mivisionx_validation_tool.py

__author__      = "Kiriti Nagesh Gowda"
__copyright__   = "Copyright 2019, AMD MIVisionX"
__credits__     = ["Hansel Yang; Lakshmi Kumar;"]
__license__     = "MIT"
__version__     = "0.9.5"
__maintainer__  = "Kiriti Nagesh Gowda"
__email__       = "mivisionx.support@amd.com"
__status__      = "ALPHA"
__script_name__ = "MIVisionX Validation Tool"

import sys
import argparse
from PyQt4 import QtGui
from inference_control import *

# MIVisionX Classifier
if __name__ == '__main__':
	app = QtGui.QApplication(sys.argv)
	app.setQuitOnLastWindowClosed(False)
	if len(sys.argv) == 1:
		panel = InferenceControl()
		panel.show()
	else:
		parser = argparse.ArgumentParser()
		parser.add_argument('--model_format',		type=str, required=True,	help='pre-trained model format, options:caffe/onnx/nnef [required]')
		parser.add_argument('--model_name',			type=str, required=True,	help='model name                             [required]')
		parser.add_argument('--model',				type=str, required=True,	help='pre_trained model file/folder          [required]')
		parser.add_argument('--model_batch_size',	type=str, required=True,	help='n - batch size			             [required]')
		parser.add_argument('--rali_mode',			type=str, required=True,	help='rali mode (1/2/3)			             [required]')
		parser.add_argument('--model_input_dims',	type=str, required=True,	help='c,h,w - channel,height,width           [required]')
		parser.add_argument('--model_output_dims',	type=str, required=True,	help='c,h,w - channel,height,width           [required]')
		parser.add_argument('--label',				type=str, required=True,	help='labels text file                       [required]')
		parser.add_argument('--output_dir',			type=str, required=True,	help='output dir to store ADAT results       [required]')
		parser.add_argument('--image_dir',			type=str, required=True,	help='image directory for analysis           [required]')
		parser.add_argument('--image_val',			type=str, default='',		help='image list with ground truth           [optional]')
		parser.add_argument('--hierarchy',			type=str, default='',		help='AMD proprietary hierarchical file      [optional]')
		parser.add_argument('--add',				type=str, default='', 		help='input preprocessing factor [optional - default:[0,0,0]]')
		parser.add_argument('--multiply',			type=str, default='',		help='input preprocessing factor [optional - default:[1,1,1]]')
		parser.add_argument('--fp16',				type=str, default='no',		help='quantize to FP16 			[optional - default:no]')
		parser.add_argument('--replace',			type=str, default='no',		help='replace/overwrite model   [optional - default:no]')
		parser.add_argument('--verbose',			type=str, default='no',		help='verbose                   [optional - default:no]')
		parser.add_argument('--loop',				type=str, default='yes',	help='verbose                   [optional - default:yes]')
		parser.add_argument('--gui',				type=str, default='yes',	help='verbose                   [optional - default:yes]')
		parser.add_argument('--fps_file',			type=str, default='',		help='verbose                   [optional]')
		args = parser.parse_args()
		
		# get arguments
		modelFormat = args.model_format
		modelName = args.model_name
		modelLocation = args.model
		modelBatchSize = args.model_batch_size
		raliMode = (int)(args.rali_mode)
		modelInputDims = args.model_input_dims
		modelOutputDims = args.model_output_dims
		label = args.label
		outputDir = args.output_dir
		imageDir = args.image_dir
		imageVal = args.image_val
		hierarchy = args.hierarchy
		inputAdd = args.add
		inputMultiply = args.multiply
		fp16 = args.fp16
		replaceModel = args.replace
		verbose = args.verbose
		loop = args.loop
		gui = args.gui
		fps_file = args.fps_file
		container_logo = 0

		viewer = InferenceViewer(modelName, modelFormat, imageDir, modelLocation, label, hierarchy, imageVal, modelInputDims, modelOutputDims, 
                                    modelBatchSize, outputDir, inputAdd, inputMultiply, verbose, fp16, replaceModel, loop, raliMode, gui, container_logo, fps_file, parent=None)
	app.exec_()