diff --git a/README.md b/README.md index b708a4288..ed2be0772 100644 --- a/README.md +++ b/README.md @@ -115,6 +115,7 @@ ChaiNNer currently supports a limited amount of neural network architectures. Mo - [Swin2SR](https://github.com/mv-lab/swin2sr) | [Models](https://github.com/mv-lab/swin2sr/releases/tag/v0.0.1) - [HAT](https://github.com/XPixelGroup/HAT) | [Models](https://drive.google.com/drive/folders/1HpmReFfoUqUbnAOQ7rvOeNU3uf_m69w0) - [Omni-SR](https://github.com/Francis0625/Omni-SR) | [Models](https://github.com/Francis0625/Omni-SR#preparation) +- [SRFormer](https://github.com/HVision-NKU/SRFormer) | [Models](https://github.com/HVision-NKU/SRFormer#pretrain-models) #### Face Restoration diff --git a/backend/src/nodes/impl/pytorch/architecture/LICENSE-SRFormer b/backend/src/nodes/impl/pytorch/architecture/LICENSE-SRFormer new file mode 100644 index 000000000..17bc97bff --- /dev/null +++ b/backend/src/nodes/impl/pytorch/architecture/LICENSE-SRFormer @@ -0,0 +1,163 @@ +## creative commons + +# Attribution-NonCommercial 4.0 International + +Creative Commons Corporation (“Creative Commons”) is not a law firm and does not provide legal services or legal advice. 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For the avoidance of doubt, this paragraph does not form part of the public licenses. +> +> Creative Commons may be contacted at creativecommons.org + +Copyright (c) 2022 MCG-NKU diff --git a/backend/src/nodes/impl/pytorch/architecture/SRFormer.py b/backend/src/nodes/impl/pytorch/architecture/SRFormer.py new file mode 100644 index 000000000..8fd952a31 --- /dev/null +++ b/backend/src/nodes/impl/pytorch/architecture/SRFormer.py @@ -0,0 +1,1371 @@ +# pylint: skip-file +# model: SRFormer +# SRFormer: Permuted Self-Attention for Single Image Super-Resolution + +import math +import re + +import torch +import torch.nn as nn +import torch.nn.functional as F +import torch.utils.checkpoint as checkpoint + +from .timm.helpers import to_2tuple +from .timm.weight_init import trunc_normal_ + + +class emptyModule(nn.Module): + def __init__(self): + super().__init__() + + def forward(self, x): + return x + + +def drop_path(x, drop_prob: float = 0.0, training: bool = False): + """Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks). + + From: https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/layers/drop.py + """ + if drop_prob == 0.0 or not training: + return x + keep_prob = 1 - drop_prob + shape = (x.shape[0],) + (1,) * ( + x.ndim - 1 + ) # work with diff dim tensors, not just 2D ConvNets + random_tensor = keep_prob + torch.rand(shape, dtype=x.dtype, device=x.device) + random_tensor.floor_() # binarize + output = x.div(keep_prob) * random_tensor + return output + + +class DropPath(nn.Module): + """Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks). + + From: https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/layers/drop.py + """ + + def __init__(self, drop_prob=None): + super(DropPath, self).__init__() + self.drop_prob = drop_prob + + def forward(self, x): + return drop_path(x, self.drop_prob, self.training) + + +class dwconv(nn.Module): + def __init__(self, hidden_features): + super(dwconv, self).__init__() + self.depthwise_conv = nn.Sequential( + nn.Conv2d( + hidden_features, + hidden_features, + kernel_size=5, + stride=1, + padding=2, + dilation=1, + groups=hidden_features, + ), + nn.GELU(), + ) + self.hidden_features = hidden_features + + def forward(self, x, x_size): + x = ( + x.transpose(1, 2) + .view(x.shape[0], self.hidden_features, x_size[0], x_size[1]) + .contiguous() + ) # b Ph*Pw c + x = self.depthwise_conv(x) + x = x.flatten(2).transpose(1, 2).contiguous() + return x + + +class ConvFFN(nn.Module): + def __init__( + self, + in_features, + hidden_features=None, + out_features=None, + act_layer=nn.GELU, + drop=0.0, + ): + super().__init__() + out_features = out_features or in_features + hidden_features = hidden_features or in_features + self.fc1 = nn.Linear(in_features, hidden_features) + self.act = act_layer() + self.before_add = emptyModule() + self.after_add = emptyModule() + self.dwconv = dwconv(hidden_features=hidden_features) + self.fc2 = nn.Linear(hidden_features, out_features) + self.drop = nn.Dropout(drop) + + def forward(self, x, x_size): + x = self.fc1(x) + x = self.act(x) + x = self.before_add(x) + x = x + self.dwconv(x, x_size) + x = self.after_add(x) + x = self.drop(x) + x = self.fc2(x) + x = self.drop(x) + return x + + +def window_partition(x, window_size): + """ + Args: + x: (b, h, w, c) + window_size (int): window size + + Returns: + windows: (num_windows*b, window_size, window_size, c) + """ + 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): + """ + Args: + windows: (num_windows*b, window_size, window_size, c) + window_size (int): Window size + h (int): Height of image + w (int): Width of image + + Returns: + x: (b, h, w, c) + """ + 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 PSA(nn.Module): + r"""Window based multi-head self attention (W-MSA) module with relative position bias. + It supports both of shifted and non-shifted window. + + Args: + dim (int): Number of input channels. + window_size (tuple[int]): The height and width of the window. + num_heads (int): Number of attention heads. + qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True + qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set + attn_drop (float, optional): Dropout ratio of attention weight. Default: 0.0 + proj_drop (float, optional): Dropout ratio of output. Default: 0.0 + """ + + def __init__( + self, + dim, + window_size, + num_heads, + qkv_bias=True, + qk_scale=None, + attn_drop=0.0, + proj_drop=0.0, + ): + super().__init__() + self.dim = dim + self.window_size = window_size # Wh, Ww + self.permuted_window_size = (window_size[0] // 2, window_size[1] // 2) + self.num_heads = num_heads + head_dim = dim // num_heads + self.scale = qk_scale or head_dim**-0.5 + + # define a parameter table of relative position bias + self.relative_position_bias_table = nn.Parameter( + torch.zeros( + (2 * self.permuted_window_size[0] - 1) + * (2 * self.permuted_window_size[1] - 1), + num_heads, + ) + ) # 2*Wh-1 * 2*Ww-1, nH + + # get pair-wise aligned relative position index for each token inside the window + coords_h = torch.arange(self.permuted_window_size[0]) + coords_w = torch.arange(self.permuted_window_size[1]) + coords = torch.stack(torch.meshgrid([coords_h, coords_w])) # 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.permuted_window_size[0] - 1 + ) # shift to start from 0 + relative_coords[:, :, 1] += self.permuted_window_size[1] - 1 + relative_coords[:, :, 0] *= 2 * self.permuted_window_size[1] - 1 + aligned_relative_position_index = relative_coords.sum(-1) # Wh*Ww, Wh*Ww + aligned_relative_position_index = ( + aligned_relative_position_index.reshape( + self.permuted_window_size[0], + self.permuted_window_size[1], + 1, + 1, + self.permuted_window_size[0] * self.permuted_window_size[1], + ) + .repeat(1, 1, 2, 2, 1) + .permute(0, 2, 1, 3, 4) + .reshape( + 4 * self.permuted_window_size[0] * self.permuted_window_size[1], + self.permuted_window_size[0] * self.permuted_window_size[1], + ) + ) # FN*FN,WN*WN + self.register_buffer( + "aligned_relative_position_index", aligned_relative_position_index + ) + # compresses the channel dimension of KV + self.kv = nn.Linear(dim, dim // 2, bias=qkv_bias) + self.q = nn.Linear(dim, dim, bias=qkv_bias) + self.attn_drop = nn.Dropout(attn_drop) + self.proj = nn.Linear(dim, dim) + self.proj_drop = nn.Dropout(proj_drop) + + trunc_normal_(self.relative_position_bias_table, std=0.02) + self.softmax = nn.Softmax(dim=-1) + + def forward(self, x, mask=None): + """ + Args: + x: input features with shape of (num_windows*b, n, c) + mask: (0/-inf) mask with shape of (num_windows, Wh*Ww, Wh*Ww) or None + """ + b_, n, c = x.shape + # compress the channel dimension of KV :(num_windows*b, num_heads, n//4, c//num_heads) + kv = ( + self.kv(x) + .reshape( + b_, + self.permuted_window_size[0], + 2, + self.permuted_window_size[1], + 2, + 2, + c // 4, + ) + .permute(0, 1, 3, 5, 2, 4, 6) + .reshape(b_, n // 4, 2, self.num_heads, c // self.num_heads) + .permute(2, 0, 3, 1, 4) + ) + k, v = kv[0], kv[1] + # keep the channel dimension of Q: (num_windows*b, num_heads, n, c//num_heads) + q = ( + self.q(x) + .reshape(b_, n, 1, self.num_heads, c // self.num_heads) + .permute(2, 0, 3, 1, 4)[0] + ) + q = q * self.scale + attn = q @ k.transpose(-2, -1) # (num_windows*b, num_heads, n, n//4) + + relative_position_bias = self.relative_position_bias_table[ + self.aligned_relative_position_index.view(-1) + ].view( + self.window_size[0] * self.window_size[1], + self.permuted_window_size[0] * self.permuted_window_size[1], + -1, + ) # (n, n//4) + relative_position_bias = relative_position_bias.permute( + 2, 0, 1 + ).contiguous() # (num_heads, n, n//4) + 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 // 4) + mask.unsqueeze( + 1 + ).unsqueeze(0) + attn = attn.view(-1, self.num_heads, n, n // 4) + attn = self.softmax(attn) + else: + attn = self.softmax(attn) + + attn = self.attn_drop(attn) + + x = (attn @ v).transpose(1, 2).reshape(b_, n, c) + x = self.proj(x) + x = self.proj_drop(x) + return x + + def extra_repr(self) -> str: + return f"dim={self.dim}, window_size={self.permuted_window_size}, num_heads={self.num_heads}" + + def flops(self, n): + # calculate flops for 1 window with token length of n + flops = 0 + # qkv = self.qkv(x) + flops += n * self.dim * 1.5 * self.dim + # attn = (q @ k.transpose(-2, -1)) + flops += self.num_heads * n * (self.dim // self.num_heads) * n / 4 + # x = (attn @ v) + flops += self.num_heads * n * n / 4 * (self.dim // self.num_heads) + # x = self.proj(x) + flops += n * self.dim * self.dim + return flops + + +class PSA_Block(nn.Module): + r"""Swin Transformer Block. + + Args: + dim (int): Number of input channels. + input_resolution (tuple[int]): Input resolution. + num_heads (int): Number of attention heads. + window_size (int): Window size. + shift_size (int): Shift size for SW-MSA. + mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. + qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True + qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set. + drop (float, optional): Dropout rate. Default: 0.0 + attn_drop (float, optional): Attention dropout rate. Default: 0.0 + drop_path (float, optional): Stochastic depth rate. Default: 0.0 + act_layer (nn.Module, optional): Activation layer. Default: nn.GELU + norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm + """ + + def __init__( + self, + dim, + input_resolution, + num_heads, + window_size=7, + shift_size=0, + mlp_ratio=4.0, + qkv_bias=True, + qk_scale=None, + drop=0.0, + attn_drop=0.0, + drop_path=0.0, + act_layer=nn.GELU, + norm_layer=nn.LayerNorm, + ): + super().__init__() + self.dim = dim + self.input_resolution = input_resolution + self.num_heads = num_heads + self.window_size = window_size + self.permuted_window_size = window_size // 2 + self.shift_size = shift_size + self.mlp_ratio = mlp_ratio + if min(self.input_resolution) <= self.window_size: + # if window size is larger than input resolution, we don't partition windows + self.shift_size = 0 + self.window_size = min(self.input_resolution) + assert ( + 0 <= self.shift_size < self.window_size + ), "shift_size must in 0-window_size" + self.norm1 = norm_layer(dim) + + self.attn = PSA( + dim, + window_size=to_2tuple(self.window_size), + num_heads=num_heads, + qkv_bias=qkv_bias, + qk_scale=qk_scale, + attn_drop=attn_drop, + proj_drop=drop, + ) + + self.drop_path = DropPath(drop_path) if drop_path > 0.0 else nn.Identity() + + self.norm2 = norm_layer(dim) + mlp_hidden_dim = int(dim * mlp_ratio) + self.mlp = ConvFFN( + in_features=dim, + hidden_features=mlp_hidden_dim, + act_layer=act_layer, + drop=drop, + ) + + if self.shift_size > 0: + attn_mask = self.calculate_mask(self.input_resolution) + else: + attn_mask = None + self.register_buffer("attn_mask", attn_mask) + + # emptyModule for Power Spectrum Based Evaluation + self.after_norm1 = emptyModule() + self.after_attention = emptyModule() + self.residual_after_attention = emptyModule() + self.after_norm2 = emptyModule() + self.after_mlp = emptyModule() + self.residual_after_mlp = emptyModule() + + def calculate_mask(self, x_size): + # calculate mask for original windows + h, w = x_size + img_mask = torch.zeros((1, h, w, 1)) # 1 h w 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 + ) # nw, window_size, window_size, 1 + mask_windows = mask_windows.view(-1, self.window_size * self.window_size) + # calculate mask for permuted windows + h, w = x_size + permuted_window_mask = torch.zeros((1, h // 2, w // 2, 1)) # 1 h w 1 + h_slices = ( + slice(0, -self.permuted_window_size), + slice(-self.permuted_window_size, -self.shift_size // 2), + slice(-self.shift_size // 2, None), + ) + w_slices = ( + slice(0, -self.permuted_window_size), + slice(-self.permuted_window_size, -self.shift_size // 2), + slice(-self.shift_size // 2, None), + ) + cnt = 0 + for h in h_slices: + for w in w_slices: + permuted_window_mask[:, h, w, :] = cnt + cnt += 1 + + permuted_windows = window_partition( + permuted_window_mask, self.permuted_window_size + ) + permuted_windows = permuted_windows.view( + -1, self.permuted_window_size * self.permuted_window_size + ) + # calculate attention mask + attn_mask = mask_windows.unsqueeze(2) - permuted_windows.unsqueeze(1) + attn_mask = attn_mask.masked_fill(attn_mask != 0, float(-100.0)).masked_fill( + attn_mask == 0, float(0.0) + ) + + return attn_mask + + def forward(self, x, x_size): + h, w = x_size + b, _, c = x.shape + # assert seq_len == h * w, "input feature has wrong size" + + shortcut = x + x = self.norm1(x) + x = self.after_norm1(x) + x = x.view(b, h, w, c) + + # cyclic shift + if self.shift_size > 0: + shifted_x = torch.roll( + x, shifts=(-self.shift_size, -self.shift_size), dims=(1, 2) + ) + else: + shifted_x = x + + # partition windows + x_windows = window_partition( + shifted_x, self.window_size + ) # nw*b, window_size, window_size, c + x_windows = x_windows.view( + -1, self.window_size * self.window_size, c + ) # nw*b, window_size*window_size, c + + # W-MSA/SW-MSA (to be compatible for testing on images whose shapes are the multiple of window size + if self.input_resolution == x_size: + attn_windows = self.attn( + x_windows, mask=self.attn_mask + ) # nw*b, window_size*window_size, c + else: + attn_windows = self.attn( + x_windows, mask=self.calculate_mask(x_size).to(x.device) + ) + + # merge windows + attn_windows = attn_windows.view(-1, self.window_size, self.window_size, c) + shifted_x = window_reverse(attn_windows, self.window_size, h, w) # b h' w' c + + # reverse cyclic shift + if self.shift_size > 0: + x = torch.roll( + shifted_x, shifts=(self.shift_size, self.shift_size), dims=(1, 2) + ) + else: + x = shifted_x + x = x.view(b, h * w, c) + x = self.after_attention(x) + # FFN + x = shortcut + self.drop_path(x) + x = self.residual_after_attention(x) + x = self.residual_after_mlp( + x + + self.drop_path( + self.after_mlp(self.mlp(self.after_norm2(self.norm2(x)), x_size)) + ) + ) + + return x + + def extra_repr(self) -> str: + return ( + f"dim={self.dim}, input_resolution={self.input_resolution}, num_heads={self.num_heads}, " + f"window_size={self.window_size}, shift_size={self.shift_size}, mlp_ratio={self.mlp_ratio}" + ) + + def flops(self): + flops = 0 + h, w = self.input_resolution + # norm1 + flops += self.dim * h * w + # W-MSA/SW-MSA + nw = h * w / self.window_size / self.window_size + flops += nw * self.attn.flops(self.window_size * self.window_size) + # mlp + flops += 2 * h * w * self.dim * self.dim * self.mlp_ratio + flops += h * w * self.dim * 25 + # norm2 + flops += self.dim * h * w + return flops + + +class PatchMerging(nn.Module): + r"""Patch Merging Layer. + + Args: + input_resolution (tuple[int]): Resolution of input feature. + dim (int): Number of input channels. + norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm + """ + + def __init__(self, input_resolution, dim, norm_layer=nn.LayerNorm): + super().__init__() + self.input_resolution = input_resolution + self.dim = dim + self.reduction = nn.Linear(4 * dim, 2 * dim, bias=False) + self.norm = norm_layer(4 * dim) + + def forward(self, x): + """ + x: b, h*w, c + """ + h, w = self.input_resolution + b, seq_len, c = x.shape + assert seq_len == h * w, "input feature has wrong size" + assert h % 2 == 0 and w % 2 == 0, f"x size ({h}*{w}) are not even." + + x = x.view(b, h, w, c) + + 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 + + def extra_repr(self) -> str: + return f"input_resolution={self.input_resolution}, dim={self.dim}" + + def flops(self): + h, w = self.input_resolution + flops = h * w * self.dim + flops += (h // 2) * (w // 2) * 4 * self.dim * 2 * self.dim + return flops + + +class BasicLayer(nn.Module): + """A basic Swin Transformer layer for one stage. + + Args: + dim (int): Number of input channels. + input_resolution (tuple[int]): Input resolution. + depth (int): Number of blocks. + num_heads (int): Number of attention heads. + window_size (int): Local window size. + mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. + qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True + qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set. + drop (float, optional): Dropout rate. Default: 0.0 + attn_drop (float, optional): Attention dropout rate. Default: 0.0 + drop_path (float | tuple[float], optional): Stochastic depth rate. Default: 0.0 + norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm + downsample (nn.Module | None, optional): Downsample layer at the end of the layer. Default: None + use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False. + """ + + def __init__( + self, + dim, + input_resolution, + depth, + num_heads, + window_size, + mlp_ratio=4.0, + qkv_bias=True, + qk_scale=None, + drop=0.0, + attn_drop=0.0, + drop_path=0.0, + norm_layer=nn.LayerNorm, + downsample=None, + use_checkpoint=False, + ): + super().__init__() + self.dim = dim + self.input_resolution = input_resolution + self.depth = depth + self.use_checkpoint = use_checkpoint + + # build blocks + self.blocks = nn.ModuleList( + [ + PSA_Block( + dim=dim, + input_resolution=input_resolution, + 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, + drop=drop, + attn_drop=attn_drop, + drop_path=drop_path[i] + if isinstance(drop_path, list) + else drop_path, + norm_layer=norm_layer, + ) + for i in range(depth) + ] + ) + + # patch merging layer + if downsample is not None: + self.downsample = downsample( + input_resolution, dim=dim, norm_layer=norm_layer + ) + else: + self.downsample = None + + def forward(self, x, x_size): + for blk in self.blocks: + if self.use_checkpoint: + x = checkpoint.checkpoint(blk, x) + else: + x = blk(x, x_size) + if self.downsample is not None: + x = self.downsample(x) + return x + + def extra_repr(self) -> str: + return f"dim={self.dim}, input_resolution={self.input_resolution}, depth={self.depth}" + + def flops(self): + flops = 0 + for blk in self.blocks: + flops += blk.flops() + if self.downsample is not None: + flops += self.downsample.flops() + return flops + + +class PSA_Group(nn.Module): + """Residual Swin Transformer Block (PSA_Group). + + Args: + dim (int): Number of input channels. + input_resolution (tuple[int]): Input resolution. + depth (int): Number of blocks. + num_heads (int): Number of attention heads. + window_size (int): Local window size. + mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. + qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True + qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set. + drop (float, optional): Dropout rate. Default: 0.0 + attn_drop (float, optional): Attention dropout rate. Default: 0.0 + drop_path (float | tuple[float], optional): Stochastic depth rate. Default: 0.0 + norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm + downsample (nn.Module | None, optional): Downsample layer at the end of the layer. Default: None + use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False. + img_size: Input image size. + patch_size: Patch size. + resi_connection: The convolutional block before residual connection. + """ + + def __init__( + self, + dim, + input_resolution, + depth, + num_heads, + window_size, + mlp_ratio=4.0, + qkv_bias=True, + qk_scale=None, + drop=0.0, + attn_drop=0.0, + drop_path=0.0, + norm_layer=nn.LayerNorm, + downsample=None, + use_checkpoint=False, + img_size=224, + patch_size=4, + resi_connection="1conv", + ): + super(PSA_Group, self).__init__() + + self.dim = dim + self.input_resolution = input_resolution + + self.residual_group = BasicLayer( + dim=dim, + input_resolution=input_resolution, + depth=depth, + num_heads=num_heads, + window_size=window_size, + mlp_ratio=mlp_ratio, + qkv_bias=qkv_bias, + qk_scale=qk_scale, + drop=drop, + attn_drop=attn_drop, + drop_path=drop_path, + norm_layer=norm_layer, + downsample=downsample, + use_checkpoint=use_checkpoint, + ) + + if resi_connection == "1conv": + self.conv = nn.Conv2d(dim, dim, 3, 1, 1) + elif resi_connection == "3conv": + # to save parameters and memory + self.conv = nn.Sequential( + nn.Conv2d(dim, dim // 4, 3, 1, 1), + nn.LeakyReLU(negative_slope=0.2, inplace=True), + nn.Conv2d(dim // 4, dim // 4, 1, 1, 0), + nn.LeakyReLU(negative_slope=0.2, inplace=True), + nn.Conv2d(dim // 4, dim, 3, 1, 1), + ) + + self.patch_embed = PatchEmbed( + img_size=img_size, + patch_size=patch_size, + in_chans=0, + embed_dim=dim, + norm_layer=None, + ) + + self.patch_unembed = PatchUnEmbed( + img_size=img_size, + patch_size=patch_size, + in_chans=0, + embed_dim=dim, + norm_layer=None, + ) + + self.before_PSA_Group_conv = emptyModule() + self.after_PSA_Group_conv = emptyModule() + self.after_PSA_Group_Residual = emptyModule() + + def forward(self, x, x_size): + return self.after_PSA_Group_Residual( + self.after_PSA_Group_conv( + self.patch_embed( + self.conv( + self.patch_unembed( + self.before_PSA_Group_conv(self.residual_group(x, x_size)), + x_size, + ) + ) + ) + ) + + x + ) + + def flops(self): + flops = 0 + flops += self.residual_group.flops() + h, w = self.input_resolution + flops += h * w * self.dim * self.dim * 9 + flops += self.patch_embed.flops() + flops += self.patch_unembed.flops() + + return flops + + +class PatchEmbed(nn.Module): + r"""Image to Patch Embedding + + Args: + img_size (int): Image size. Default: 224. + patch_size (int): Patch token size. Default: 4. + in_chans (int): Number of input image channels. Default: 3. + embed_dim (int): Number of linear projection output channels. Default: 96. + norm_layer (nn.Module, optional): Normalization layer. Default: None + """ + + def __init__( + self, + img_size=224, + window_size=22, + patch_size=4, + in_chans=3, + embed_dim=96, + norm_layer=None, + ): + super().__init__() + if img_size % window_size != 0: + img_size = img_size + (window_size - img_size % window_size) + img_size = to_2tuple(img_size) + patch_size = to_2tuple(patch_size) + patches_resolution = [ + img_size[0] // patch_size[0], + img_size[1] // patch_size[1], + ] + self.img_size = img_size + self.patch_size = patch_size + self.patches_resolution = patches_resolution + self.num_patches = patches_resolution[0] * patches_resolution[1] + + self.in_chans = in_chans + self.embed_dim = embed_dim + + if norm_layer is not None: + self.norm = norm_layer(embed_dim) + else: + self.norm = None + + def forward(self, x): + x = x.flatten(2).transpose(1, 2) # b Ph*Pw c + if self.norm is not None: + x = self.norm(x) + return x + + def flops(self): + flops = 0 + h, w = self.img_size + if self.norm is not None: + flops += h * w * self.embed_dim + return flops + + +class PatchUnEmbed(nn.Module): + r"""Image to Patch Unembedding + + Args: + img_size (int): Image size. Default: 224. + patch_size (int): Patch token size. Default: 4. + in_chans (int): Number of input image channels. Default: 3. + embed_dim (int): Number of linear projection output channels. Default: 96. + norm_layer (nn.Module, optional): Normalization layer. Default: None + """ + + def __init__( + self, + img_size=224, + window_size=24, + patch_size=4, + in_chans=3, + embed_dim=96, + norm_layer=None, + ): + super().__init__() + if img_size % window_size != 0: + img_size = img_size + (window_size - img_size % window_size) + img_size = to_2tuple(img_size) + patch_size = to_2tuple(patch_size) + patches_resolution = [ + img_size[0] // patch_size[0], + img_size[1] // patch_size[1], + ] + self.img_size = img_size + self.patch_size = patch_size + self.patches_resolution = patches_resolution + self.num_patches = patches_resolution[0] * patches_resolution[1] + self.in_chans = in_chans + self.embed_dim = embed_dim + + def forward(self, x, x_size): + x = x.transpose(1, 2).view( + x.shape[0], self.embed_dim, x_size[0], x_size[1] + ) # b Ph*Pw c + return x + + def flops(self): + flops = 0 + return flops + + +class Upsample(nn.Sequential): + """Upsample module. + + Args: + scale (int): Scale factor. Supported scales: 2^n and 3. + num_feat (int): Channel number of intermediate features. + """ + + def __init__(self, scale, num_feat): + m = [] + if (scale & (scale - 1)) == 0: # scale = 2^n + for _ in range(int(math.log(scale, 2))): + m.append(nn.Conv2d(num_feat, 4 * num_feat, 3, 1, 1)) + m.append(nn.PixelShuffle(2)) + elif scale == 3: + m.append(nn.Conv2d(num_feat, 9 * num_feat, 3, 1, 1)) + m.append(nn.PixelShuffle(3)) + else: + raise ValueError( + f"scale {scale} is not supported. Supported scales: 2^n and 3." + ) + super(Upsample, self).__init__(*m) + + +class UpsampleOneStep(nn.Sequential): + """UpsampleOneStep module (the difference with Upsample is that it always only has 1conv + 1pixelshuffle) + Used in lightweight SR to save parameters. + + Args: + scale (int): Scale factor. Supported scales: 2^n and 3. + num_feat (int): Channel number of intermediate features. + + """ + + def __init__(self, scale, num_feat, num_out_ch, input_resolution=None): + self.num_feat = num_feat + self.input_resolution = input_resolution + m = [] + m.append(nn.Conv2d(num_feat, (scale**2) * num_out_ch, 3, 1, 1)) + m.append(nn.PixelShuffle(scale)) + super(UpsampleOneStep, self).__init__(*m) + + def flops(self): + h, w = self.input_resolution + flops = h * w * self.num_feat * 3 * 9 + return flops + + +class SRFormer(nn.Module): + r"""SRFormer + A PyTorch implement of : `SRFormer: Permuted Self-Attention for Single Image Super-Resolution`, based on Swin Transformer. + + Args: + img_size (int | tuple(int)): Input image size. Default 64 + patch_size (int | tuple(int)): Patch size. Default: 1 + in_chans (int): Number of input image channels. Default: 3 + embed_dim (int): Patch embedding dimension. Default: 96 + depths (tuple(int)): Depth of each Swin Transformer layer. + num_heads (tuple(int)): Number of attention heads in different layers. + window_size (int): Window size. Default: 7 + mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. Default: 4 + qkv_bias (bool): If True, add a learnable bias to query, key, value. Default: True + qk_scale (float): Override default qk scale of head_dim ** -0.5 if set. Default: None + drop_rate (float): Dropout rate. Default: 0 + attn_drop_rate (float): Attention dropout rate. Default: 0 + drop_path_rate (float): Stochastic depth rate. Default: 0.1 + norm_layer (nn.Module): Normalization layer. Default: nn.LayerNorm. + ape (bool): If True, add absolute position embedding to the patch embedding. Default: False + patch_norm (bool): If True, add normalization after patch embedding. Default: True + use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False + upscale: Upscale factor. 2/3/4/8 for image SR, 1 for denoising and compress artifact reduction + img_range: Image range. 1. or 255. + upsampler: The reconstruction reconstruction module. 'pixelshuffle'/'pixelshuffledirect'/'nearest+conv'/None + resi_connection: The convolutional block before residual connection. '1conv'/'3conv' + """ + + def __init__(self, state_dict): + super(SRFormer, self).__init__() + + # Default + img_size = 64 + patch_size = 1 + in_chans = 3 + embed_dim = 96 + depths = (6, 6, 6, 6) + num_heads = (6, 6, 6, 6) + window_size = 7 + mlp_ratio = 4.0 + qkv_bias = True + qk_scale = None + drop_rate = 0.0 + attn_drop_rate = 0.0 + drop_path_rate = 0.1 + norm_layer = nn.LayerNorm + ape = False + patch_norm = True + use_checkpoint = False + upscale = 2 + img_range = 1.0 + upsampler = "" + resi_connection = "1conv" + + self.model_arch = "SRFormer" + self.sub_type = "SR" + self.state = state_dict + + state_keys = list(state_dict.keys()) + + if "conv_before_upsample.0.weight" in state_keys: + if "conv_up1.weight" in state_keys: + upsampler = "nearest+conv" + else: + upsampler = "pixelshuffle" + supports_fp16 = False + elif "upsample.0.weight" in state_keys: + upsampler = "pixelshuffledirect" + else: + upsampler = "" + + num_feat = ( + state_dict.get("conv_before_upsample.0.weight", None).shape[1] + if state_dict.get("conv_before_upsample.weight", None) + else 64 + ) + + num_in_ch = state_dict["conv_first.weight"].shape[1] + in_chans = num_in_ch + if "conv_last.weight" in state_keys: + num_out_ch = state_dict["conv_last.weight"].shape[0] + else: + num_out_ch = num_in_ch + + upscale = 1 + if upsampler == "nearest+conv": + upsample_keys = [ + x for x in state_keys if "conv_up" in x and "bias" not in x + ] + + for upsample_key in upsample_keys: + upscale *= 2 + elif upsampler == "pixelshuffle": + upsample_keys = [ + x + for x in state_keys + if "upsample" in x and "conv" not in x and "bias" not in x + ] + for upsample_key in upsample_keys: + shape = state_dict[upsample_key].shape[0] + upscale *= math.sqrt(shape // num_feat) + upscale = int(upscale) + elif upsampler == "pixelshuffledirect": + upscale = int( + math.sqrt(state_dict["upsample.0.bias"].shape[0] // num_out_ch) + ) + + max_layer_num = 0 + max_block_num = 0 + for key in state_keys: + result = re.match( + r"layers.(\d*).residual_group.blocks.(\d*).norm1.weight", key + ) + if result: + layer_num, block_num = result.groups() + max_layer_num = max(max_layer_num, int(layer_num)) + max_block_num = max(max_block_num, int(block_num)) + + depths = [max_block_num + 1 for _ in range(max_layer_num + 1)] + + if ( + "layers.0.residual_group.blocks.0.attn.relative_position_bias_table" + in state_keys + ): + num_heads_num = state_dict[ + "layers.0.residual_group.blocks.0.attn.relative_position_bias_table" + ].shape[-1] + num_heads = [num_heads_num for _ in range(max_layer_num + 1)] + else: + num_heads = depths + + embed_dim = state_dict["conv_first.weight"].shape[0] + + mlp_ratio = float( + state_dict["layers.0.residual_group.blocks.0.mlp.fc1.bias"].shape[0] + / embed_dim + ) + + # TODO: could actually count the layers, but this should do + # TOOD: confirm this is correct and the same as SwinIR + if "layers.0.conv.4.weight" in state_keys: + resi_connection = "3conv" + else: + resi_connection = "1conv" + + window_size = int( + math.sqrt( + state_dict[ + "layers.0.residual_group.blocks.0.attn.aligned_relative_position_index" + ].shape[0] + ) + ) + + if "layers.0.residual_group.blocks.1.attn_mask" in state_keys: + img_size = int( + ( + math.sqrt( + state_dict["layers.0.residual_group.blocks.1.attn_mask"].shape[ + 0 + ] + ) + ) + * 16 + ) + + self.in_nc = num_in_ch + self.out_nc = num_out_ch + self.num_feat = num_feat + self.embed_dim = embed_dim + self.num_heads = num_heads + self.depths = depths + self.window_size = window_size + self.mlp_ratio = mlp_ratio + self.scale = upscale + self.upsampler = upsampler + self.img_size = img_size + self.img_range = img_range + + self.supports_fp16 = False # Too much weirdness to support this at the moment + self.supports_bfp16 = True + self.min_size_restriction = 16 + + num_in_ch = in_chans + num_out_ch = in_chans + num_feat = 64 + self.img_range = img_range + if in_chans == 3: + rgb_mean = (0.4488, 0.4371, 0.4040) + self.mean = torch.Tensor(rgb_mean).view(1, 3, 1, 1) + else: + self.mean = torch.zeros(1, 1, 1, 1) + self.upscale = upscale + self.upsampler = upsampler + self.window_size = window_size + # ------------------------- 1, shallow feature extraction ------------------------- # + self.conv_first = nn.Conv2d(num_in_ch, embed_dim, 3, 1, 1) + + # ------------------------- 2, deep feature extraction ------------------------- # + self.num_layers = len(depths) + self.embed_dim = embed_dim + self.ape = ape + self.patch_norm = patch_norm + self.num_features = embed_dim + self.mlp_ratio = mlp_ratio + + # split image into non-overlapping patches + self.patch_embed = PatchEmbed( + img_size=img_size, + window_size=window_size, + patch_size=patch_size, + in_chans=embed_dim, + embed_dim=embed_dim, + norm_layer=norm_layer if self.patch_norm else None, + ) + num_patches = self.patch_embed.num_patches + patches_resolution = self.patch_embed.patches_resolution + self.patches_resolution = patches_resolution + + # merge non-overlapping patches into image + self.patch_unembed = PatchUnEmbed( + img_size=img_size, + window_size=window_size, + patch_size=patch_size, + in_chans=embed_dim, + embed_dim=embed_dim, + norm_layer=norm_layer if self.patch_norm else None, + ) + + # absolute position embedding + if self.ape: + self.absolute_pos_embed = nn.Parameter( + torch.zeros(1, num_patches, embed_dim) + ) + trunc_normal_(self.absolute_pos_embed, std=0.02) + + self.pos_drop = nn.Dropout(p=drop_rate) + + # stochastic depth + dpr = [ + x.item() for x in torch.linspace(0, drop_path_rate, sum(depths)) + ] # stochastic depth decay rule + + # build Permuted Self Attention Group (PSA_Group) + self.layers = nn.ModuleList() + for i_layer in range(self.num_layers): + layer = PSA_Group( + dim=embed_dim, + input_resolution=(patches_resolution[0], patches_resolution[1]), + depth=depths[i_layer], + num_heads=num_heads[i_layer], + window_size=window_size, + mlp_ratio=self.mlp_ratio, + qkv_bias=qkv_bias, + qk_scale=qk_scale, + drop=drop_rate, + attn_drop=attn_drop_rate, + drop_path=dpr[ + sum(depths[:i_layer]) : sum(depths[: i_layer + 1]) + ], # no impact on SR results + norm_layer=norm_layer, + downsample=None, + use_checkpoint=use_checkpoint, + img_size=img_size, + patch_size=patch_size, + resi_connection=resi_connection, + ) + self.layers.append(layer) + self.norm = norm_layer(self.num_features) + + # build the last conv layer in deep feature extraction + if resi_connection == "1conv": + self.conv_after_body = nn.Conv2d(embed_dim, embed_dim, 3, 1, 1) + elif resi_connection == "3conv": + # to save parameters and memory + self.conv_after_body = nn.Sequential( + nn.Conv2d(embed_dim, embed_dim // 4, 3, 1, 1), + nn.LeakyReLU(negative_slope=0.2, inplace=True), + nn.Conv2d(embed_dim // 4, embed_dim // 4, 1, 1, 0), + nn.LeakyReLU(negative_slope=0.2, inplace=True), + nn.Conv2d(embed_dim // 4, embed_dim, 3, 1, 1), + ) + + # ------------------------- 3, high quality image reconstruction ------------------------- # + if self.upsampler == "pixelshuffle": + # for classical SR + self.conv_before_upsample = nn.Sequential( + nn.Conv2d(embed_dim, num_feat, 3, 1, 1), nn.LeakyReLU(inplace=True) + ) + self.upsample = Upsample(upscale, num_feat) + self.conv_last = nn.Conv2d(num_feat, num_out_ch, 3, 1, 1) + elif self.upsampler == "pixelshuffledirect": + # for lightweight SR (to save parameters) + self.upsample = UpsampleOneStep( + upscale, + embed_dim, + num_out_ch, + (patches_resolution[0], patches_resolution[1]), + ) + elif self.upsampler == "nearest+conv": + # for real-world SR (less artifacts) + assert self.upscale == 4, "only support x4 now." + self.conv_before_upsample = nn.Sequential( + nn.Conv2d(embed_dim, num_feat, 3, 1, 1), nn.LeakyReLU(inplace=True) + ) + self.conv_up1 = nn.Conv2d(num_feat, num_feat, 3, 1, 1) + self.conv_up2 = nn.Conv2d(num_feat, num_feat, 3, 1, 1) + self.conv_hr = nn.Conv2d(num_feat, num_feat, 3, 1, 1) + self.conv_last = nn.Conv2d(num_feat, num_out_ch, 3, 1, 1) + self.lrelu = nn.LeakyReLU(negative_slope=0.2, inplace=True) + else: + # for image denoising and JPEG compression artifact reduction + self.conv_last = nn.Conv2d(embed_dim, num_out_ch, 3, 1, 1) + + self.apply(self._init_weights) + + self.load_state_dict(state_dict, strict=True) + + def _init_weights(self, m): + if isinstance(m, nn.Linear): + trunc_normal_(m.weight, std=0.02) + if isinstance(m, nn.Linear) and m.bias is not None: + nn.init.constant_(m.bias, 0) + elif isinstance(m, nn.LayerNorm): + nn.init.constant_(m.bias, 0) + nn.init.constant_(m.weight, 1.0) + + @torch.jit.ignore + def no_weight_decay(self): + return {"absolute_pos_embed"} + + @torch.jit.ignore + def no_weight_decay_keywords(self): + return {"relative_position_bias_table"} + + def forward_features(self, x): + x_size = (x.shape[2], x.shape[3]) + x = self.patch_embed(x) + if self.ape: + x = x + self.absolute_pos_embed + x = self.pos_drop(x) + + for layer in self.layers: + x = layer(x, x_size) + + x = self.norm(x) # b seq_len c + x = self.patch_unembed(x, x_size) + + return x + + def check_image_size(self, x): + _, _, h, w = x.size() + mod_pad_h = (self.window_size - h % self.window_size) % self.window_size + mod_pad_w = (self.window_size - w % self.window_size) % self.window_size + x = F.pad(x, (0, mod_pad_w, 0, mod_pad_h), "reflect") + return x + + def forward(self, x): + H, W = x.shape[2:] + x = self.check_image_size(x) + self.mean = self.mean.type_as(x) + x = (x - self.mean) * self.img_range + + if self.upsampler == "pixelshuffle": + # for classical SR + x = self.conv_first(x) + x = self.conv_after_body(self.forward_features(x)) + x + x = self.conv_before_upsample(x) + x = self.conv_last(self.upsample(x)) + elif self.upsampler == "pixelshuffledirect": + # for lightweight SR + x = self.conv_first(x) + x = self.conv_after_body(self.forward_features(x)) + x + x = self.upsample(x) + elif self.upsampler == "nearest+conv": + # for real-world SR + x = self.conv_first(x) + x = self.conv_after_body(self.forward_features(x)) + x + x = self.conv_before_upsample(x) + x = self.lrelu( + self.conv_up1( + torch.nn.functional.interpolate(x, scale_factor=2, mode="nearest") + ) + ) + x = self.lrelu( + self.conv_up2( + torch.nn.functional.interpolate(x, scale_factor=2, mode="nearest") + ) + ) + x = self.conv_last(self.lrelu(self.conv_hr(x))) + else: + # for image denoising and JPEG compression artifact reduction + x_first = self.conv_first(x) + res = self.conv_after_body(self.forward_features(x_first)) + x_first + x = x + self.conv_last(res) + + x = x / self.img_range + self.mean + + return x[:, :, : H * self.upscale, : W * self.upscale] + + def flops(self): + flops = 0 + h, w = self.patches_resolution + flops += h * w * 3 * self.embed_dim * 9 + flops += self.patch_embed.flops() + for layer in self.layers: + flops += layer.flops() + flops += h * w * 9 * self.embed_dim * self.embed_dim + flops += self.upsample.flops() + return flops diff --git a/backend/src/nodes/impl/pytorch/model_loading.py b/backend/src/nodes/impl/pytorch/model_loading.py index d46ccd4ae..f1e977cca 100644 --- a/backend/src/nodes/impl/pytorch/model_loading.py +++ b/backend/src/nodes/impl/pytorch/model_loading.py @@ -10,6 +10,7 @@ from .architecture.RRDB import RRDBNet as ESRGAN from .architecture.SCUNet import SCUNet from .architecture.SPSR import SPSRNet as SPSR +from .architecture.SRFormer import SRFormer from .architecture.SRVGG import SRVGGNetCompact as RealESRGANv2 from .architecture.SwiftSRGAN import Generator as SwiftSRGAN from .architecture.Swin2SR import Swin2SR @@ -34,7 +35,6 @@ def load_state_dict(state_dict) -> PyTorchModel: state_dict = state_dict["params"] state_dict_keys = list(state_dict.keys()) - # SRVGGNet Real-ESRGAN (v2) if "body.0.weight" in state_dict_keys and "body.1.weight" in state_dict_keys: model = RealESRGANv2(state_dict) @@ -47,12 +47,19 @@ def load_state_dict(state_dict) -> PyTorchModel: and "initial.cnn.depthwise.weight" in state_dict["model"].keys() ): model = SwiftSRGAN(state_dict) - # HAT -- be sure it is above swinir - elif "layers.0.residual_group.blocks.0.conv_block.cab.0.weight" in state_dict_keys: - model = HAT(state_dict) - # SwinIR + # SwinIR, Swin2SR, SRFormer, HAT elif "layers.0.residual_group.blocks.0.norm1.weight" in state_dict_keys: - if "patch_embed.proj.weight" in state_dict_keys: + if ( + "layers.0.residual_group.blocks.0.attn.aligned_relative_position_index" + in state_dict_keys + ): + model = SRFormer(state_dict) + elif ( + "layers.0.residual_group.blocks.0.conv_block.cab.0.weight" + in state_dict_keys + ): + model = HAT(state_dict) + elif "patch_embed.proj.weight" in state_dict_keys: model = Swin2SR(state_dict) else: model = SwinIR(state_dict) diff --git a/backend/src/nodes/impl/pytorch/types.py b/backend/src/nodes/impl/pytorch/types.py index 7271bb965..7198f8684 100644 --- a/backend/src/nodes/impl/pytorch/types.py +++ b/backend/src/nodes/impl/pytorch/types.py @@ -10,6 +10,7 @@ from .architecture.RRDB import RRDBNet as ESRGAN from .architecture.SCUNet import SCUNet from .architecture.SPSR import SPSRNet as SPSR +from .architecture.SRFormer import SRFormer from .architecture.SRVGG import SRVGGNetCompact as RealESRGANv2 from .architecture.SwiftSRGAN import Generator as SwiftSRGAN from .architecture.Swin2SR import Swin2SR @@ -25,6 +26,7 @@ HAT, OmniSR, SCUNet, + SRFormer, ) PyTorchSRModel = Union[ RealESRGANv2, @@ -36,6 +38,7 @@ HAT, OmniSR, SCUNet, + SRFormer, ] diff --git a/backend/src/nodes/properties/outputs/pytorch_outputs.py b/backend/src/nodes/properties/outputs/pytorch_outputs.py index 621a8e8dc..d6b027959 100644 --- a/backend/src/nodes/properties/outputs/pytorch_outputs.py +++ b/backend/src/nodes/properties/outputs/pytorch_outputs.py @@ -14,6 +14,7 @@ def _get_sizes(value: PyTorchModel) -> List[str]: "SwinIR" in value.model_arch or "Swin2SR" in value.model_arch or "HAT" in value.model_arch + or "SRFormer" in value.model_arch ): head_length = len(value.depths) # type: ignore if head_length <= 4: diff --git a/backend/src/packages/chaiNNer_pytorch/pytorch/io/load_model.py b/backend/src/packages/chaiNNer_pytorch/pytorch/io/load_model.py index e7968209b..a272bf105 100644 --- a/backend/src/packages/chaiNNer_pytorch/pytorch/io/load_model.py +++ b/backend/src/packages/chaiNNer_pytorch/pytorch/io/load_model.py @@ -29,7 +29,7 @@ ( "- For Super-Resolution, we support most variations of the RRDB" " architecture (ESRGAN, Real-ESRGAN, RealSR, BSRGAN, SPSR), Real-ESRGAN's" - " SRVGG architecture, Swift-SRGAN, SwinIR, Swin2SR, HAT, and Omni-SR." + " SRVGG architecture, Swift-SRGAN, SwinIR, Swin2SR, HAT, Omni-SR, and SRFormer." ), ( "- For Face-Restoration, we support GFPGAN (1.2, 1.3, 1.4), RestoreFormer," diff --git a/backend/src/packages/chaiNNer_pytorch/pytorch/utility/convert_to_ncnn.py b/backend/src/packages/chaiNNer_pytorch/pytorch/utility/convert_to_ncnn.py index 582eab663..36ce2006a 100644 --- a/backend/src/packages/chaiNNer_pytorch/pytorch/utility/convert_to_ncnn.py +++ b/backend/src/packages/chaiNNer_pytorch/pytorch/utility/convert_to_ncnn.py @@ -6,6 +6,7 @@ from nodes.impl.pytorch.architecture.HAT import HAT from nodes.impl.pytorch.architecture.OmniSR.OmniSR import OmniSR from nodes.impl.pytorch.architecture.SCUNet import SCUNet +from nodes.impl.pytorch.architecture.SRFormer import SRFormer from nodes.impl.pytorch.architecture.Swin2SR import Swin2SR from nodes.impl.pytorch.architecture.SwinIR import SwinIR from nodes.impl.pytorch.types import PyTorchSRModel @@ -72,6 +73,10 @@ def convert_to_ncnn_node( model, SCUNet ), "SCUNet is not supported for NCNN conversion at this time." + assert not isinstance( + model, SRFormer + ), "SRFormer is not supported for NCNN conversion at this time." + # Intermediate conversion to ONNX is always fp32 onnx_model = convert_to_onnx_node(model, FP_MODE_32)[0] ncnn_model, fp_mode = onnx_convert_to_ncnn_node(onnx_model, is_fp16)