输入尺寸和输出尺寸的问题

[LanWong1]

我并没有看到在输入端把图像resize到1024512，仅仅是把短边等比例缩放到512，长边并没有固定到1024，为何输出的时候，就直接缩放到1024512呢？
class Decoder (nn.Module):
def init(self, num_classes):
super().init()

    self.apn = APN_Module(in_ch=128,out_ch=20)
    # self.upsample = Interpolate(size=(512, 1024), mode="bilinear")
    # self.output_conv = nn.ConvTranspose2d(16, num_classes, kernel_size=4, stride=2, padding=1, output_padding=0, bias=True)
    # self.output_conv = nn.ConvTranspose2d(16, num_classes, kernel_size=3, stride=2, padding=1, output_padding=1, bias=True)
    # self.output_conv = nn.ConvTranspose2d(16, num_classes, kernel_size=2, stride=2, padding=0, output_padding=0, bias=True)

def forward(self, input):
    
    output = self.apn(input)
    out = interpolate(output, size=(512, 1024), mode="bilinear", align_corners=True)
    # out = self.upsample(output)
    # print(out.shape)
    return out

[Benjiaminh]

我并没有看到在输入端把图像resize到1024_512，仅仅是把短边等比例缩放到512，长边并没有固定到1024，为何输出的时候，就直接缩放到1024_512呢？
class Decoder (nn.Module):
def init(self, num_classes):
super().init()

    self.apn = APN_Module(in_ch=128,out_ch=20)
    # self.upsample = Interpolate(size=(512, 1024), mode="bilinear")
    # self.output_conv = nn.ConvTranspose2d(16, num_classes, kernel_size=4, stride=2, padding=1, output_padding=0, bias=True)
    # self.output_conv = nn.ConvTranspose2d(16, num_classes, kernel_size=3, stride=2, padding=1, output_padding=1, bias=True)
    # self.output_conv = nn.ConvTranspose2d(16, num_classes, kernel_size=2, stride=2, padding=0, output_padding=0, bias=True)

def forward(self, input):
    
    output = self.apn(input)
    out = interpolate(output, size=(512, 1024), mode="bilinear", align_corners=True)
    # out = self.upsample(output)
    # print(out.shape)
    return out

可以相互交流下不，我最近也在看这个，但是也有些问题，我的qq联系方式1602356172

[Vieeo]

LEDNet‘paras is 0.94M？Why I get 2.3M？About you？

[HKQX]

Resize函数会将另外一边按照原先图像比例乘以512 实际另外一边是2048%1024*512=1024，具体可以看函数说明