mindspore.nn.TensorAddQuant
- class mindspore.nn.TensorAddQuant(ema_decay=0.999, quant_config=quant_config_default, quant_dtype=QuantDtype.INT8)[source]
Adds fake quantized operation after TensorAdd operation.
This part is a more detailed overview of TensorAdd operation. For more details about Quantization, please refer to the implementation of class of FakeQuantWithMinMaxObserver,
mindspore.nn.FakeQuantWithMinMaxObserver
.- Parameters
ema_decay (float) – Exponential Moving Average algorithm parameter. Default: 0.999.
quant_config (QuantConfig) – Configures the types of quant observer and quant settings of weight and activation. Note that, QuantConfig is a special namedtuple, which is designed for quantization and can be generated by
mindspore.compression.quant.create_quant_config()
method. Default: QuantConfig with both items set to defaultFakeQuantWithMinMaxObserver
.quant_dtype (QuantDtype) – Specifies the FakeQuant datatype. Default: QuantDtype.INT8.
- Inputs:
x1 (Tensor) - The first tensor of TensorAddQuant. The input dimension is preferably 2D or 4D.
x2 (Tensor) - The second tensor of TensorAddQuant. Has the same shape with x1.
- Outputs:
Tensor, with the same type and shape as the x1.
- Raises
TypeError – If ema_decay is not a float.
ValueError – If the shape of x2 is different with x1.
- Supported Platforms:
Ascend
GPU
Examples
>>> import mindspore >>> from mindspore.compression import quant >>> from mindspore import Tensor >>> qconfig = quant.create_quant_config() >>> add_quant = nn.TensorAddQuant(quant_config=qconfig) >>> x1 = Tensor(np.array([[1, 2, 1], [-2, 0, -1]]), mindspore.float32) >>> x2 = Tensor(np.ones((2, 3)), mindspore.float32) >>> output = add_quant(x1, x2) >>> print(output) [[ 1.9764705 3.011765 1.9764705] [-0.9882355 0.9882355 0. ]]