def create_int8_bias_tensor_fill(tensor, out_blob_name, x_q_param, w_q_param):
"""
Similar to create_int8_given_tensor_fill, but for bias blobs to be stored
as int32.
"""
scale = x_q_param.scale * w_q_param.scale
quantized_tensor = np.around(tensor / scale).astype(np.int32)
quantized_tensor.reshape(-1)
op = core.CreateOperator("Int8GivenIntTensorFill", [], out_blob_name)
op.arg.extend([
utils.MakeArgument("values", quantized_tensor),
utils.MakeArgument("shape", quantized_tensor.shape),
])
q_param = hardcode_scale_zp.QuantizationParam(scale, 0)
add_quantization_param_args_(op, q_param)
return op
def choose_quantization_params(tensor_min,
tensor_max,
preserve_sparsity=False):
if tensor_min < 0 and tensor_max > 0 and preserve_sparsity:
symmetric_qmin = -(255 // 2 + 1)
symmetric_qmax = 255 // 2
max_scale = max(abs(tensor_min / symmetric_qmin),
abs(tensor_max / symmetric_qmax))
tensor_min = max_scale * symmetric_qmin
tensor_max = max_scale * symmetric_qmax
q_param = hardcode_scale_zp.choose_quantization_params(
tensor_min, tensor_max)
if tensor_min < 0 and tensor_max > 0 and preserve_sparsity:
q_param = hardcode_scale_zp.QuantizationParam(q_param.scale, 128)
return q_param
def add_quantization_param_args(op, tensor, preserve_sparsity=False):
tensor_min = 0 if tensor.size == 0 else tensor.min()
tensor_max = 0 if tensor.size == 0 else tensor.max()
if tensor_min < 0 and tensor_max > 0 and preserve_sparsity:
symmetric_qmin = -(255 // 2 + 1)
symmetric_qmax = 255 // 2
max_scale = max(abs(tensor_min / symmetric_qmin),
abs(tensor_max / symmetric_qmax))
tensor_min = max_scale * symmetric_qmin
tensor_max = max_scale * symmetric_qmax
q_param = hardcode_scale_zp.choose_quantization_params(
tensor_min, tensor_max)
if tensor_min < 0 and tensor_max > 0 and preserve_sparsity:
q_param = hardcode_scale_zp.QuantizationParam(q_param.scale, 128)
add_quantization_param_args_(op, q_param)
return q_param