    """
    Automatically build examples from publically available models
    """
    build_models(['VGG_16', 'VGG_19', 'RESNET_50', 'MOBILENET'])


def predict(model, input_file):
    """
    Return a prediction from a model
    The input_file is either an image or a json file describing the input
    """
    if input_file.endswith('.json'):
        with open(input_file, 'w') as fd:
            data = json.loads(input_file)
    else:
        data = imread(input_file)
    result = model.predict(data)
    print('Model predicted class: %s' % result)
    return result


<mask token>
parser.add_argument('operation', type=str, help=
    'One of [predict, runserver, or build_examples]')
parser.add_argument('--model', help='The PMML file to load')
parser.add_argument('--input', default='', help=
    'The path to the input file for testing')
parser.add_argument('--runserver', default=False, help='Run a server')
if __name__ == '__main__':
    args = parser.parse_args()
    if args.operation.lower() == 'build_examples':
        build_examples()
    elif args.operation.lower() == 'predict':
        model = load_pmml(args.model)
        prediction = predict(model, args.input)
    elif args.operation.lower() == 'runserver':
        model = load_pmml(args.model)
    else:
        raise ValueError('Unknown operation %s' % args.operation)

<mask token>


def load_pmml(filename):
    """
    Load a PMML file
    The model type is determined read from the PMML file
    """
    tree = etree.iterparse(filename)
    root = strip_namespace(tree).root
    config = {}
    config['filename'] = filename
    header = root.find('Header')
    if 'description' in header.attrib:
        config['description'] = header.attrib['description']
    if 'copyright' in header.attrib:
        config['copyright'] = header.attrib['copyright']
    dnn = root.find('DeepNetwork')
    gpr = root.find('GaussianProcessModel')
    if dnn is not None:
        model = DeepNetwork(**config)
        model.load_pmml(root)
    elif gpr is not None:
        parser = GaussianProcessParser()
        model = parser.parse(filename)
    else:
        raise ValueError('Could not find a valud model in %s' % filename)
    return model


def build_examples():
    """
    Automatically build examples from publically available models
    """
    build_models(['VGG_16', 'VGG_19', 'RESNET_50', 'MOBILENET'])


def predict(model, input_file):
    """
    Return a prediction from a model
    The input_file is either an image or a json file describing the input
    """
    if input_file.endswith('.json'):
        with open(input_file, 'w') as fd:
            data = json.loads(input_file)
    else:
        data = imread(input_file)
    result = model.predict(data)
    print('Model predicted class: %s' % result)
    return result


parser = argparse.ArgumentParser(description=
    'Main entry point for PMML package.')
parser.add_argument('operation', type=str, help=
    'One of [predict, runserver, or build_examples]')
parser.add_argument('--model', help='The PMML file to load')
parser.add_argument('--input', default='', help=
    'The path to the input file for testing')
parser.add_argument('--runserver', default=False, help='Run a server')
if __name__ == '__main__':
    args = parser.parse_args()
    if args.operation.lower() == 'build_examples':
        build_examples()
    elif args.operation.lower() == 'predict':
        model = load_pmml(args.model)
        prediction = predict(model, args.input)
    elif args.operation.lower() == 'runserver':
        model = load_pmml(args.model)
    else:
        raise ValueError('Unknown operation %s' % args.operation)

<mask token>
import json
import argparse
from lxml import etree
from scipy.misc import imread
from models.gpr.parser import GaussianProcessParser
from models.deepnetwork.core.intermediate import DeepNetwork
from models.deepnetwork.core.utils import strip_namespace
from models.deepnetwork.generate_models import build_models


def load_pmml(filename):
    """
    Load a PMML file
    The model type is determined read from the PMML file
    """
    tree = etree.iterparse(filename)
    root = strip_namespace(tree).root
    config = {}
    config['filename'] = filename
    header = root.find('Header')
    if 'description' in header.attrib:
        config['description'] = header.attrib['description']
    if 'copyright' in header.attrib:
        config['copyright'] = header.attrib['copyright']
    dnn = root.find('DeepNetwork')
    gpr = root.find('GaussianProcessModel')
    if dnn is not None:
        model = DeepNetwork(**config)
        model.load_pmml(root)
    elif gpr is not None:
        parser = GaussianProcessParser()
        model = parser.parse(filename)
    else:
        raise ValueError('Could not find a valud model in %s' % filename)
    return model


def build_examples():
    """
    Automatically build examples from publically available models
    """
    build_models(['VGG_16', 'VGG_19', 'RESNET_50', 'MOBILENET'])


def predict(model, input_file):
    """
    Return a prediction from a model
    The input_file is either an image or a json file describing the input
    """
    if input_file.endswith('.json'):
        with open(input_file, 'w') as fd:
            data = json.loads(input_file)
    else:
        data = imread(input_file)
    result = model.predict(data)
    print('Model predicted class: %s' % result)
    return result


parser = argparse.ArgumentParser(description=
    'Main entry point for PMML package.')
parser.add_argument('operation', type=str, help=
    'One of [predict, runserver, or build_examples]')
parser.add_argument('--model', help='The PMML file to load')
parser.add_argument('--input', default='', help=
    'The path to the input file for testing')
parser.add_argument('--runserver', default=False, help='Run a server')
if __name__ == '__main__':
    args = parser.parse_args()
    if args.operation.lower() == 'build_examples':
        build_examples()
    elif args.operation.lower() == 'predict':
        model = load_pmml(args.model)
        prediction = predict(model, args.input)
    elif args.operation.lower() == 'runserver':
        model = load_pmml(args.model)
    else:
        raise ValueError('Unknown operation %s' % args.operation)

"""
Read PMML files and make predictions

Example usage:
    python pmml.py predict \
        --model=examples/deepnetwork/VGG16/model.pmml \
        --input=test/assets/cat.jpg

    python pmml.py runserver \
        --model=examples/deepnetwork/VGG16/model.pmml

    python pmml.py build_torch_examples
"""
import json
import argparse
from lxml import etree
from scipy.misc import imread
from models.gpr.parser import GaussianProcessParser
from models.deepnetwork.core.intermediate import DeepNetwork
from models.deepnetwork.core.utils import strip_namespace
from models.deepnetwork.generate_models import build_models



def load_pmml(filename):
    """
    Load a PMML file
    The model type is determined read from the PMML file
    """
    tree = etree.iterparse(filename)
    root = strip_namespace(tree).root

    config = {}
    config["filename"] = filename

    header = root.find("Header")
    if "description" in header.attrib:
        config["description"] = header.attrib["description"]
    if "copyright" in header.attrib:
        config["copyright"] = header.attrib["copyright"]

    dnn = root.find("DeepNetwork")
    gpr = root.find("GaussianProcessModel")

    if dnn is not None:
        model = DeepNetwork(**config)
        model.load_pmml(root)
    elif gpr is not None:
        parser = GaussianProcessParser()
        model = parser.parse(filename)
    else:
        raise ValueError("Could not find a valud model in %s"%filename)
    return model



def build_examples():
    """
    Automatically build examples from publically available models
    """
    build_models([
        "VGG_16",
        "VGG_19",
        "RESNET_50",
        "MOBILENET",
        #"INCEPTION_V3",
        #"INCEPTION_RESNET",
        #"DENSENET_121",
        #"DENSENET_169",
        #"DENSENET_201"])
        ])



def predict(model, input_file):
    """
    Return a prediction from a model
    The input_file is either an image or a json file describing the input
    """
    if input_file.endswith(".json"):
        with open(input_file,"w") as fd:
            data = json.loads(input_file)
    else:
        data = imread(input_file)
    result = model.predict(data)
    print("Model predicted class: %s"%result)
    return result



parser = argparse.ArgumentParser(description='Main entry point for PMML package.')
parser.add_argument('operation', type=str, help='One of [predict, runserver, or build_examples]')
parser.add_argument('--model', help='The PMML file to load')
parser.add_argument('--input', default='', help='The path to the input file for testing')
parser.add_argument('--runserver', default=False, help='Run a server')



if __name__=="__main__":
    args = parser.parse_args()
    if args.operation.lower()=="build_examples":
        build_examples()

    elif args.operation.lower()=="predict":
        model = load_pmml(args.model)
        prediction = predict(model, args.input)

    elif args.operation.lower()=="runserver":
        model = load_pmml(args.model)

    else:
        raise ValueError("Unknown operation %s"%args.operation)
