Class TestNet

imperative/python/test/unit/module/test_qat.py:42–55

    kernel_size = 3

    class TestNet(Module):
        def __init__(self, groups, bias):
            super().__init__()
            self.quant = QuantStub()
            self.dequant = DequantStub()
            self.conv_bn = ConvBn2d(
                in_channels, out_channels, kernel_size, groups=groups, bias=bias,
            )

        def forward(self, inp):
            out = self.quant(inp)
            out = self.conv_bn(out)
            out = self.dequant(out)
            return out

    inputs = tensor(np.random.randn(4, in_channels, 32, 32).astype(np.float32))
    for groups, bias in product([1, 4], [True, False]):