import torch import comfy.ops import numpy as np import torch.nn as nn from functools import partial import torch.nn.functional as F from torchvision.ops import deform_conv2d from comfy.ldm.modules.attention import optimized_attention_for_device CXT = [3072, 1536, 768, 384][1:][::-1][-3:] class Attention(nn.Module): def __init__(self, dim, num_heads=8, qkv_bias=False, qk_scale=None, device=None, dtype=None, operations=None): super().__init__() self.dim = dim self.num_heads = num_heads head_dim = dim // num_heads self.scale = qk_scale or head_dim ** -0.5 self.q = operations.Linear(dim, dim, bias=qkv_bias, device=device, dtype=dtype) self.kv = operations.Linear(dim, dim * 2, bias=qkv_bias, device=device, dtype=dtype) self.proj = operations.Linear(dim, dim, device=device, dtype=dtype) def forward(self, x): B, N, C = x.shape optimized_attention = optimized_attention_for_device(x.device, mask=False, small_input=True) q = self.q(x).reshape(B, N, self.num_heads, C // self.num_heads).permute(0, 2, 1, 3) kv = self.kv(x).reshape(B, -1, 2, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4) k, v = kv[0], kv[1] x = optimized_attention( q, k, v, heads=self.num_heads, skip_output_reshape=True, skip_reshape=True ).transpose(1, 2).reshape(B, N, C) x = self.proj(x) return x class Mlp(nn.Module): def __init__(self, in_features, hidden_features=None, out_features=None, device=None, dtype=None, operations=None): super().__init__() out_features = out_features or in_features hidden_features = hidden_features or in_features self.fc1 = operations.Linear(in_features, hidden_features, device=device, dtype=dtype) self.act = nn.GELU() self.fc2 = operations.Linear(hidden_features, out_features, device=device, dtype=dtype) def forward(self, x): x = self.fc1(x) x = self.act(x) x = self.fc2(x) return x def window_partition(x, window_size): B, H, W, C = x.shape x = x.view(B, H // window_size, window_size, W // window_size, window_size, C) windows = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(-1, window_size, window_size, C) return windows def window_reverse(windows, window_size, H, W): B = int(windows.shape[0] / (H * W / window_size / window_size)) x = windows.view(B, H // window_size, W // window_size, window_size, window_size, -1) x = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(B, H, W, -1) return x class WindowAttention(nn.Module): def __init__(self, dim, window_size, num_heads, qkv_bias=True, qk_scale=None, device=None, dtype=None, operations=None): super().__init__() self.dim = dim self.window_size = window_size # Wh, Ww self.num_heads = num_heads head_dim = dim // num_heads self.scale = qk_scale or head_dim ** -0.5 self.relative_position_bias_table = nn.Parameter( torch.zeros((2 * window_size[0] - 1) * (2 * window_size[1] - 1), num_heads, device=device, dtype=dtype)) coords_h = torch.arange(self.window_size[0]) coords_w = torch.arange(self.window_size[1]) coords = torch.stack(torch.meshgrid([coords_h, coords_w], indexing='ij')) # 2, Wh, Ww coords_flatten = torch.flatten(coords, 1) # 2, Wh*Ww relative_coords = coords_flatten[:, :, None] - coords_flatten[:, None, :] # 2, Wh*Ww, Wh*Ww relative_coords = relative_coords.permute(1, 2, 0).contiguous() # Wh*Ww, Wh*Ww, 2 relative_coords[:, :, 0] += self.window_size[0] - 1 relative_coords[:, :, 1] += self.window_size[1] - 1 relative_coords[:, :, 0] *= 2 * self.window_size[1] - 1 relative_position_index = relative_coords.sum(-1) # Wh*Ww, Wh*Ww self.register_buffer("relative_position_index", relative_position_index) self.qkv = operations.Linear(dim, dim * 3, bias=qkv_bias, device=device, dtype=dtype) self.proj = operations.Linear(dim, dim, device=device, dtype=dtype) self.softmax = nn.Softmax(dim=-1) def forward(self, x, mask=None): B_, N, C = x.shape qkv = self.qkv(x).reshape(B_, N, 3, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4) q, k, v = qkv[0], qkv[1], qkv[2] q = q * self.scale attn = (q @ k.transpose(-2, -1)) relative_position_bias = self.relative_position_bias_table[self.relative_position_index.long().view(-1)].view( self.window_size[0] * self.window_size[1], self.window_size[0] * self.window_size[1], -1) # Wh*Ww,Wh*Ww,nH relative_position_bias = comfy.ops.cast_to_input(relative_position_bias.permute(2, 0, 1).contiguous(), attn) # nH, Wh*Ww, Wh*Ww attn = attn + relative_position_bias.unsqueeze(0) if mask is not None: nW = mask.shape[0] attn = attn.view(B_ // nW, nW, self.num_heads, N, N) + mask.unsqueeze(1).unsqueeze(0) attn = attn.view(-1, self.num_heads, N, N) attn = self.softmax(attn) else: attn = self.softmax(attn) x = (attn @ v).transpose(1, 2).reshape(B_, N, C) x = self.proj(x) return x class SwinTransformerBlock(nn.Module): def __init__(self, dim, num_heads, window_size=7, shift_size=0, mlp_ratio=4., qkv_bias=True, qk_scale=None, norm_layer=nn.LayerNorm, device=None, dtype=None, operations=None): super().__init__() self.dim = dim self.num_heads = num_heads self.window_size = window_size self.shift_size = shift_size self.mlp_ratio = mlp_ratio self.norm1 = norm_layer(dim, device=device, dtype=dtype) self.attn = WindowAttention( dim, window_size=(self.window_size, self.window_size), num_heads=num_heads, qkv_bias=qkv_bias, qk_scale=qk_scale, device=device, dtype=dtype, operations=operations) self.norm2 = norm_layer(dim, device=device, dtype=dtype) mlp_hidden_dim = int(dim * mlp_ratio) self.mlp = Mlp(in_features=dim, hidden_features=mlp_hidden_dim, device=device, dtype=dtype, operations=operations) self.H = None self.W = None def forward(self, x, mask_matrix): B, L, C = x.shape H, W = self.H, self.W shortcut = x x = self.norm1(x) x = x.view(B, H, W, C) pad_l = pad_t = 0 pad_r = (self.window_size - W % self.window_size) % self.window_size pad_b = (self.window_size - H % self.window_size) % self.window_size x = F.pad(x, (0, 0, pad_l, pad_r, pad_t, pad_b)) _, Hp, Wp, _ = x.shape if self.shift_size > 0: shifted_x = torch.roll(x, shifts=(-self.shift_size, -self.shift_size), dims=(1, 2)) attn_mask = mask_matrix else: shifted_x = x attn_mask = None x_windows = window_partition(shifted_x, self.window_size) x_windows = x_windows.view(-1, self.window_size * self.window_size, C) attn_windows = self.attn(x_windows, mask=attn_mask) attn_windows = attn_windows.view(-1, self.window_size, self.window_size, C) shifted_x = window_reverse(attn_windows, self.window_size, Hp, Wp) # B H' W' C if self.shift_size > 0: x = torch.roll(shifted_x, shifts=(self.shift_size, self.shift_size), dims=(1, 2)) else: x = shifted_x if pad_r > 0 or pad_b > 0: x = x[:, :H, :W, :].contiguous() x = x.view(B, H * W, C) x = shortcut + x x = x + self.mlp(self.norm2(x)) return x class PatchMerging(nn.Module): def __init__(self, dim, device=None, dtype=None, operations=None): super().__init__() self.dim = dim self.reduction = operations.Linear(4 * dim, 2 * dim, bias=False, device=device, dtype=dtype) self.norm = operations.LayerNorm(4 * dim, device=device, dtype=dtype) def forward(self, x, H, W): B, L, C = x.shape x = x.view(B, H, W, C) # padding pad_input = (H % 2 == 1) or (W % 2 == 1) if pad_input: x = F.pad(x, (0, 0, 0, W % 2, 0, H % 2)) x0 = x[:, 0::2, 0::2, :] # B H/2 W/2 C x1 = x[:, 1::2, 0::2, :] # B H/2 W/2 C x2 = x[:, 0::2, 1::2, :] # B H/2 W/2 C x3 = x[:, 1::2, 1::2, :] # B H/2 W/2 C x = torch.cat([x0, x1, x2, x3], -1) # B H/2 W/2 4*C x = x.view(B, -1, 4 * C) # B H/2*W/2 4*C x = self.norm(x) x = self.reduction(x) return x class BasicLayer(nn.Module): def __init__(self, dim, depth, num_heads, window_size=7, mlp_ratio=4., qkv_bias=True, qk_scale=None, norm_layer=nn.LayerNorm, downsample=None, device=None, dtype=None, operations=None): super().__init__() self.window_size = window_size self.shift_size = window_size // 2 self.depth = depth # build blocks self.blocks = nn.ModuleList([ SwinTransformerBlock( dim=dim, num_heads=num_heads, window_size=window_size, shift_size=0 if (i % 2 == 0) else window_size // 2, mlp_ratio=mlp_ratio, qkv_bias=qkv_bias, qk_scale=qk_scale, norm_layer=norm_layer, device=device, dtype=dtype, operations=operations) for i in range(depth)]) # patch merging layer if downsample is not None: self.downsample = downsample(dim=dim, device=device, dtype=dtype, operations=operations) else: self.downsample = None def forward(self, x, H, W): Hp = int(np.ceil(H / self.window_size)) * self.window_size Wp = int(np.ceil(W / self.window_size)) * self.window_size img_mask = torch.zeros((1, Hp, Wp, 1), device=x.device) # 1 Hp Wp 1 h_slices = (slice(0, -self.window_size), slice(-self.window_size, -self.shift_size), slice(-self.shift_size, None)) w_slices = (slice(0, -self.window_size), slice(-self.window_size, -self.shift_size), slice(-self.shift_size, None)) cnt = 0 for h in h_slices: for w in w_slices: img_mask[:, h, w, :] = cnt cnt += 1 mask_windows = window_partition(img_mask, self.window_size) mask_windows = mask_windows.view(-1, self.window_size * self.window_size) attn_mask = mask_windows.unsqueeze(1) - mask_windows.unsqueeze(2) attn_mask = attn_mask.masked_fill(attn_mask != 0, float(-100.0)).masked_fill(attn_mask == 0, float(0.0)) for blk in self.blocks: blk.H, blk.W = H, W x = blk(x, attn_mask) if self.downsample is not None: x_down = self.downsample(x, H, W) Wh, Ww = (H + 1) // 2, (W + 1) // 2 return x, H, W, x_down, Wh, Ww else: return x, H, W, x, H, W class PatchEmbed(nn.Module): def __init__(self, patch_size=4, in_channels=3, embed_dim=96, norm_layer=None, device=None, dtype=None, operations=None): super().__init__() patch_size = (patch_size, patch_size) self.patch_size = patch_size self.in_channels = in_channels self.embed_dim = embed_dim self.proj = operations.Conv2d(in_channels, embed_dim, kernel_size=patch_size, stride=patch_size, device=device, dtype=dtype) if norm_layer is not None: self.norm = norm_layer(embed_dim, device=device, dtype=dtype) else: self.norm = None def forward(self, x): _, _, H, W = x.size() if W % self.patch_size[1] != 0: x = F.pad(x, (0, self.patch_size[1] - W % self.patch_size[1])) if H % self.patch_size[0] != 0: x = F.pad(x, (0, 0, 0, self.patch_size[0] - H % self.patch_size[0])) x = self.proj(x) # B C Wh Ww if self.norm is not None: Wh, Ww = x.size(2), x.size(3) x = x.flatten(2).transpose(1, 2) x = self.norm(x) x = x.transpose(1, 2).view(-1, self.embed_dim, Wh, Ww) return x class SwinTransformer(nn.Module): def __init__(self, pretrain_img_size=224, patch_size=4, in_channels=3, embed_dim=96, depths=[2, 2, 6, 2], num_heads=[3, 6, 12, 24], window_size=7, mlp_ratio=4., qkv_bias=True, qk_scale=None, patch_norm=True, out_indices=(0, 1, 2, 3), frozen_stages=-1, device=None, dtype=None, operations=None): super().__init__() norm_layer = partial(operations.LayerNorm, device=device, dtype=dtype) self.pretrain_img_size = pretrain_img_size self.num_layers = len(depths) self.embed_dim = embed_dim self.patch_norm = patch_norm self.out_indices = out_indices self.frozen_stages = frozen_stages self.patch_embed = PatchEmbed( patch_size=patch_size, in_channels=in_channels, embed_dim=embed_dim, device=device, dtype=dtype, operations=operations, norm_layer=norm_layer if self.patch_norm else None) self.layers = nn.ModuleList() for i_layer in range(self.num_layers): layer = BasicLayer( dim=int(embed_dim * 2 ** i_layer), depth=depths[i_layer], num_heads=num_heads[i_layer], window_size=window_size, mlp_ratio=mlp_ratio, qkv_bias=qkv_bias, qk_scale=qk_scale, norm_layer=norm_layer, downsample=PatchMerging if (i_layer < self.num_layers - 1) else None, device=device, dtype=dtype, operations=operations) self.layers.append(layer) num_features = [int(embed_dim * 2 ** i) for i in range(self.num_layers)] self.num_features = num_features for i_layer in out_indices: layer = norm_layer(num_features[i_layer]) layer_name = f'norm{i_layer}' self.add_module(layer_name, layer) def forward(self, x): x = self.patch_embed(x) Wh, Ww = x.size(2), x.size(3) outs = [] x = x.flatten(2).transpose(1, 2) for i in range(self.num_layers): layer = self.layers[i] x_out, H, W, x, Wh, Ww = layer(x, Wh, Ww) if i in self.out_indices: norm_layer = getattr(self, f'norm{i}') x_out = norm_layer(x_out) out = x_out.view(-1, H, W, self.num_features[i]).permute(0, 3, 1, 2).contiguous() outs.append(out) return tuple(outs) class DeformableConv2d(nn.Module): def __init__(self, in_channels, out_channels, kernel_size=3, stride=1, padding=1, bias=False, device=None, dtype=None, operations=None): super(DeformableConv2d, self).__init__() kernel_size = kernel_size if type(kernel_size) is tuple else (kernel_size, kernel_size) self.stride = stride if type(stride) is tuple else (stride, stride) self.padding = padding self.offset_conv = operations.Conv2d(in_channels, 2 * kernel_size[0] * kernel_size[1], kernel_size=kernel_size, stride=stride, padding=self.padding, bias=True, device=device, dtype=dtype) self.modulator_conv = operations.Conv2d(in_channels, 1 * kernel_size[0] * kernel_size[1], kernel_size=kernel_size, stride=stride, padding=self.padding, bias=True, device=device, dtype=dtype) self.regular_conv = operations.Conv2d(in_channels, out_channels=out_channels, kernel_size=kernel_size, stride=stride, padding=self.padding, bias=bias, device=device, dtype=dtype) def forward(self, x): offset = self.offset_conv(x) modulator = 2. * torch.sigmoid(self.modulator_conv(x)) weight, bias, offload_info = comfy.ops.cast_bias_weight(self.regular_conv, x, offloadable=True) x = deform_conv2d( input=x, offset=offset, weight=weight, bias=None, padding=self.padding, mask=modulator, stride=self.stride, ) comfy.ops.uncast_bias_weight(self.regular_conv, weight, bias, offload_info) return x class BasicDecBlk(nn.Module): def __init__(self, in_channels=64, out_channels=64, inter_channels=64, device=None, dtype=None, operations=None): super(BasicDecBlk, self).__init__() inter_channels = 64 self.conv_in = operations.Conv2d(in_channels, inter_channels, 3, 1, padding=1, device=device, dtype=dtype) self.relu_in = nn.ReLU(inplace=True) self.dec_att = ASPPDeformable(in_channels=inter_channels, device=device, dtype=dtype, operations=operations) self.conv_out = operations.Conv2d(inter_channels, out_channels, 3, 1, padding=1, device=device, dtype=dtype) self.bn_in = operations.BatchNorm2d(inter_channels, device=device, dtype=dtype) self.bn_out = operations.BatchNorm2d(out_channels, device=device, dtype=dtype) def forward(self, x): x = self.conv_in(x) x = self.bn_in(x) x = self.relu_in(x) x = self.dec_att(x) x = self.conv_out(x) x = self.bn_out(x) return x class BasicLatBlk(nn.Module): def __init__(self, in_channels=64, out_channels=64, device=None, dtype=None, operations=None): super(BasicLatBlk, self).__init__() self.conv = operations.Conv2d(in_channels, out_channels, 1, 1, 0, device=device, dtype=dtype) def forward(self, x): x = self.conv(x) return x class _ASPPModuleDeformable(nn.Module): def __init__(self, in_channels, planes, kernel_size, padding, device, dtype, operations): super(_ASPPModuleDeformable, self).__init__() self.atrous_conv = DeformableConv2d(in_channels, planes, kernel_size=kernel_size, stride=1, padding=padding, bias=False, device=device, dtype=dtype, operations=operations) self.bn = operations.BatchNorm2d(planes, device=device, dtype=dtype) self.relu = nn.ReLU(inplace=True) def forward(self, x): x = self.atrous_conv(x) x = self.bn(x) return self.relu(x) class ASPPDeformable(nn.Module): def __init__(self, in_channels, out_channels=None, parallel_block_sizes=[1, 3, 7], device=None, dtype=None, operations=None): super(ASPPDeformable, self).__init__() self.down_scale = 1 if out_channels is None: out_channels = in_channels self.in_channelster = 256 // self.down_scale self.aspp1 = _ASPPModuleDeformable(in_channels, self.in_channelster, 1, padding=0, device=device, dtype=dtype, operations=operations) self.aspp_deforms = nn.ModuleList([ _ASPPModuleDeformable(in_channels, self.in_channelster, conv_size, padding=int(conv_size//2), device=device, dtype=dtype, operations=operations) for conv_size in parallel_block_sizes ]) self.global_avg_pool = nn.Sequential(nn.AdaptiveAvgPool2d((1, 1)), operations.Conv2d(in_channels, self.in_channelster, 1, stride=1, bias=False, device=device, dtype=dtype), operations.BatchNorm2d(self.in_channelster, device=device, dtype=dtype), nn.ReLU(inplace=True)) self.conv1 = operations.Conv2d(self.in_channelster * (2 + len(self.aspp_deforms)), out_channels, 1, bias=False, device=device, dtype=dtype) self.bn1 = operations.BatchNorm2d(out_channels, device=device, dtype=dtype) self.relu = nn.ReLU(inplace=True) def forward(self, x): x1 = self.aspp1(x) x_aspp_deforms = [aspp_deform(x) for aspp_deform in self.aspp_deforms] x5 = self.global_avg_pool(x) x5 = F.interpolate(x5, size=x1.size()[2:], mode='bilinear', align_corners=True) x = torch.cat((x1, *x_aspp_deforms, x5), dim=1) x = self.conv1(x) x = self.bn1(x) x = self.relu(x) return x class BiRefNet(nn.Module): def __init__(self, config=None, dtype=None, device=None, operations=None): super(BiRefNet, self).__init__() self.bb = SwinTransformer(embed_dim=192, depths=[2, 2, 18, 2], num_heads=[6, 12, 24, 48], window_size=12, device=device, dtype=dtype, operations=operations) channels = [1536, 768, 384, 192] channels = [c * 2 for c in channels] self.cxt = channels[1:][::-1][-3:] self.squeeze_module = nn.Sequential(*[ BasicDecBlk(channels[0]+sum(self.cxt), channels[0], device=device, dtype=dtype, operations=operations) for _ in range(1) ]) self.decoder = Decoder(channels, device=device, dtype=dtype, operations=operations) def forward_enc(self, x): x1, x2, x3, x4 = self.bb(x) B, C, H, W = x.shape x1_, x2_, x3_, x4_ = self.bb(F.interpolate(x, size=(H//2, W//2), mode='bilinear', align_corners=True)) x1 = torch.cat([x1, F.interpolate(x1_, size=x1.shape[2:], mode='bilinear', align_corners=True)], dim=1) x2 = torch.cat([x2, F.interpolate(x2_, size=x2.shape[2:], mode='bilinear', align_corners=True)], dim=1) x3 = torch.cat([x3, F.interpolate(x3_, size=x3.shape[2:], mode='bilinear', align_corners=True)], dim=1) x4 = torch.cat([x4, F.interpolate(x4_, size=x4.shape[2:], mode='bilinear', align_corners=True)], dim=1) x4 = torch.cat( ( *[ F.interpolate(x1, size=x4.shape[2:], mode='bilinear', align_corners=True), F.interpolate(x2, size=x4.shape[2:], mode='bilinear', align_corners=True), F.interpolate(x3, size=x4.shape[2:], mode='bilinear', align_corners=True), ][-len(CXT):], x4 ), dim=1 ) return (x1, x2, x3, x4) def forward_ori(self, x): (x1, x2, x3, x4) = self.forward_enc(x) x4 = self.squeeze_module(x4) features = [x, x1, x2, x3, x4] scaled_preds = self.decoder(features) return scaled_preds def forward(self, pixel_values, intermediate_output=None): scaled_preds = self.forward_ori(pixel_values) return scaled_preds class Decoder(nn.Module): def __init__(self, channels, device, dtype, operations): super(Decoder, self).__init__() # factory kwargs fk = {"device":device, "dtype":dtype, "operations":operations} DecoderBlock = partial(BasicDecBlk, **fk) LateralBlock = partial(BasicLatBlk, **fk) DBlock = partial(SimpleConvs, **fk) self.split = True N_dec_ipt = 64 ic = 64 ipt_cha_opt = 1 self.ipt_blk5 = DBlock(2**10*3 if self.split else 3, [N_dec_ipt, channels[0]//8][ipt_cha_opt], inter_channels=ic) self.ipt_blk4 = DBlock(2**8*3 if self.split else 3, [N_dec_ipt, channels[0]//8][ipt_cha_opt], inter_channels=ic) self.ipt_blk3 = DBlock(2**6*3 if self.split else 3, [N_dec_ipt, channels[1]//8][ipt_cha_opt], inter_channels=ic) self.ipt_blk2 = DBlock(2**4*3 if self.split else 3, [N_dec_ipt, channels[2]//8][ipt_cha_opt], inter_channels=ic) self.ipt_blk1 = DBlock(2**0*3 if self.split else 3, [N_dec_ipt, channels[3]//8][ipt_cha_opt], inter_channels=ic) self.decoder_block4 = DecoderBlock(channels[0]+([N_dec_ipt, channels[0]//8][ipt_cha_opt]), channels[1]) self.decoder_block3 = DecoderBlock(channels[1]+([N_dec_ipt, channels[0]//8][ipt_cha_opt]), channels[2]) self.decoder_block2 = DecoderBlock(channels[2]+([N_dec_ipt, channels[1]//8][ipt_cha_opt]), channels[3]) self.decoder_block1 = DecoderBlock(channels[3]+([N_dec_ipt, channels[2]//8][ipt_cha_opt]), channels[3]//2) fk = {"device":device, "dtype":dtype} self.conv_out1 = nn.Sequential(operations.Conv2d(channels[3]//2+([N_dec_ipt, channels[3]//8][ipt_cha_opt]), 1, 1, 1, 0, **fk)) self.lateral_block4 = LateralBlock(channels[1], channels[1]) self.lateral_block3 = LateralBlock(channels[2], channels[2]) self.lateral_block2 = LateralBlock(channels[3], channels[3]) self.conv_ms_spvn_4 = operations.Conv2d(channels[1], 1, 1, 1, 0, **fk) self.conv_ms_spvn_3 = operations.Conv2d(channels[2], 1, 1, 1, 0, **fk) self.conv_ms_spvn_2 = operations.Conv2d(channels[3], 1, 1, 1, 0, **fk) _N = 16 self.gdt_convs_4 = nn.Sequential(operations.Conv2d(channels[0] // 2, _N, 3, 1, 1, **fk), operations.BatchNorm2d(_N, **fk), nn.ReLU(inplace=True)) self.gdt_convs_3 = nn.Sequential(operations.Conv2d(channels[1] // 2, _N, 3, 1, 1, **fk), operations.BatchNorm2d(_N, **fk), nn.ReLU(inplace=True)) self.gdt_convs_2 = nn.Sequential(operations.Conv2d(channels[2] // 2, _N, 3, 1, 1, **fk), operations.BatchNorm2d(_N, **fk), nn.ReLU(inplace=True)) [setattr(self, f"gdt_convs_pred_{i}", nn.Sequential(operations.Conv2d(_N, 1, 1, 1, 0, **fk))) for i in range(2, 5)] [setattr(self, f"gdt_convs_attn_{i}", nn.Sequential(operations.Conv2d(_N, 1, 1, 1, 0, **fk))) for i in range(2, 5)] def get_patches_batch(self, x, p): _size_h, _size_w = p.shape[2:] patches_batch = [] for idx in range(x.shape[0]): columns_x = torch.split(x[idx], split_size_or_sections=_size_w, dim=-1) patches_x = [] for column_x in columns_x: patches_x += [p.unsqueeze(0) for p in torch.split(column_x, split_size_or_sections=_size_h, dim=-2)] patch_sample = torch.cat(patches_x, dim=1) patches_batch.append(patch_sample) return torch.cat(patches_batch, dim=0) def forward(self, features): x, x1, x2, x3, x4 = features patches_batch = self.get_patches_batch(x, x4) if self.split else x x4 = torch.cat((x4, self.ipt_blk5(F.interpolate(patches_batch, size=x4.shape[2:], mode='bilinear', align_corners=True))), 1) p4 = self.decoder_block4(x4) p4_gdt = self.gdt_convs_4(p4) gdt_attn_4 = self.gdt_convs_attn_4(p4_gdt).sigmoid() p4 = p4 * gdt_attn_4 _p4 = F.interpolate(p4, size=x3.shape[2:], mode='bilinear', align_corners=True) _p3 = _p4 + self.lateral_block4(x3) patches_batch = self.get_patches_batch(x, _p3) if self.split else x _p3 = torch.cat((_p3, self.ipt_blk4(F.interpolate(patches_batch, size=x3.shape[2:], mode='bilinear', align_corners=True))), 1) p3 = self.decoder_block3(_p3) p3_gdt = self.gdt_convs_3(p3) gdt_attn_3 = self.gdt_convs_attn_3(p3_gdt).sigmoid() p3 = p3 * gdt_attn_3 _p3 = F.interpolate(p3, size=x2.shape[2:], mode='bilinear', align_corners=True) _p2 = _p3 + self.lateral_block3(x2) patches_batch = self.get_patches_batch(x, _p2) if self.split else x _p2 = torch.cat((_p2, self.ipt_blk3(F.interpolate(patches_batch, size=x2.shape[2:], mode='bilinear', align_corners=True))), 1) p2 = self.decoder_block2(_p2) p2_gdt = self.gdt_convs_2(p2) gdt_attn_2 = self.gdt_convs_attn_2(p2_gdt).sigmoid() p2 = p2 * gdt_attn_2 _p2 = F.interpolate(p2, size=x1.shape[2:], mode='bilinear', align_corners=True) _p1 = _p2 + self.lateral_block2(x1) patches_batch = self.get_patches_batch(x, _p1) if self.split else x _p1 = torch.cat((_p1, self.ipt_blk2(F.interpolate(patches_batch, size=x1.shape[2:], mode='bilinear', align_corners=True))), 1) _p1 = self.decoder_block1(_p1) _p1 = F.interpolate(_p1, size=x.shape[2:], mode='bilinear', align_corners=True) patches_batch = self.get_patches_batch(x, _p1) if self.split else x _p1 = torch.cat((_p1, self.ipt_blk1(F.interpolate(patches_batch, size=x.shape[2:], mode='bilinear', align_corners=True))), 1) p1_out = self.conv_out1(_p1) return p1_out class SimpleConvs(nn.Module): def __init__( self, in_channels: int, out_channels: int, inter_channels=64, device=None, dtype=None, operations=None ) -> None: super().__init__() self.conv1 = operations.Conv2d(in_channels, inter_channels, 3, 1, 1, device=device, dtype=dtype) self.conv_out = operations.Conv2d(inter_channels, out_channels, 3, 1, 1, device=device, dtype=dtype) def forward(self, x): return self.conv_out(self.conv1(x))