ConvTranspose2d

类torch.ao.nn.quantized.ConvTranspose2d(in_channels, out_channels, kernel_size, stride=1, padding=0, output_padding=0, groups=1, bias=True, dilation=1, padding_mode='zeros', device=None, dtype=None)[源代码]

对由多个输入平面组成的一幅输入图像应用二维转置卷积运算。有关输入参数、超参数和实现的详细信息，请参见ConvTranspose2d。

有关特殊说明，请参见Conv2d

变量

• weight (Tensor) – 由可学习的权重参数派生而来的打包张量。

• scale (张量) – 控制输出尺度的标量值

• zero_point (Tensor) – 表示输出零点的标量值

参见 ConvTranspose2d 以获取其他属性。

示例:

>>> # QNNPACK or FBGEMM as backend
>>> torch.backends.quantized.engine = 'qnnpack'
>>> # With square kernels and equal stride
>>> import torch.ao.nn.quantized as nnq
>>> m = nnq.ConvTranspose2d(16, 33, 3, stride=2)
>>> # non-square kernels and unequal stride and with padding
>>> m = nnq.ConvTranspose2d(16, 33, (3, 5), stride=(2, 1), padding=(4, 2))
>>> input = torch.randn(20, 16, 50, 100)
>>> q_input = torch.quantize_per_tensor(input, scale=1.0, zero_point=0, dtype=torch.quint8)
>>> output = m(q_input)
>>> # exact output size can be also specified as an argument
>>> input = torch.randn(1, 16, 12, 12)
>>> q_input = torch.quantize_per_tensor(input, scale=1.0, zero_point=0, dtype=torch.quint8)
>>> downsample = nnq.Conv2d(16, 16, 3, stride=2, padding=1)
>>> upsample = nnq.ConvTranspose2d(16, 16, 3, stride=2, padding=1)
>>> h = downsample(q_input)
>>> h.size()
torch.Size([1, 16, 6, 6])
>>> output = upsample(h, output_size=input.size())
>>> output.size()
torch.Size([1, 16, 12, 12])