utils.py

import torch
import torch.nn as nn
import numpy as np
import torch.nn.functional as F

from torch.autograd import Variable


class conv2DBatchNorm(nn.Module):
    def __init__(
        self,
        in_channels,
        n_filters,
        k_size,
        stride,
        padding,
        bias=True,
        dilation=1,
        is_batchnorm=True,
    ):
        super(conv2DBatchNorm, self).__init__()

        conv_mod = nn.Conv2d(
            int(in_channels),
            int(n_filters),
            kernel_size=k_size,
            padding=padding,
            stride=stride,
            bias=bias,
            dilation=dilation,
        )

        if is_batchnorm:
            self.cb_unit = nn.Sequential(conv_mod, nn.BatchNorm2d(int(n_filters)))
        else:
            self.cb_unit = nn.Sequential(conv_mod)

    def forward(self, inputs):
        outputs = self.cb_unit(inputs)
        return outputs


class conv2DGroupNorm(nn.Module):
    def __init__(
        self, in_channels, n_filters, k_size, stride, padding, bias=True, dilation=1, n_groups=16
    ):
        super(conv2DGroupNorm, self).__init__()

        conv_mod = nn.Conv2d(
            int(in_channels),
            int(n_filters),
            kernel_size=k_size,
            padding=padding,
            stride=stride,
            bias=bias,
            dilation=dilation,
        )

        self.cg_unit = nn.Sequential(conv_mod, nn.GroupNorm(n_groups, int(n_filters)))

    def forward(self, inputs):
        outputs = self.cg_unit(inputs)
        return outputs


class deconv2DBatchNorm(nn.Module):
    def __init__(self, in_channels, n_filters, k_size, stride, padding, bias=True):
        super(deconv2DBatchNorm, self).__init__()

        self.dcb_unit = nn.Sequential(
            nn.ConvTranspose2d(
                int(in_channels),
                int(n_filters),
                kernel_size=k_size,
                padding=padding,
                stride=stride,
                bias=bias,
            ),
            nn.BatchNorm2d(int(n_filters)),
        )

    def forward(self, inputs):
        outputs = self.dcb_unit(inputs)
        return outputs


class conv2DBatchNormRelu(nn.Module):
    def __init__(
        self,
        in_channels,
        n_filters,
        k_size,
        stride,
        padding,
        bias=True,
        dilation=1,
        is_batchnorm=True,
    ):
        super(conv2DBatchNormRelu, self).__init__()

        conv_mod = nn.Conv2d(
            int(in_channels),
            int(n_filters),
            kernel_size=k_size,
            padding=padding,
            stride=stride,
            bias=bias,
            dilation=dilation,
        )

        if is_batchnorm:
            self.cbr_unit = nn.Sequential(
                conv_mod, nn.BatchNorm2d(int(n_filters)), nn.ReLU(inplace=True)
            )
        else:
            self.cbr_unit = nn.Sequential(conv_mod, nn.ReLU(inplace=True))

    def forward(self, inputs):
        outputs = self.cbr_unit(inputs)
        return outputs


class conv2DGroupNormRelu(nn.Module):
    def __init__(
        self, in_channels, n_filters, k_size, stride, padding, bias=True, dilation=1, n_groups=16
    ):
        super(conv2DGroupNormRelu, self).__init__()

        conv_mod = nn.Conv2d(
            int(in_channels),
            int(n_filters),
            kernel_size=k_size,
            padding=padding,
            stride=stride,
            bias=bias,
            dilation=dilation,
        )

        self.cgr_unit = nn.Sequential(
            conv_mod, nn.GroupNorm(n_groups, int(n_filters)), nn.ReLU(inplace=True)
        )

    def forward(self, inputs):
        outputs = self.cgr_unit(inputs)
        return outputs


class deconv2DBatchNormRelu(nn.Module):
    def __init__(self, in_channels, n_filters, k_size, stride, padding, bias=True):
        super(deconv2DBatchNormRelu, self).__init__()

        self.dcbr_unit = nn.Sequential(
            nn.ConvTranspose2d(
                int(in_channels),
                int(n_filters),
                kernel_size=k_size,
                padding=padding,
                stride=stride,
                bias=bias,
            ),
            nn.BatchNorm2d(int(n_filters)),
            nn.ReLU(inplace=True),
        )

    def forward(self, inputs):
        outputs = self.dcbr_unit(inputs)
        return outputs


class unetConv2(nn.Module):
    def __init__(self, in_size, out_size, is_batchnorm):
        super(unetConv2, self).__init__()

        if is_batchnorm:
            self.conv1 = nn.Sequential(
                nn.Conv2d(in_size, out_size, 3, 1, 0), nn.BatchNorm2d(out_size), nn.ReLU()
            )
            self.conv2 = nn.Sequential(
                nn.Conv2d(out_size, out_size, 3, 1, 0), nn.BatchNorm2d(out_size), nn.ReLU()
            )
        else:
            self.conv1 = nn.Sequential(nn.Conv2d(in_size, out_size, 3, 1, 0), nn.ReLU())
            self.conv2 = nn.Sequential(nn.Conv2d(out_size, out_size, 3, 1, 0), nn.ReLU())

    def forward(self, inputs):
        outputs = self.conv1(inputs)
        outputs = self.conv2(outputs)
        return outputs


class unetUp(nn.Module):
    def __init__(self, in_size, out_size, is_deconv):
        super(unetUp, self).__init__()
        self.conv = unetConv2(in_size, out_size, False)
        if is_deconv:
            self.up = nn.ConvTranspose2d(in_size, out_size, kernel_size=2, stride=2)
        else:
            self.up = nn.UpsamplingBilinear2d(scale_factor=2)

    def forward(self, inputs1, inputs2):
        outputs2 = self.up(inputs2)
        offset = outputs2.size()[2] - inputs1.size()[2]
        padding = 2 * [offset // 2, offset // 2]
        outputs1 = F.pad(inputs1, padding)
        return self.conv(torch.cat([outputs1, outputs2], 1))


class segnetDown2(nn.Module):
    def __init__(self, in_size, out_size):
        super(segnetDown2, self).__init__()
        self.conv1 = conv2DBatchNormRelu(in_size, out_size, 3, 1, 1)
        self.conv2 = conv2DBatchNormRelu(out_size, out_size, 3, 1, 1)
        self.maxpool_with_argmax = nn.MaxPool2d(2, 2, return_indices=True)

    def forward(self, inputs):
        outputs = self.conv1(inputs)
        outputs = self.conv2(outputs)
        unpooled_shape = outputs.size()
        outputs, indices = self.maxpool_with_argmax(outputs)
        return outputs, indices, unpooled_shape


class segnetDown3(nn.Module):
    def __init__(self, in_size, out_size):
        super(segnetDown3, self).__init__()
        self.conv1 = conv2DBatchNormRelu(in_size, out_size, 3, 1, 1)
        self.conv2 = conv2DBatchNormRelu(out_size, out_size, 3, 1, 1)
        self.conv3 = conv2DBatchNormRelu(out_size, out_size, 3, 1, 1)
        self.maxpool_with_argmax = nn.MaxPool2d(2, 2, return_indices=True)

    def forward(self, inputs):
        outputs = self.conv1(inputs)
        outputs = self.conv2(outputs)
        outputs = self.conv3(outputs)
        unpooled_shape = outputs.size()
        outputs, indices = self.maxpool_with_argmax(outputs)
        return outputs, indices, unpooled_shape


class segnetUp2(nn.Module):
    def __init__(self, in_size, out_size):
        super(segnetUp2, self).__init__()
        self.unpool = nn.MaxUnpool2d(2, 2)
        self.conv1 = conv2DBatchNormRelu(in_size, in_size, 3, 1, 1)
        self.conv2 = conv2DBatchNormRelu(in_size, out_size, 3, 1, 1)

    def forward(self, inputs, indices, output_shape):
        outputs = self.unpool(input=inputs, indices=indices, output_size=output_shape)
        outputs = self.conv1(outputs)
        outputs = self.conv2(outputs)
        return outputs


class segnetUp3(nn.Module):
    def __init__(self, in_size, out_size):
        super(segnetUp3, self).__init__()
        self.unpool = nn.MaxUnpool2d(2, 2)
        self.conv1 = conv2DBatchNormRelu(in_size, in_size, 3, 1, 1)
        self.conv2 = conv2DBatchNormRelu(in_size, in_size, 3, 1, 1)
        self.conv3 = conv2DBatchNormRelu(in_size, out_size, 3, 1, 1)

    def forward(self, inputs, indices, output_shape):
        outputs = self.unpool(input=inputs, indices=indices, output_size=output_shape)
        outputs = self.conv1(outputs)
        outputs = self.conv2(outputs)
        outputs = self.conv3(outputs)
        return outputs


class residualBlock(nn.Module):
    expansion = 1

    def __init__(self, in_channels, n_filters, stride=1, downsample=None):
        super(residualBlock, self).__init__()

        self.convbnrelu1 = conv2DBatchNormRelu(in_channels, n_filters, 3, stride, 1, bias=False)
        self.convbn2 = conv2DBatchNorm(n_filters, n_filters, 3, 1, 1, bias=False)
        self.downsample = downsample
        self.stride = stride
        self.relu = nn.ReLU(inplace=True)

    def forward(self, x):
        residual = x

        out = self.convbnrelu1(x)
        out = self.convbn2(out)

        if self.downsample is not None:
            residual = self.downsample(x)

        out += residual
        out = self.relu(out)
        return out


class residualBottleneck(nn.Module):
    expansion = 4

    def __init__(self, in_channels, n_filters, stride=1, downsample=None):
        super(residualBottleneck, self).__init__()
        self.convbn1 = nn.Conv2DBatchNorm(in_channels, n_filters, k_size=1, bias=False)
        self.convbn2 = nn.Conv2DBatchNorm(
            n_filters, n_filters, k_size=3, padding=1, stride=stride, bias=False
        )
        self.convbn3 = nn.Conv2DBatchNorm(n_filters, n_filters * 4, k_size=1, bias=False)
        self.relu = nn.ReLU(inplace=True)
        self.downsample = downsample
        self.stride = stride

    def forward(self, x):
        residual = x

        out = self.convbn1(x)
        out = self.convbn2(out)
        out = self.convbn3(out)

        if self.downsample is not None:
            residual = self.downsample(x)

        out += residual
        out = self.relu(out)

        return out


class linknetUp(nn.Module):
    def __init__(self, in_channels, n_filters):
        super(linknetUp, self).__init__()

        # B, 2C, H, W -> B, C/2, H, W
        self.convbnrelu1 = conv2DBatchNormRelu(
            in_channels, n_filters / 2, k_size=1, stride=1, padding=1
        )

        # B, C/2, H, W -> B, C/2, H, W
        self.deconvbnrelu2 = nn.deconv2DBatchNormRelu(
            n_filters / 2, n_filters / 2, k_size=3, stride=2, padding=0
        )

        # B, C/2, H, W -> B, C, H, W
        self.convbnrelu3 = conv2DBatchNormRelu(
            n_filters / 2, n_filters, k_size=1, stride=1, padding=1
        )

    def forward(self, x):
        x = self.convbnrelu1(x)
        x = self.deconvbnrelu2(x)
        x = self.convbnrelu3(x)
        return x


class FRRU(nn.Module):
    """
    Full Resolution Residual Unit for FRRN
    """

    def __init__(self, prev_channels, out_channels, scale, group_norm=False, n_groups=None):
        super(FRRU, self).__init__()
        self.scale = scale
        self.prev_channels = prev_channels
        self.out_channels = out_channels
        self.group_norm = group_norm
        self.n_groups = n_groups

        if self.group_norm:
            conv_unit = conv2DGroupNormRelu
            self.conv1 = conv_unit(
                prev_channels + 32,
                out_channels,
                k_size=3,
                stride=1,
                padding=1,
                bias=False,
                n_groups=self.n_groups,
            )
            self.conv2 = conv_unit(
                out_channels,
                out_channels,
                k_size=3,
                stride=1,
                padding=1,
                bias=False,
                n_groups=self.n_groups,
            )

        else:
            conv_unit = conv2DBatchNormRelu
            self.conv1 = conv_unit(
                prev_channels + 32, out_channels, k_size=3, stride=1, padding=1, bias=False
            )
            self.conv2 = conv_unit(
                out_channels, out_channels, k_size=3, stride=1, padding=1, bias=False
            )

        self.conv_res = nn.Conv2d(out_channels, 32, kernel_size=1, stride=1, padding=0)

    def forward(self, y, z):
        x = torch.cat([y, nn.MaxPool2d(self.scale, self.scale)(z)], dim=1)
        y_prime = self.conv1(x)
        y_prime = self.conv2(y_prime)

        x = self.conv_res(y_prime)
        upsample_size = torch.Size([_s * self.scale for _s in y_prime.shape[-2:]])
        x = F.upsample(x, size=upsample_size, mode="nearest")
        z_prime = z + x

        return y_prime, z_prime


class RU(nn.Module):
    """
    Residual Unit for FRRN
    """

    def __init__(self, channels, kernel_size=3, strides=1, group_norm=False, n_groups=None):
        super(RU, self).__init__()
        self.group_norm = group_norm
        self.n_groups = n_groups

        if self.group_norm:
            self.conv1 = conv2DGroupNormRelu(
                channels,
                channels,
                k_size=kernel_size,
                stride=strides,
                padding=1,
                bias=False,
                n_groups=self.n_groups,
            )
            self.conv2 = conv2DGroupNorm(
                channels,
                channels,
                k_size=kernel_size,
                stride=strides,
                padding=1,
                bias=False,
                n_groups=self.n_groups,
            )

        else:
            self.conv1 = conv2DBatchNormRelu(
                channels, channels, k_size=kernel_size, stride=strides, padding=1, bias=False
            )
            self.conv2 = conv2DBatchNorm(
                channels, channels, k_size=kernel_size, stride=strides, padding=1, bias=False
            )

    def forward(self, x):
        incoming = x
        x = self.conv1(x)
        x = self.conv2(x)
        return x + incoming


class residualConvUnit(nn.Module):
    def __init__(self, channels, kernel_size=3):
        super(residualConvUnit, self).__init__()

        self.residual_conv_unit = nn.Sequential(
            nn.ReLU(inplace=True),
            nn.Conv2d(channels, channels, kernel_size=kernel_size),
            nn.ReLU(inplace=True),
            nn.Conv2d(channels, channels, kernel_size=kernel_size),
        )

    def forward(self, x):
        input = x
        x = self.residual_conv_unit(x)
        return x + input


class multiResolutionFusion(nn.Module):
    def __init__(self, channels, up_scale_high, up_scale_low, high_shape, low_shape):
        super(multiResolutionFusion, self).__init__()

        self.up_scale_high = up_scale_high
        self.up_scale_low = up_scale_low

        self.conv_high = nn.Conv2d(high_shape[1], channels, kernel_size=3)

        if low_shape is not None:
            self.conv_low = nn.Conv2d(low_shape[1], channels, kernel_size=3)

    def forward(self, x_high, x_low):
        high_upsampled = F.upsample(
            self.conv_high(x_high), scale_factor=self.up_scale_high, mode="bilinear"
        )

        if x_low is None:
            return high_upsampled

        low_upsampled = F.upsample(
            self.conv_low(x_low), scale_factor=self.up_scale_low, mode="bilinear"
        )

        return low_upsampled + high_upsampled


class chainedResidualPooling(nn.Module):
    def __init__(self, channels, input_shape):
        super(chainedResidualPooling, self).__init__()

        self.chained_residual_pooling = nn.Sequential(
            nn.ReLU(inplace=True),
            nn.MaxPool2d(5, 1, 2),
            nn.Conv2d(input_shape[1], channels, kernel_size=3),
        )

    def forward(self, x):
        input = x
        x = self.chained_residual_pooling(x)
        return x + input


class pyramidPooling(nn.Module):
    def __init__(
        self, in_channels, pool_sizes, model_name="pspnet", fusion_mode="cat", is_batchnorm=True
    ):
        super(pyramidPooling, self).__init__()

        bias = not is_batchnorm

        self.paths = []
        for i in range(len(pool_sizes)):
            self.paths.append(
                conv2DBatchNormRelu(
                    in_channels,
                    int(in_channels / len(pool_sizes)),
                    1,
                    1,
                    0,
                    bias=bias,
                    is_batchnorm=is_batchnorm,
                )
            )

        self.path_module_list = nn.ModuleList(self.paths)
        self.pool_sizes = pool_sizes
        self.model_name = model_name
        self.fusion_mode = fusion_mode

    def forward(self, x):
        h, w = x.shape[2:]

        if self.training or self.model_name != "icnet":  # general settings or pspnet
            k_sizes = []
            strides = []
            for pool_size in self.pool_sizes:
                k_sizes.append((int(h / pool_size), int(w / pool_size)))
                strides.append((int(h / pool_size), int(w / pool_size)))
        else:  # eval mode and icnet: pre-trained for 1025 x 2049
            k_sizes = [(8, 15), (13, 25), (17, 33), (33, 65)]
            strides = [(5, 10), (10, 20), (16, 32), (33, 65)]

        if self.fusion_mode == "cat":  # pspnet: concat (including x)
            output_slices = [x]

            for i, (module, pool_size) in enumerate(zip(self.path_module_list, self.pool_sizes)):
                out = F.avg_pool2d(x, k_sizes[i], stride=strides[i], padding=0)
                # out = F.adaptive_avg_pool2d(x, output_size=(pool_size, pool_size))
                if self.model_name != "icnet":
                    out = module(out)
                out = F.interpolate(out, size=(h, w), mode="bilinear", align_corners=True)
                output_slices.append(out)

            return torch.cat(output_slices, dim=1)
        else:  # icnet: element-wise sum (including x)
            pp_sum = x

            for i, (module, pool_size) in enumerate(zip(self.path_module_list, self.pool_sizes)):
                out = F.avg_pool2d(x, k_sizes[i], stride=strides[i], padding=0)
                # out = F.adaptive_avg_pool2d(x, output_size=(pool_size, pool_size))
                if self.model_name != "icnet":
                    out = module(out)
                out = F.interpolate(out, size=(h, w), mode="bilinear", align_corners=True)
                pp_sum = pp_sum + out

            return pp_sum


class bottleNeckPSP(nn.Module):
    def __init__(
        self, in_channels, mid_channels, out_channels, stride, dilation=1, is_batchnorm=True
    ):
        super(bottleNeckPSP, self).__init__()

        bias = not is_batchnorm

        self.cbr1 = conv2DBatchNormRelu(
            in_channels, mid_channels, 1, stride=1, padding=0, bias=bias, is_batchnorm=is_batchnorm
        )
        if dilation > 1:
            self.cbr2 = conv2DBatchNormRelu(
                mid_channels,
                mid_channels,
                3,
                stride=stride,
                padding=dilation,
                bias=bias,
                dilation=dilation,
                is_batchnorm=is_batchnorm,
            )
        else:
            self.cbr2 = conv2DBatchNormRelu(
                mid_channels,
                mid_channels,
                3,
                stride=stride,
                padding=1,
                bias=bias,
                dilation=1,
                is_batchnorm=is_batchnorm,
            )
        self.cb3 = conv2DBatchNorm(
            mid_channels, out_channels, 1, stride=1, padding=0, bias=bias, is_batchnorm=is_batchnorm
        )
        self.cb4 = conv2DBatchNorm(
            in_channels,
            out_channels,
            1,
            stride=stride,
            padding=0,
            bias=bias,
            is_batchnorm=is_batchnorm,
        )

    def forward(self, x):
        conv = self.cb3(self.cbr2(self.cbr1(x)))
        residual = self.cb4(x)
        return F.relu(conv + residual, inplace=True)


class bottleNeckIdentifyPSP(nn.Module):
    def __init__(self, in_channels, mid_channels, stride, dilation=1, is_batchnorm=True):
        super(bottleNeckIdentifyPSP, self).__init__()

        bias = not is_batchnorm

        self.cbr1 = conv2DBatchNormRelu(
            in_channels, mid_channels, 1, stride=1, padding=0, bias=bias, is_batchnorm=is_batchnorm
        )
        if dilation > 1:
            self.cbr2 = conv2DBatchNormRelu(
                mid_channels,
                mid_channels,
                3,
                stride=1,
                padding=dilation,
                bias=bias,
                dilation=dilation,
                is_batchnorm=is_batchnorm,
            )
        else:
            self.cbr2 = conv2DBatchNormRelu(
                mid_channels,
                mid_channels,
                3,
                stride=1,
                padding=1,
                bias=bias,
                dilation=1,
                is_batchnorm=is_batchnorm,
            )
        self.cb3 = conv2DBatchNorm(
            mid_channels, in_channels, 1, stride=1, padding=0, bias=bias, is_batchnorm=is_batchnorm
        )

    def forward(self, x):
        residual = x
        x = self.cb3(self.cbr2(self.cbr1(x)))
        return F.relu(x + residual, inplace=True)


class residualBlockPSP(nn.Module):
    def __init__(
        self,
        n_blocks,
        in_channels,
        mid_channels,
        out_channels,
        stride,
        dilation=1,
        include_range="all",
        is_batchnorm=True,
    ):
        super(residualBlockPSP, self).__init__()

        if dilation > 1:
            stride = 1

        # residualBlockPSP = convBlockPSP + identityBlockPSPs
        layers = []
        if include_range in ["all", "conv"]:
            layers.append(
                bottleNeckPSP(
                    in_channels,
                    mid_channels,
                    out_channels,
                    stride,
                    dilation,
                    is_batchnorm=is_batchnorm,
                )
            )
        if include_range in ["all", "identity"]:
            for i in range(n_blocks - 1):
                layers.append(
                    bottleNeckIdentifyPSP(
                        out_channels, mid_channels, stride, dilation, is_batchnorm=is_batchnorm
                    )
                )

        self.layers = nn.Sequential(*layers)

    def forward(self, x):
        return self.layers(x)


class cascadeFeatureFusion(nn.Module):
    def __init__(
        self, n_classes, low_in_channels, high_in_channels, out_channels, is_batchnorm=True
    ):
        super(cascadeFeatureFusion, self).__init__()

        bias = not is_batchnorm

        self.low_dilated_conv_bn = conv2DBatchNorm(
            low_in_channels,
            out_channels,
            3,
            stride=1,
            padding=2,
            bias=bias,
            dilation=2,
            is_batchnorm=is_batchnorm,
        )
        self.low_classifier_conv = nn.Conv2d(
            int(low_in_channels),
            int(n_classes),
            kernel_size=1,
            padding=0,
            stride=1,
            bias=True,
            dilation=1,
        )  # Train only
        self.high_proj_conv_bn = conv2DBatchNorm(
            high_in_channels,
            out_channels,
            1,
            stride=1,
            padding=0,
            bias=bias,
            is_batchnorm=is_batchnorm,
        )

    def forward(self, x_low, x_high):
        x_low_upsampled = F.interpolate(
            x_low, size=get_interp_size(x_low, z_factor=2), mode="bilinear", align_corners=True
        )

        low_cls = self.low_classifier_conv(x_low_upsampled)

        low_fm = self.low_dilated_conv_bn(x_low_upsampled)
        high_fm = self.high_proj_conv_bn(x_high)
        high_fused_fm = F.relu(low_fm + high_fm, inplace=True)

        return high_fused_fm, low_cls


def get_interp_size(input, s_factor=1, z_factor=1):  # for caffe
    ori_h, ori_w = input.shape[2:]

    # shrink (s_factor >= 1)
    ori_h = (ori_h - 1) / s_factor + 1
    ori_w = (ori_w - 1) / s_factor + 1

    # zoom (z_factor >= 1)
    ori_h = ori_h + (ori_h - 1) * (z_factor - 1)
    ori_w = ori_w + (ori_w - 1) * (z_factor - 1)

    resize_shape = (int(ori_h), int(ori_w))
    return resize_shape


def interp(input, output_size, mode="bilinear"):
    n, c, ih, iw = input.shape
    oh, ow = output_size

    # normalize to [-1, 1]
    h = torch.arange(0, oh, dtype=torch.float, device=input.device) / (oh - 1) * 2 - 1
    w = torch.arange(0, ow, dtype=torch.float, device=input.device) / (ow - 1) * 2 - 1

    grid = torch.zeros(oh, ow, 2, dtype=torch.float, device=input.device)
    grid[:, :, 0] = w.unsqueeze(0).repeat(oh, 1)
    grid[:, :, 1] = h.unsqueeze(0).repeat(ow, 1).transpose(0, 1)
    grid = grid.unsqueeze(0).repeat(n, 1, 1, 1)  # grid.shape: [n, oh, ow, 2]
    grid = Variable(grid)
    if input.is_cuda:
        grid = grid.cuda()

    return F.grid_sample(input, grid, mode=mode)


def get_upsampling_weight(in_channels, out_channels, kernel_size):
    """Make a 2D bilinear kernel suitable for upsampling"""
    factor = (kernel_size + 1) // 2
    if kernel_size % 2 == 1:
        center = factor - 1
    else:
        center = factor - 0.5
    og = np.ogrid[:kernel_size, :kernel_size]
    filt = (1 - abs(og[0] - center) / factor) * (1 - abs(og[1] - center) / factor)
    weight = np.zeros((in_channels, out_channels, kernel_size, kernel_size), dtype=np.float64)
    weight[range(in_channels), range(out_channels), :, :] = filt
    return torch.from_numpy(weight).float()