def input_options(parser):
parser.add_argument('input_files',
nargs="*",
help="test data file to input",
completer_method=Cmd.path_complete)
parser.add_argument('-B', '--bit_shift',
type=float, default=None,
help="shift input before offset and divisor. Negative for right shift.")
parser.add_argument('-D', '--divisor',
type=float, default=None,
help="divide all input data by this value")
parser.add_argument('-O', '--offset',
type=float, default=None,
help="offset all input data by this value")
parser.add_argument('-H', '--height',
type=int, default=None,
help="adjust image height to this value")
parser.add_argument('-W', '--width',
type=int, default=None,
help="adjust image width this value")
parser.add_argument('-T', '--transpose',
action="store_true", help='Swap W and H')
parser.add_argument('-F', '--nptype',
choices=np.sctypeDict.keys(), default=None,
help='interpret pixels as this numpy type')
parser.add_argument('-M', '--mode',
choices=MODES.keys(), default=None,
help="mode to import image in")
parser.add_argument('-N', '--norm_func',
choices=MODES.keys(), default=None,
help="lambda function to apply on input in the form x: fn(x)")
parser.add_argument('--rgb888_rgb565',
action="store_true",
help="convert 3 channel 8bits input into 1 channel 16bit rgb565")
def image_mode(self, val):
val = find_choice(MODES.keys(), val)
self.settings['image_mode'] = str(val)
},
'adjust_image': {
'type': bool,
'descr': 'adjust image input size and channels'
},
'image_width': {
'type': int,
'descr': 'input image width'
},
'image_height': {
'type': int,
'descr': 'input image height'
},
'image_mode': {
'type': str,
'descr': 'input image mode (one of {})'.format(", ".join(MODES.keys()))
},
'input_divisor': {
'type': float,
'descr': 'divide input tensor values by this value'
},
'input_offset': {
'type': float,
'descr': 'add this value to input tensor values'
},
'input_norm_func': {
'type': str,
'descr': 'lambda function in the form x: fn(x) where x is any input'
},
'graph_name': {
'type': str,
'type': bool,
'descr': 'adjust image input size and channels',
'choices': [True, False]
},
'image_width': {
'type': int,
'descr': 'input image width'
},
'image_height': {
'type': int,
'descr': 'input image height'
},
'image_mode': {
'type': str,
'descr': 'input image mode',
'choices': MODES.keys()
},
'input_divisor': {
'type': float,
'descr': 'divide input tensor values by this value'
},
'input_offset': {
'type': float,
'descr': 'add this value to input tensor values'
},
'input_norm_func': {
'type': str,
'descr': 'lambda function in the form x: fn(x) where x is any input'
},
'graph_name': {
'type': str,
]
DEFAULT_OPT_DESCRIPTIONS = {
'log_level': {'type': str, 'descr': 'set logging level', 'choices': VALID_LOG_LEVELS},
'load_quantization': {'type': bool, 'descr': 'load TFLITE quantization information', 'choices': [True, False]},
'load_dequantized': {'type': bool, 'descr': 'load the dequantized constant values from tflite quantized graph',
'choices': [True, False]},
'fusions': {'type': bool, 'descr': 'run standard graph fusions on graph load', 'choices': [True, False]},
'adjust_order': {'type': bool, 'descr': 'adjust activation and parameter dimension order\
to match autotiler on graph load', 'choices': [True, False]},
'weight_equalization': {'type': bool, 'descr': 'equalize weights on graph load', 'choices': [True, False]},
'equalization_threshold': {'type': float, 'descr': 'threshold for weight equalization convergence'},
'adjust_image': {'type': bool, 'descr': 'adjust image input size and channels', 'choices': [True, False]},
'image_width': {'type': int, 'descr': 'input image width'},
'image_height': {'type': int, 'descr': 'input image height'},
'image_mode': {'type': str, 'descr': 'input image mode', 'choices': MODES.keys()},
'input_divisor': {'type': float, 'descr': 'divide input tensor values by this value'},
'input_offset': {'type': float, 'descr': 'add this value to input tensor values'},
'input_norm_func': {'type': str, 'descr': 'lambda function in the form x: fn(x) where x is any input'},
'graph_name': {'type': str, 'descr': 'name of the graph used for code generation'},
'template_file': {'type': str, 'descr': 'template file used for code generation'},
}
class NNToolShellSettings(Cmd):
'''
This class have all the settings and properties that can be set up from the NNToolShell
To see the Code Generation settings, please refer to generation/autotiler_options.py
'''
def __init__(self, *args, **kwargs):
super(NNToolShellSettings, self).__init__(*args, **kwargs)