def forward(self, f, b, mask=None):
        """ Contextual attention layer implementation.
        Contextual attention is first introduced in publication:
            Generative Image Inpainting with Contextual Attention, Yu et al.
        Args:
            f: Input feature to match (foreground).
            b: Input feature for match (background).
            mask: Input mask for b, indicating patches not available.
            ksize: Kernel size for contextual attention.
            stride: Stride for extracting patches from b.
            rate: Dilation for matching.
            softmax_scale: Scaled softmax for attention.
        Returns:
            torch.tensor: output
        """
        # get shapes
        raw_int_fs = list(f.size())   # b*c*h*w
        raw_int_bs = list(b.size())   # b*c*h*w

        # extract patches from background with stride and rate
        kernel = 2 * self.rate
        # raw_w is extracted for reconstruction
        raw_w = extract_image_patches(b, ksizes=[kernel, kernel],
                                      strides=[self.rate*self.stride,
                                               self.rate*self.stride],
                                      rates=[1, 1],
                                      padding='same') # [N, C*k*k, L]
        # raw_shape: [N, C, k, k, L]
        raw_w = raw_w.view(raw_int_bs[0], raw_int_bs[1], kernel, kernel, -1)
        raw_w = raw_w.permute(0, 4, 1, 2, 3)    # raw_shape: [N, L, C, k, k]
        raw_w_groups = torch.split(raw_w, 1, dim=0)

        # downscaling foreground option: downscaling both foreground and
        # background for matching and use original background for reconstruction.
        f = F.interpolate(f, scale_factor=1./self.rate, mode='nearest')
        b = F.interpolate(b, scale_factor=1./self.rate, mode='nearest')
        int_fs = list(f.size())     # b*c*h*w
        int_bs = list(b.size())
        f_groups = torch.split(f, 1, dim=0)  # split tensors along the batch dimension
        # w shape: [N, C*k*k, L]
        w = extract_image_patches(b, ksizes=[self.ksize, self.ksize],
                                  strides=[self.stride, self.stride],
                                  rates=[1, 1],
                                  padding='same')
        # w shape: [N, C, k, k, L]
        w = w.view(int_bs[0], int_bs[1], self.ksize, self.ksize, -1)
        w = w.permute(0, 4, 1, 2, 3)    # w shape: [N, L, C, k, k]
        w_groups = torch.split(w, 1, dim=0)

        # process mask
        if mask is None:
            mask = torch.zeros([int_bs[0], 1, int_bs[2], int_bs[3]])
            if self.use_cuda:
                mask = mask.cuda()
        else:
            mask = F.interpolate(mask, scale_factor=1./(4*self.rate), mode='nearest')
        int_ms = list(mask.size())
        # m shape: [N, C*k*k, L]
        m = extract_image_patches(mask, ksizes=[self.ksize, self.ksize],
                                  strides=[self.stride, self.stride],
                                  rates=[1, 1],
                                  padding='same')
        # m shape: [N, C, k, k, L]
        m = m.view(int_ms[0], int_ms[1], self.ksize, self.ksize, -1)
        m = m.permute(0, 4, 1, 2, 3)    # m shape: [N, L, C, k, k]
        m = m[0]    # m shape: [L, C, k, k]
        # mm shape: [L, 1, 1, 1]
        mm = (reduce_mean(m, axis=[1, 2, 3], keepdim=True)==0.).to(torch.float32)
        mm = mm.permute(1, 0, 2, 3) # mm shape: [1, L, 1, 1]

        y = []
        offsets = []
        k = self.fuse_k
        scale = self.softmax_scale    # to fit the PyTorch tensor image value range
        fuse_weight = torch.eye(k).view(1, 1, k, k)  # 1*1*k*k
        if self.use_cuda:
            fuse_weight = fuse_weight.cuda()

        for xi, wi, raw_wi in zip(f_groups, w_groups, raw_w_groups):
            '''
            O => output channel as a conv filter
            I => input channel as a conv filter
            xi : separated tensor along batch dimension of front; (B=1, C=128, H=32, W=32)
            wi : separated patch tensor along batch dimension of back; (B=1, O=32*32, I=128, KH=3, KW=3)
            raw_wi : separated tensor along batch dimension of back; (B=1, I=32*32, O=128, KH=4, KW=4)
            '''
            # conv for compare
            escape_NaN = torch.FloatTensor([1e-4])
            if self.use_cuda:
                escape_NaN = escape_NaN.cuda()
            wi = wi[0]  # [L, C, k, k]
            max_wi = torch.sqrt(reduce_sum(torch.pow(wi, 2) + escape_NaN, axis=[1, 2, 3], keepdim=True))
            wi_normed = wi / max_wi
            # xi shape: [1, C, H, W], yi shape: [1, L, H, W]
            xi = same_padding(xi, [self.ksize, self.ksize], [1, 1], [1, 1])  # xi: 1*c*H*W
            yi = F.conv2d(xi, wi_normed, stride=1)   # [1, L, H, W]
            # conv implementation for fuse scores to encourage large patches
            if self.fuse:
                # make all of depth to spatial resolution
                yi = yi.view(1, 1, int_bs[2]*int_bs[3], int_fs[2]*int_fs[3])  # (B=1, I=1, H=32*32, W=32*32)
                yi = same_padding(yi, [k, k], [1, 1], [1, 1])
                yi = F.conv2d(yi, fuse_weight, stride=1)  # (B=1, C=1, H=32*32, W=32*32)
                yi = yi.contiguous().view(1, int_bs[2], int_bs[3], int_fs[2], int_fs[3])  # (B=1, 32, 32, 32, 32)
                yi = yi.permute(0, 2, 1, 4, 3)
                yi = yi.contiguous().view(1, 1, int_bs[2]*int_bs[3], int_fs[2]*int_fs[3])
                yi = same_padding(yi, [k, k], [1, 1], [1, 1])
                yi = F.conv2d(yi, fuse_weight, stride=1)
                yi = yi.contiguous().view(1, int_bs[3], int_bs[2], int_fs[3], int_fs[2])
                yi = yi.permute(0, 2, 1, 4, 3).contiguous()
            yi = yi.view(1, int_bs[2] * int_bs[3], int_fs[2], int_fs[3])  # (B=1, C=32*32, H=32, W=32)
            # softmax to match
            yi = yi * mm
            yi = F.softmax(yi*scale, dim=1)
            yi = yi * mm  # [1, L, H, W]

            offset = torch.argmax(yi, dim=1, keepdim=True)  # 1*1*H*W

            if int_bs != int_fs:
                # Normalize the offset value to match foreground dimension
                times = float(int_fs[2] * int_fs[3]) / float(int_bs[2] * int_bs[3])
                offset = ((offset + 1).float() * times - 1).to(torch.int64)
            offset = torch.cat([offset//int_fs[3], offset%int_fs[3]], dim=1)  # 1*2*H*W

            # deconv for patch pasting
            wi_center = raw_wi[0]
            # yi = F.pad(yi, [0, 1, 0, 1])    # here may need conv_transpose same padding
            yi = F.conv_transpose2d(yi, wi_center, stride=self.rate, padding=1) / 4.  # (B=1, C=128, H=64, W=64)
            y.append(yi)
            offsets.append(offset)

        y = torch.cat(y, dim=0)  # back to the mini-batch
        y.contiguous().view(raw_int_fs)

        offsets = torch.cat(offsets, dim=0)
        offsets = offsets.view(int_fs[0], 2, *int_fs[2:])

        # case1: visualize optical flow: minus current position
        h_add = torch.arange(int_fs[2]).view([1, 1, int_fs[2], 1]).expand(int_fs[0], -1, -1, int_fs[3])
        w_add = torch.arange(int_fs[3]).view([1, 1, 1, int_fs[3]]).expand(int_fs[0], -1, int_fs[2], -1)
        ref_coordinate = torch.cat([h_add, w_add], dim=1)
        if self.use_cuda:
            ref_coordinate = ref_coordinate.cuda()

        offsets = offsets - ref_coordinate
        # flow = pt_flow_to_image(offsets)

        flow = torch.from_numpy(flow_to_image(offsets.permute(0, 2, 3, 1).cpu().data.numpy())) / 255.
        flow = flow.permute(0, 3, 1, 2)
        if self.use_cuda:
            flow = flow.cuda()
        # case2: visualize which pixels are attended
        # flow = torch.from_numpy(highlight_flow((offsets * mask.long()).cpu().data.numpy()))

        if self.rate != 1:
            flow = F.interpolate(flow, scale_factor=self.rate*4, mode='nearest')

        return y, flow
 Contextual attention layer implementation.
        Contextual attention is first introduced in publication:
            Generative Image Inpainting with Contextual Attention, Yu et al.
        Args:
            f: Input feature to match (foreground).
            b: Input feature for match (background).
            mask: Input mask for b, indicating patches not available.
            ksize: Kernel size for contextual attention.
            stride: Stride for extracting patches from b.
            rate: Dilation for matching.
            softmax_scale: Scaled softmax for attention.
        Returns:
            torch.tensor: output
        
model/networks.py