ConvTranspose2dQAT

Function Usage

Constructs the QAT operator of ConvTranspose2d.

Prototype

API for operator construction from scratch:

amct_pytorch.nn.module.quantization.conv_tranpose_2d.ConvTranspose2dQAT(in_channels, out_channels, kernel_size, stride, padding, dilation, groups, bias, padding_mode, device, dtype, config)

API for construction based on the native operator:

amct_pytorch.nn.module.quantization.conv_transpose_2d.ConvTranspose2dQAT.from_float(mod, config)

Command-Line Options

**Table 1** Parameters in the API for operator construction from scratch
Option	Input/Output	Meaning	Restriction
in_channels	Input	Number of input channels.	Type: int This parameter is mandatory.
out_channels	Input	Number of output channels.	Type: int This parameter is mandatory.
kernel_size	Input	Size of the convolution kernel.	An int or a tuple. This parameter is mandatory.
stride	Input	Convolution stride.	An int or a tuple. The default value is 1.
padding	Input	Padding size.	An int or a tuple. The default value is 0.
dilation	Input	Spacing between kernel elements.	An int or a tuple. The default value is 1.
groups	Input	Connections between the inputs and outputs.	Type: int Default value: 1
bias	Input	Indicates whether to enable bias items to participate in learning.	Type: bool The default value is True.
padding_mode	Input	Padding mode.	Must be zeros.
device	Input	Running device.	Default: None
dtype	Input	Torch data type.	Torch data type. Only torch.float32 is supported.
config	Input	The following is a configuration example. For details about quantization configuration parameters, see Quantization Configuration Parameters. config = { "retrain_enable":true, "retrain_data_config": { "dst_type": "INT8", "batch_num": 10, "fixed_min": False, "clip_min": -1.0, "clip_max": 1.0 }, "retrain_weight_config": { "dst_type": "INT8", "weights_retrain_algo": "arq_retrain", "channel_wise": False } }	A dict. Default: None

**Table 2** Parameters in the API for construction based on native operators
Parameter	Input/Output	Description	Restriction
mod	Input	Native ConvTranspose2dQAT operator to be quantized.	A torch.nn.Module.
config	Input	Quantization configuration. The following is a configuration example. For details about quantization configuration parameters, see Quantization Configuration Parameters. config = { "retrain_enable":true, "retrain_data_config": { "dst_type": "INT8", "batch_num": 10, "fixed_min": False, "clip_min": -1.0, "clip_max": 1.0 }, "retrain_weight_config": { "dst_type": "INT8", "weights_retrain_algo": "arq_retrain", "channel_wise": False } }	A dict. Default: None

Returns

A QAT operator of ConvTranspose2dQAT for subsequent quantization perception training.

Calling Example

Construction from scratch:

from amct_pytorch.nn.module.quantization.conv_transpose_2d import ConvTranspose2dQAT

ConvTranspose2dQAT(in_channels=1, out_channels=1, kernel_size=1, stride=1,
                   padding=0, dilation=1, groups=1, bias=True,
                   padding_mode='zeros', device=None, dtype=None, config=None)

Construction based on the native operator:

import torch

from amct_pytorch.nn.module.quantization.conv_transpose_2d import ConvTranspose2dQAT

conv_transpose2d_op = torch.nn.ConvTranspose2d(in_channels=1, out_channels=1, kernel_size=1, stride=1, padding=0, dilation=1, groups=1, bias=True, padding_mode='zeros', device=None, dtype=None)
ConvTranspose2dQAT.from_float(mod=conv_transpose2d_op, config=None)

Parent topic: Single-operator mode