From 39d804ec2cf9236730f76c961631e21d00d23ef5 Mon Sep 17 00:00:00 2001 From: RunDevelopment Date: Sun, 7 Apr 2024 17:53:59 +0200 Subject: [PATCH] Add support for SAFMN_BCIE --- README.md | 2 +- .../spandrel/__helpers/main_registry.py | 2 + .../architectures/SAFMNBCIE/__init__.py | 83 ++++++++ .../architectures/SAFMNBCIE/arch/LICENSE | 201 ++++++++++++++++++ .../SAFMNBCIE/arch/safmn_bcie.py | 149 +++++++++++++ tests/__snapshots__/test_SAFMNBCIE.ambr | 23 ++ tests/images/outputs/jpeg-15/SAFMN_BCIE.png | Bin 0 -> 119850 bytes tests/test_SAFMNBCIE.py | 45 ++++ 8 files changed, 504 insertions(+), 1 deletion(-) create mode 100644 libs/spandrel/spandrel/architectures/SAFMNBCIE/__init__.py create mode 100644 libs/spandrel/spandrel/architectures/SAFMNBCIE/arch/LICENSE create mode 100644 libs/spandrel/spandrel/architectures/SAFMNBCIE/arch/safmn_bcie.py create mode 100644 tests/__snapshots__/test_SAFMNBCIE.ambr create mode 100644 tests/images/outputs/jpeg-15/SAFMN_BCIE.png create mode 100644 tests/test_SAFMNBCIE.py diff --git a/README.md b/README.md index a7cd6958..45ee15ca 100644 --- a/README.md +++ b/README.md @@ -99,7 +99,7 @@ Spandrel currently supports a limited amount of network architectures. If the ar - [SPAN](https://github.com/hongyuanyu/SPAN) | [Models](https://drive.google.com/file/d/1iYUA2TzKuxI0vzmA-UXr_nB43XgPOXUg/view?usp=sharing) - [Real-CUGAN](https://github.com/bilibili/ailab/tree/main/Real-CUGAN) | [Models](https://drive.google.com/drive/folders/1jAJyBf2qKe2povySwsGXsVMnzVyQzqDD), [Pro Models](https://drive.google.com/drive/folders/1hfT4WwnNUaS43ErrgXk0J1R5Ik8s5NVo) - [CRAFT](https://github.com/AVC2-UESTC/CRAFT-SR) | [Models](https://drive.google.com/file/d/13wAmc93BPeBUBQ24zUZOuUpdBFG2aAY5/view?usp=sharing) -- [SAFMN](https://github.com/sunny2109/SAFMN) | [Models](https://drive.google.com/drive/folders/12O_xgwfgc76DsYbiClYnl6ErCDrsi_S9?usp=share_link) +- [SAFMN](https://github.com/sunny2109/SAFMN) | [Models](https://drive.google.com/drive/folders/12O_xgwfgc76DsYbiClYnl6ErCDrsi_S9?usp=share_link), [JPEG model](https://github.com/sunny2109/SAFMN/releases/tag/v0.1.1) - [RGT](https://github.com/zhengchen1999/RGT) | [RGT Models](https://drive.google.com/drive/folders/1zxrr31Kp2D_N9a-OUAPaJEn_yTaSXTfZ?usp=drive_link), [RGT-S Models](https://drive.google.com/drive/folders/1j46WHs1Gvyif1SsZXKy1Y1IrQH0gfIQ1?usp=drive_link) - [DCTLSA](https://github.com/zengkun301/DCTLSA) | [Models](https://github.com/zengkun301/DCTLSA/tree/main/pretrained) - [ATD](https://github.com/LabShuHangGU/Adaptive-Token-Dictionary) | [Models](https://drive.google.com/drive/folders/1D3BvTS1xBcaU1mp50k3pBzUWb7qjRvmB?usp=sharing) diff --git a/libs/spandrel/spandrel/__helpers/main_registry.py b/libs/spandrel/spandrel/__helpers/main_registry.py index 42f51694..4d1e5384 100644 --- a/libs/spandrel/spandrel/__helpers/main_registry.py +++ b/libs/spandrel/spandrel/__helpers/main_registry.py @@ -14,6 +14,7 @@ IPT, RGT, SAFMN, + SAFMNBCIE, SPAN, Compact, DnCNN, @@ -65,6 +66,7 @@ ArchSupport.from_architecture(SPAN.SPANArch()), ArchSupport.from_architecture(RealCUGAN.RealCUGANArch()), ArchSupport.from_architecture(SAFMN.SAFMNArch()), + ArchSupport.from_architecture(SAFMNBCIE.SAFMNBCIEArch()), ArchSupport.from_architecture(DCTLSA.DCTLSAArch()), ArchSupport.from_architecture(FFTformer.FFTformerArch()), ArchSupport.from_architecture(NAFNet.NAFNetArch()), diff --git a/libs/spandrel/spandrel/architectures/SAFMNBCIE/__init__.py b/libs/spandrel/spandrel/architectures/SAFMNBCIE/__init__.py new file mode 100644 index 00000000..590b879f --- /dev/null +++ b/libs/spandrel/spandrel/architectures/SAFMNBCIE/__init__.py @@ -0,0 +1,83 @@ +import math + +from typing_extensions import override + +from spandrel.util import KeyCondition, get_seq_len + +from ...__helpers.model_descriptor import ( + Architecture, + ImageModelDescriptor, + SizeRequirements, + StateDict, +) +from .arch.safmn_bcie import SAFMN_BCIE + + +class SAFMNBCIEArch(Architecture[SAFMN_BCIE]): + def __init__(self) -> None: + super().__init__( + id="SAFMNBCIE", + name="SAFMN BCIE", + detect=KeyCondition.has_all( + "to_feat.1.weight", + "to_feat.1.bias", + "feats.0.layers.0.norm1.weight", + "feats.0.layers.0.norm2.weight", + "feats.0.layers.0.safm.mfr.0.weight", + "feats.0.layers.0.safm.mfr.3.weight", + "feats.0.layers.0.ccm.ccm.0.weight", + "feats.0.layers.0.ccm.ccm.2.weight", + "feats.0.conv.weight", + "feats.0.conv.bias", + "to_img.0.weight", + "to_img.0.bias", + "to_img.2.weight", + "to_img.2.bias", + ), + ) + + @override + def load(self, state_dict: StateDict) -> ImageModelDescriptor[SAFMN_BCIE]: + dim: int + n_blocks: int = 6 + num_layers: int = 6 + ffn_scale: float = 2.0 + upscaling_factor: int = 2 + + dim = state_dict["to_feat.1.weight"].shape[0] + # 3 * upscaling_factor**2 + upscaling_factor = math.isqrt(state_dict["to_feat.1.weight"].shape[1] // 3) + + n_blocks = get_seq_len(state_dict, "feats") + num_layers = get_seq_len(state_dict, "feats.0.layers") + + # hidden_dim = int(dim * ffn_scale) + hidden_dim = state_dict["feats.0.layers.0.ccm.ccm.0.weight"].shape[0] + ffn_scale = hidden_dim / dim + + model = SAFMN_BCIE( + dim=dim, + n_blocks=n_blocks, + num_layers=num_layers, + ffn_scale=ffn_scale, + upscaling_factor=upscaling_factor, + ) + + return ImageModelDescriptor( + model, + state_dict, + architecture=self, + purpose="Restoration", + tags=[ + f"{dim}dim", + f"{num_layers}nl", + f"{n_blocks}nb", + f"{upscaling_factor}uf", + ], + supports_half=False, # TODO: verify + supports_bfloat16=True, + scale=1, + input_channels=3, # hard-coded in the arch + output_channels=3, # hard-coded in the arch + size_requirements=SizeRequirements(multiple_of=16), + ) diff --git a/libs/spandrel/spandrel/architectures/SAFMNBCIE/arch/LICENSE b/libs/spandrel/spandrel/architectures/SAFMNBCIE/arch/LICENSE new file mode 100644 index 00000000..65376df5 --- /dev/null +++ b/libs/spandrel/spandrel/architectures/SAFMNBCIE/arch/LICENSE @@ -0,0 +1,201 @@ + Apache License + Version 2.0, January 2004 + http://www.apache.org/licenses/ + + TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION + + 1. 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b/libs/spandrel/spandrel/architectures/SAFMNBCIE/arch/safmn_bcie.py @@ -0,0 +1,149 @@ +import torch +import torch.nn as nn +import torch.nn.functional as F + +from spandrel.util import store_hyperparameters + + +# Layer Norm +class LayerNorm(nn.Module): + def __init__(self, normalized_shape, eps=1e-6, data_format="channels_first"): + super().__init__() + self.weight = nn.Parameter(torch.ones(normalized_shape)) + self.bias = nn.Parameter(torch.zeros(normalized_shape)) + self.eps = eps + self.data_format = data_format + if self.data_format not in ["channels_last", "channels_first"]: + raise NotImplementedError + self.normalized_shape = (normalized_shape,) + + def forward(self, x): + if self.data_format == "channels_last": + return F.layer_norm( + x, self.normalized_shape, self.weight, self.bias, self.eps + ) + elif self.data_format == "channels_first": + u = x.mean(1, keepdim=True) + s = (x - u).pow(2).mean(1, keepdim=True) + x = (x - u) / torch.sqrt(s + self.eps) + x = self.weight[:, None, None] * x + self.bias[:, None, None] + return x + + +# CCM +class CCM(nn.Module): + def __init__(self, dim, growth_rate=2.0): + super().__init__() + hidden_dim = int(dim * growth_rate) + + self.ccm = nn.Sequential( + nn.Conv2d(dim, hidden_dim, 3, 1, 1), + nn.GELU(), + nn.Conv2d(hidden_dim, dim, 1, 1, 0), + ) + + def forward(self, x): + return self.ccm(x) + + +# SAFM +class SAFM(nn.Module): + def __init__(self, dim, n_levels=4): + super().__init__() + self.n_levels = n_levels + chunk_dim = dim // n_levels + + # Spatial Weighting + self.mfr = nn.ModuleList( + [ + nn.Conv2d(chunk_dim, chunk_dim, 3, 1, 1, groups=chunk_dim) + for _ in range(self.n_levels) + ] + ) + + # # Feature Aggregation + self.aggr = nn.Conv2d(dim, dim, 1, 1, 0) + + # Activation + self.act = nn.GELU() + + def forward(self, x): + h, w = x.size()[-2:] + + xc = x.chunk(self.n_levels, dim=1) + out = [] + for i in range(self.n_levels): + if i > 0: + p_size = (h // 2**i, w // 2**i) + s = F.adaptive_max_pool2d(xc[i], p_size) + s = self.mfr[i](s) + s = F.interpolate(s, size=(h, w), mode="nearest") + else: + s = self.mfr[i](xc[i]) + out.append(s) + + out = self.aggr(torch.cat(out, dim=1)) + out = self.act(out) * x + return out + + +class AttBlock(nn.Module): + def __init__(self, dim, ffn_scale=2.0): + super().__init__() + + self.norm1 = LayerNorm(dim) + self.norm2 = LayerNorm(dim) + + # Multiscale Block + self.safm = SAFM(dim) + # Feedforward layer + self.ccm = CCM(dim, ffn_scale) + + def forward(self, x): + x = self.safm(self.norm1(x)) + x + x = self.ccm(self.norm2(x)) + x + return x + + +class BasicLayer(nn.Module): + def __init__(self, num_layer, dim, ffn_scale=2.0): + super().__init__() + + self.layers = nn.Sequential( + *[AttBlock(dim, ffn_scale) for _ in range(num_layer)] + ) + self.conv = nn.Conv2d(dim, dim, 3, 1, 1) + + def forward(self, x): + return self.conv(self.layers(x)) + x + + +@store_hyperparameters() +class SAFMN_BCIE(nn.Module): + hyperparameters = {} + + def __init__( + self, dim: int, n_blocks=6, num_layers=6, ffn_scale=2.0, upscaling_factor=2 + ): + super().__init__() + + self.to_feat = nn.Sequential( + nn.PixelUnshuffle(upscaling_factor), + nn.Conv2d(3 * upscaling_factor**2, dim, 3, 1, 1), + ) + + self.feats = nn.Sequential( + *[BasicLayer(num_layers, dim, ffn_scale) for _ in range(n_blocks)] + ) + + self.to_img = nn.Sequential( + nn.Conv2d(dim, dim * upscaling_factor**2, 3, 1, 1), + nn.PixelShuffle(upscaling_factor), + nn.Conv2d(dim, 3, 3, 1, 1), + ) + + def forward(self, x): + x = self.to_feat(x) + x = self.feats(x) + x + x = self.to_img(x) + return x diff --git a/tests/__snapshots__/test_SAFMNBCIE.ambr b/tests/__snapshots__/test_SAFMNBCIE.ambr new file mode 100644 index 00000000..d0145a1a --- /dev/null +++ b/tests/__snapshots__/test_SAFMNBCIE.ambr @@ -0,0 +1,23 @@ +# serializer version: 1 +# name: test_SAFMN_BCIE + ImageModelDescriptor( + architecture=SAFMNBCIEArch( + id='SAFMNBCIE', + name='SAFMN BCIE', + ), + input_channels=3, + output_channels=3, + 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