def _get_precision_from_quantizer(quantizer):
import qkeras
if isinstance(quantizer, str):
quantizer_obj = qkeras.get_quantizer(quantizer)
quantizer = {}
# Some activations are classes with get_config method
if hasattr(quantizer_obj, 'get_config'):
quantizer['class_name'] = quantizer_obj.__class__.__name__
quantizer['config'] = quantizer_obj.get_config()
# Some activations are just functions
else:
quantizer['class_name'] = quantizer_obj.__name__
supported_quantizers = ['quantized_bits', 'quantized_relu', 'quantized_tanh']
if quantizer['class_name'] in supported_quantizers:
bits = int(quantizer['config']['bits']) + 1
integer = int(quantizer['config']['integer']) + 1
elif quantizer['class_name'] in ['binary', 'stochastic_binary', 'binary_tanh']:
bits = 2
integer = 2
elif quantizer['class_name'] in ['ternary', 'stochastic_ternary', 'ternary_tanh']:
bits = 2
integer = 2
else:
raise Exception('ERROR: Unsupported quantizer: {}'.format(quantizer['class_name']))
decimal = bits - integer
if decimal > 0:
return 'ap_fixed<{},{}>'.format(bits, integer)
else:
return 'ap_int<{}>'.format(bits)
def _get_precision_from_quantizer(quantizer):
import qkeras
if isinstance(quantizer, str):
quantizer_obj = qkeras.get_quantizer(quantizer)
quantizer = {}
# Some activations are classes with get_config method
if hasattr(quantizer_obj, 'get_config'):
quantizer['class_name'] = quantizer_obj.__class__.__name__
quantizer['config'] = quantizer_obj.get_config()
# Some activations are just functions
else:
quantizer['class_name'] = quantizer_obj.__name__
supported_quantizers = [
'quantized_bits', 'quantized_relu', 'quantized_tanh', 'quantized_po2',
'quantized_relu_po2'
]
signed = True
if quantizer['class_name'] in supported_quantizers:
bits = int(quantizer['config']['bits'])
# if integer isn't specified, it should be the same as bits
integer = int(quantizer['config'].get('integer', bits - 1)) + 1
if quantizer['class_name'] == 'quantized_relu':
signed = False
integer -= 1
elif quantizer['class_name'] in [
'binary', 'stochastic_binary', 'binary_tanh'
]:
bits = 2
integer = 2
elif quantizer['class_name'] in [
'ternary', 'stochastic_ternary', 'ternary_tanh'
]:
bits = 2
integer = 2
else:
raise Exception('ERROR: Unsupported quantizer: {}'.format(
quantizer['class_name']))
decimal = bits - integer
signed = '' if signed else 'u'
if decimal > 0:
return 'ap_{}fixed<{},{}>'.format(signed, bits, integer)
else:
return 'ap_{}int<{}>'.format(signed, bits)
def __init__(self, config):
self.bits = config['config']['bits']
self.quantizer_fn = get_quantizer(config)
self.hls_type = ExponentPrecisionType(width=self.bits, signed=True)
def _act_size(self, layer):
"""Computes size of activations of a layer in bits."""
i_size = self.input_bits
o_size = self.output_bits
t_size = self.ref_bits
output_size = np.prod(layer.output.shape[1:])
# we compute activation sizes for inputs and outputs
if layer.__class__.__name__ in ["InputLayer"]:
return i_size * output_size
elif layer.__class__.__name__ in [
"Dense", "Conv2D", "Conv1D", "DepthwiseConv2D"
]:
if layer.activation is not None and layer.activation.__name__ != "linear":
return t_size * output_size
else:
return 0
elif layer.__class__.__name__ in [
"QDense", "QConv2D", "QConv1D", "QDepthwiseConv2D"
]:
if layer.activation is None:
is_softmax = False
is_linear = False
else:
if isinstance(layer.activation, six.string_types):
is_softmax = layer.activation == "softmax"
is_linear = layer.activation == "linear"
elif hasattr(layer.activation, "__name__"):
is_softmax = layer.activation.__name__ == "softmax"
is_linear = layer.activation.__name__ == "linear"
else:
is_softmax = False
is_linear = False
if is_softmax:
bits = o_size
elif is_linear:
bits = 0
else:
assert not isinstance(layer.activation, six.string_types)
if hasattr(layer.activation, "bits"):
bits = layer.activation.bits
else:
bits = t_size
return bits * np.prod(layer.output.shape.as_list()[1:])
elif layer.__class__.__name__ in ["QActivation", "Activation"]:
if isinstance(layer.activation, six.string_types):
is_linear = layer.activation == "linear"
is_softmax = layer.activation == "softmax"
is_sigmoid = layer.activation == "sigmoid"
else:
is_linear = layer.activation.__name__ == "linear"
is_softmax = layer.activation.__name__ == "softmax"
is_sigmoid = layer.activation.__name__ == "sigmoid"
if is_linear:
bits = 0
elif is_softmax or is_sigmoid:
bits = o_size
else:
if isinstance(layer.activation, six.string_types):
activation = get_quantizer(layer.activation)
else:
activation = layer.activation
if hasattr(activation, "bits"):
bits = activation.bits
else:
bits = t_size
return bits * output_size
return 0
