def __init__(
self,
*args,
input_quantizer=None,
depthwise_quantizer=None,
pointwise_quantizer=None,
**kwargs,
):
# This is currently undocumented until we have explored better options
self._custom_metrics = kwargs.pop("metrics", ["flip_ratio"])
self.input_quantizer = quantizers.get(input_quantizer)
self.depthwise_quantizer = quantizers.get(depthwise_quantizer)
self.pointwise_quantizer = quantizers.get(pointwise_quantizer)
self.quantized_latent_weights = []
self.quantizers = []
super().__init__(*args, **kwargs)
if depthwise_quantizer and not self.depthwise_constraint:
log.warning(
"Using `depthwise_quantizer` without setting `depthwise_constraint` "
"may result in starved weights (where the gradient is always zero)."
)
if pointwise_quantizer and not self.pointwise_constraint:
log.warning(
"Using `pointwise_quantizer` without setting `pointwise_constraint` "
"may result in starved weights (where the gradient is always zero)."
)
def __init__(
self,
*args,
input_quantizer=None,
depthwise_quantizer=None,
pointwise_quantizer=None,
metrics=None,
**kwargs,
):
self.input_quantizer = quantizers.get(input_quantizer)
self.depthwise_quantizer = quantizers.get(depthwise_quantizer)
self.pointwise_quantizer = quantizers.get(pointwise_quantizer)
self._custom_metrics = (
metrics if metrics is not None else lq_metrics.get_training_metrics()
)
super().__init__(*args, **kwargs)
if depthwise_quantizer and not self.depthwise_constraint:
log.warning(
"Using `depthwise_quantizer` without setting `depthwise_constraint` "
"may result in starved weights (where the gradient is always zero)."
)
if pointwise_quantizer and not self.pointwise_constraint:
log.warning(
"Using `pointwise_quantizer` without setting `pointwise_constraint` "
"may result in starved weights (where the gradient is always zero)."
)
def __init__(
self, *args, input_quantizer=None, kernel_quantizer=None, metrics=None, **kwargs
):
self.input_quantizer = quantizers.get(input_quantizer)
self.kernel_quantizer = quantizers.get(kernel_quantizer)
self._custom_metrics = (
metrics if metrics is not None else lq_metrics.get_training_metrics()
)
super().__init__(*args, **kwargs)
if kernel_quantizer and not self.kernel_constraint:
log.warning(
"Using a weight quantizer without setting `kernel_constraint` "
"may result in starved weights (where the gradient is always zero)."
)
def __init__(self, *args, input_quantizer=None, **kwargs):
self.input_quantizer = quantizers.get(input_quantizer)
super().__init__(*args, **kwargs)
