Demosaic/code/lambda_networks/lambda_networks.py

import torch
from torch import nn, einsum
import torch.nn.functional as F
from einops import rearrange

# helpers functions

def exists(val):
    return val is not None

def default(val, d):
    return val if exists(val) else d

# lambda layer

class LambdaLayer(nn.Module):
    def __init__(
        self,
        dim,
        *,
        dim_k,
        n = None,
        r = None,
        heads = 4,
        dim_out = None,
        dim_u = 1,
        norm="batch"):
        super().__init__()
        dim_out = default(dim_out, dim)
        self.u = dim_u # intra-depth dimension
        self.heads = heads

        assert (dim_out % heads) == 0, 'values dimension must be divisible by number of heads for multi-head query'
        dim_v = dim_out // heads

        self.to_q = nn.Conv2d(dim, dim_k * heads, 1, bias = False)
        self.to_k = nn.Conv2d(dim, dim_k * dim_u, 1, bias = False)
        self.to_v = nn.Conv2d(dim, dim_v * dim_u, 1, bias = False)
        if norm=="instance":
            self.norm_q = nn.InstanceNorm2d(dim_k * heads)
            self.norm_v = nn.InstanceNorm2d(dim_v * dim_u)
        else:
            self.norm_q = nn.BatchNorm2d(dim_k * heads)
            self.norm_v = nn.BatchNorm2d(dim_v * dim_u)
        self.local_contexts = exists(r)
        if exists(r):
            assert (r % 2) == 1, 'Receptive kernel size should be odd'
            self.pos_conv = nn.Conv3d(dim_u, dim_k, (1, r, r), padding = (0, r // 2, r // 2))
        else:
            assert exists(n), 'You must specify the total sequence length (h x w)'
            self.pos_emb = nn.Parameter(torch.randn(n, n, dim_k, dim_u))


    def forward(self, x):
        b, c, hh, ww, u, h = *x.shape, self.u, self.heads

        q = self.to_q(x)
        k = self.to_k(x)
        v = self.to_v(x)

        q = self.norm_q(q)
        v = self.norm_v(v)

        q = rearrange(q, 'b (h k) hh ww -> b h k (hh ww)', h = h)
        k = rearrange(k, 'b (u k) hh ww -> b u k (hh ww)', u = u)
        v = rearrange(v, 'b (u v) hh ww -> b u v (hh ww)', u = u)

        k = k.softmax(dim=-1)

        λc = einsum('b u k m, b u v m -> b k v', k, v)
        Yc = einsum('b h k n, b k v -> b h v n', q, λc)

        if self.local_contexts:
            v = rearrange(v, 'b u v (hh ww) -> b u v hh ww', hh = hh, ww = ww)
            λp = self.pos_conv(v)
            Yp = einsum('b h k n, b k v n -> b h v n', q, λp.flatten(3))
        else:
            λp = einsum('n m k u, b u v m -> b n k v', self.pos_emb, v)
            Yp = einsum('b h k n, b n k v -> b h v n', q, λp)

        Y = Yc + Yp
        out = rearrange(Y, 'b h v (hh ww) -> b (h v) hh ww', hh = hh, ww = ww)
        return out


# i'm not sure whether this will work or not
class Recursion(nn.Module):
    def __init__(self, N: int, hidden_dim:int=64):
        super(Recursion,self).__init__()
        self.N = N
        self.lambdaNxN_identity = LambdaLayer(dim=hidden_dim, dim_out=hidden_dim, n=N * N, dim_k=16, heads=2, dim_u=1)
        # merge upstream information here
        self.lambdaNxN_merge = LambdaLayer(dim=2*hidden_dim, dim_out=hidden_dim, n=N * N, dim_k=16, heads=2, dim_u=1)
        self.downscale_conv = nn.Conv2d(hidden_dim, hidden_dim, kernel_size=N, stride=N)
        self.upscale_conv = nn.Conv2d(hidden_dim, hidden_dim * N * N, kernel_size=3,padding=1)
        self.pixel_shuffle = nn.PixelShuffle(N)
    
    def forward(self, x: torch.Tensor):
        N = self.N

        def to_patch(blocks:torch.Tensor)->torch.Tensor:
            shape = blocks.shape
            blocks_patch = F.unfold(blocks, kernel_size=N, stride=N)
            blocks_patch = blocks_patch.view(shape[0], shape[1], N, N, -1)
            num_patch = blocks_patch.shape[-1]
            blocks_patch = blocks_patch.permute(0, 4, 1, 2, 3).reshape(-1, shape[1], N, N).contiguous()
            return blocks_patch, num_patch
        
        def combine_patch(processed_patch,shape,num_patch):
            processed_patch = processed_patch.reshape(shape[0], num_patch, shape[1], N, N)
            processed_patch=processed_patch.permute(0, 2, 3, 4, 1).reshape(shape[0],shape[1] * N * N,num_patch).contiguous()
            processed=F.fold(processed_patch,output_size=(shape[-2],shape[-1]),kernel_size=N,stride=N)
            return processed

        def process(blocks:torch.Tensor)->torch.Tensor:
            shape = blocks.shape
            if blocks.shape[-1] == N:
                processed = self.lambdaNxN_identity(blocks)
                return processed
            # to NxN patchs
            blocks_patch,num_patch=to_patch(blocks)
            # pass through identity
            processed_patch = self.lambdaNxN_identity(blocks_patch)
            # back to HxW
            processed=combine_patch(processed_patch,shape,num_patch)
            # get feedback
            feedback = process(self.downscale_conv(processed))
            # upscale feedback
            upscale_feedback = self.upscale_conv(feedback)
            upscale_feedback=self.pixel_shuffle(upscale_feedback)
            # combine results
            combined = torch.cat([processed, upscale_feedback], dim=1)
            combined_shape=combined.shape
            combined_patch,num_patch=to_patch(combined)
            combined_patch_reduced = self.lambdaNxN_merge(combined_patch)
            ret_shape=(combined_shape[0],combined_shape[1]//2,combined_shape[2],combined_shape[3])
            ret=combine_patch(combined_patch_reduced,ret_shape,num_patch)
            return ret

        return process(x)