def __init__(
self,
bit_width: int,
min_val: float = -1.0,
max_val: float = 1.0,
narrow_range: bool = False,
quant_type: QuantType = QuantType.FP,
float_to_int_impl_type: FloatToIntImplType = FloatToIntImplType.
ROUND,
scaling_impl_type: ScalingImplType = ScalingImplType.PARAMETER,
scaling_override: Optional[Module] = None,
scaling_per_channel: bool = False,
scaling_stats_sigma: float = 3.0,
scaling_stats_op: StatsOp = StatsOp.MEAN_LEARN_SIGMA_STD,
scaling_stats_buffer_momentum: float = 0.1,
scaling_stats_permute_dims: Tuple = (1, 0, 2, 3),
per_channel_broadcastable_shape: Optional[Tuple[int, ...]] = None,
min_overall_bit_width: Optional[int] = 2,
max_overall_bit_width: Optional[int] = None,
bit_width_impl_override: Union[BitWidthParameter] = None,
bit_width_impl_type: BitWidthImplType = BitWidthImplType.CONST,
restrict_bit_width_type: RestrictValueType = RestrictValueType.INT,
restrict_scaling_type: RestrictValueType = RestrictValueType.
LOG_FP,
scaling_min_val: Optional[float] = SCALING_MIN_VAL,
override_pretrained_bit_width: bool = False,
return_quant_tensor: bool = False):
super(QuantHardTanh,
self).__init__(return_quant_tensor=return_quant_tensor)
if quant_type == QuantType.FP:
activation_impl = ConstScalarClamp(min_val=min_val,
max_val=max_val)
else:
activation_impl = Identity()
self.act_quant_proxy = ActivationQuantProxy(
activation_impl=activation_impl,
bit_width=bit_width,
signed=True,
narrow_range=narrow_range,
scaling_override=scaling_override,
min_val=min_val,
max_val=max_val,
quant_type=quant_type,
float_to_int_impl_type=float_to_int_impl_type,
scaling_impl_type=scaling_impl_type,
scaling_per_channel=scaling_per_channel,
scaling_min_val=scaling_min_val,
per_channel_broadcastable_shape=per_channel_broadcastable_shape,
min_overall_bit_width=min_overall_bit_width,
max_overall_bit_width=max_overall_bit_width,
bit_width_impl_override=bit_width_impl_override,
bit_width_impl_type=bit_width_impl_type,
restrict_bit_width_type=restrict_bit_width_type,
restrict_scaling_type=restrict_scaling_type,
override_pretrained_bit_width=override_pretrained_bit_width,
scaling_stats_sigma=scaling_stats_sigma,
scaling_stats_op=scaling_stats_op,
scaling_stats_buffer_momentum=scaling_stats_buffer_momentum,
scaling_stats_permute_dims=scaling_stats_permute_dims)
def __init__(
self,
bit_width: int,
max_val: float,
quant_type: QuantType = QuantType.FP,
float_to_int_impl_type: FloatToIntImplType = FloatToIntImplType.
ROUND,
scaling_impl_type: ScalingImplType = ScalingImplType.PARAMETER,
scaling_override: Optional[Module] = None,
scaling_per_channel: bool = False,
scaling_min_val: Optional[float] = SCALING_MIN_VAL,
scaling_stats_sigma=2.0,
scaling_stats_input_view_shape_impl: Optional[
StatsInputViewShapeImpl] = StatsInputViewShapeImpl.
OVER_OUTPUT_CHANNELS,
scaling_stats_op=StatsOp.MEAN_LEARN_SIGMA_STD,
scaling_stats_buffer_momentum=0.1,
scaling_stats_permute_dims=(1, 0, 2, 3),
per_channel_broadcastable_shape: Optional[Tuple[int, ...]] = None,
min_overall_bit_width: Optional[int] = 2,
max_overall_bit_width: Optional[int] = None,
bit_width_impl_override: Union[BitWidthParameter] = None,
bit_width_impl_type: BitWidthImplType = BitWidthImplType.CONST,
restrict_bit_width_type: RestrictValueType = RestrictValueType.INT,
restrict_scaling_type: RestrictValueType = RestrictValueType.
LOG_FP,
override_pretrained_bit_width: bool = False,
return_quant_tensor: bool = False):
super(QuantReLU,
self).__init__(return_quant_tensor=return_quant_tensor)
activation_impl = nn.ReLU()
self.act_quant_proxy = ActivationQuantProxy(
activation_impl=activation_impl,
bit_width=bit_width,
signed=False,
narrow_range=False,
scaling_override=scaling_override,
min_val=0.0,
max_val=max_val,
quant_type=quant_type,
float_to_int_impl_type=float_to_int_impl_type,
scaling_impl_type=scaling_impl_type,
scaling_per_channel=scaling_per_channel,
scaling_min_val=scaling_min_val,
per_channel_broadcastable_shape=per_channel_broadcastable_shape,
min_overall_bit_width=min_overall_bit_width,
max_overall_bit_width=max_overall_bit_width,
bit_width_impl_override=bit_width_impl_override,
bit_width_impl_type=bit_width_impl_type,
restrict_bit_width_type=restrict_bit_width_type,
restrict_scaling_type=restrict_scaling_type,
override_pretrained_bit_width=override_pretrained_bit_width,
scaling_stats_sigma=scaling_stats_sigma,
scaling_stats_permute_dims=scaling_stats_permute_dims,
scaling_stats_input_view_shape_impl=
scaling_stats_input_view_shape_impl,
scaling_stats_op=scaling_stats_op,
scaling_stats_buffer_momentum=scaling_stats_buffer_momentum)
def __init__(
self,
bit_width: int,
max_val: float,
quant_type: QuantType = QuantType.FP,
float_to_int_impl_type: FloatToIntImplType = FloatToIntImplType.
ROUND,
scaling_impl_type: ScalingImplType = ScalingImplType.PARAMETER,
scaling_override: Optional[Module] = None,
scaling_per_channel: bool = False,
scaling_min_val: Optional[float] = SCALING_MIN_VAL,
scaling_stats_sigma=2.0,
scaling_stats_op=StatsOp.MEAN_LEARN_SIGMA_STD,
scaling_stats_buffer_momentum=0.1,
scaling_stats_permute_dims=(1, 0, 2, 3),
per_channel_broadcastable_shape: Optional[Tuple[int, ...]] = None,
min_overall_bit_width: Optional[int] = 2,
max_overall_bit_width: Optional[int] = None,
bit_width_impl_override: Union[BitWidthParameter] = None,
bit_width_impl_type: BitWidthImplType = BitWidthImplType.CONST,
restrict_bit_width_type: RestrictValueType = RestrictValueType.INT,
restrict_scaling_type: RestrictValueType = RestrictValueType.
LOG_FP,
override_pretrained_bit_width: bool = False,
return_quant_tensor: bool = False):
super(QuantReLU,
self).__init__(return_quant_tensor=return_quant_tensor)
# save a copy of args passed constructor, used to determine whether
# the quantization config is exportable to something FINN supports
self.init_args = locals()
activation_impl = nn.ReLU()
self.act_quant_proxy = ActivationQuantProxy(
activation_impl=activation_impl,
bit_width=bit_width,
signed=False,
narrow_range=False,
scaling_override=scaling_override,
min_val=0.0,
max_val=max_val,
quant_type=quant_type,
float_to_int_impl_type=float_to_int_impl_type,
scaling_impl_type=scaling_impl_type,
scaling_per_channel=scaling_per_channel,
scaling_min_val=scaling_min_val,
per_channel_broadcastable_shape=per_channel_broadcastable_shape,
min_overall_bit_width=min_overall_bit_width,
max_overall_bit_width=max_overall_bit_width,
bit_width_impl_override=bit_width_impl_override,
bit_width_impl_type=bit_width_impl_type,
restrict_bit_width_type=restrict_bit_width_type,
restrict_scaling_type=restrict_scaling_type,
override_pretrained_bit_width=override_pretrained_bit_width,
scaling_stats_sigma=scaling_stats_sigma,
scaling_stats_permute_dims=scaling_stats_permute_dims,
scaling_stats_op=scaling_stats_op,
scaling_stats_buffer_momentum=scaling_stats_buffer_momentum)
def __init__(
self,
bit_width: int,
narrow_range: bool = False,
quant_type: QuantType = QuantType.FP,
float_to_int_impl_type: FloatToIntImplType = FloatToIntImplType.
ROUND,
min_overall_bit_width: Optional[int] = 2,
max_overall_bit_width: Optional[int] = None,
bit_width_impl_override: Union[BitWidthParameter] = None,
bit_width_impl_type: BitWidthImplType = BitWidthImplType.CONST,
restrict_bit_width_type: RestrictValueType = RestrictValueType.INT,
restrict_scaling_type: RestrictValueType = RestrictValueType.
LOG_FP,
scaling_min_val: Optional[float] = SCALING_MIN_VAL,
override_pretrained_bit_width: bool = False,
return_quant_tensor: bool = False):
super(QuantTanh,
self).__init__(return_quant_tensor=return_quant_tensor)
activation_impl = nn.Tanh()
self.act_quant_proxy = ActivationQuantProxy(
activation_impl=activation_impl,
bit_width=bit_width,
signed=True,
narrow_range=narrow_range,
scaling_override=None,
min_val=-1.0,
max_val=1.0,
quant_type=quant_type,
float_to_int_impl_type=float_to_int_impl_type,
scaling_impl_type=ScalingImplType.CONST,
scaling_per_channel=False,
scaling_min_val=scaling_min_val,
per_channel_broadcastable_shape=None,
min_overall_bit_width=min_overall_bit_width,
max_overall_bit_width=max_overall_bit_width,
bit_width_impl_override=bit_width_impl_override,
bit_width_impl_type=bit_width_impl_type,
restrict_bit_width_type=restrict_bit_width_type,
restrict_scaling_type=restrict_scaling_type,
override_pretrained_bit_width=override_pretrained_bit_width,
scaling_stats_sigma=None,
scaling_stats_input_view_shape_impl=None,
scaling_stats_op=None,
scaling_stats_buffer_momentum=None,
scaling_stats_permute_dims=None)
