def main():
args = doParsing()
print(args)
# Read training configuration (config file is in common for dataset creation and training hyperparameters)
configParams = ConfigParams(args.configFile)
# Select train device
trainDevice = selectTrainDevice(args.useGpu)
# Load DataProvider
dataProvider = DatasetTFReader(
datasetDir=args.datasetDir,
datasetMetadata=DatasetMetadata().initFromJson(os.path.join(args.datasetDir, "metadata.json")),
configParams=configParams)
# Load base model graph (fine tuning training)
baseTFModel = None
try:
baseTFModel = TensorflowModel(os.path.join(args.baseModelDir, "graph.pb"))
except Exception as e:
print("Warning: no base model provided or impossible to read,"
" this works only for custom model created from scratch")
baseTFModel = TensorflowModel()
# Append classifier for fine tuning training
trainingModel = ModelFactory.create(config=configParams, tfmodel=baseTFModel,
dataProvider=dataProvider, trainDevice=trainDevice)
# Run training
trainProcess = TrainProcess(config=configParams, trainingModel=trainingModel,
dataProvider=dataProvider, outputDir=args.checkpointOutputDir,
tensorboardDir=args.tensorboardDir)
trainProcess.runTrain()
# Freeze graph (graphdef plus parameters),
# this includes in the graph only the layers needed to provide the output_node_names
freeze_graph(input_graph=args.checkpointOutputDir + "/model_graph.pb", input_saver="", input_binary=True,
input_checkpoint=args.checkpointOutputDir + "/model", output_node_names=configParams.outputName,
restore_op_name="save/restore_all", filename_tensor_name="save/Const:0",
output_graph=args.modelOutputDir + "/graph.pb", clear_devices=True, initializer_nodes="")
def create_product():
data = request.json
factory = ModelFactory()
product_id = factory.create(Product,
seller=data['seller'],
name=data['name'])
for color in data['option']['color']:
for size in data['option']['size']:
factory.create(ProductOption,
product_id=product_id,
color=color,
size=size)
for seq, image in enumerate(data['images']):
factory.create(ProductImage,
product_id=product_id,
seq=int(seq),
path=image['path'],
url=image['url'])
factory.create(ProductPrice, product_id=product_id, price=data['price'])
return jsonify(status=201, message='Created')