From 1fa4dcdafa44cbd9ef4ceb0fe6c84237fa5584ce Mon Sep 17 00:00:00 2001 From: RunDevelopment Date: Wed, 29 May 2024 16:27:57 +0200 Subject: [PATCH] Add support for HVI-CIDNet --- README.md | 1 + .../spandrel/__helpers/main_registry.py | 2 + .../architectures/HVICIDNet/__init__.py | 94 ++++++++++++ .../HVICIDNet/arch/HVI_transform.py | 135 ++++++++++++++++++ .../architectures/HVICIDNet/arch/LCA.py | 133 +++++++++++++++++ .../architectures/HVICIDNet/arch/LICENSE | 21 +++ .../architectures/HVICIDNet/arch/cidnet.py | 131 +++++++++++++++++ .../HVICIDNet/arch/transformer_utils.py | 81 +++++++++++ tests/__snapshots__/test_HVICIDNet.ambr | 19 +++ .../HVI-CIDNet_lolv1_w_perc.png | Bin 0 -> 194966 bytes tests/test_HVICIDNet.py | 46 ++++++ 11 files changed, 663 insertions(+) create mode 100644 libs/spandrel/spandrel/architectures/HVICIDNet/__init__.py create mode 100644 libs/spandrel/spandrel/architectures/HVICIDNet/arch/HVI_transform.py create mode 100644 libs/spandrel/spandrel/architectures/HVICIDNet/arch/LCA.py create mode 100644 libs/spandrel/spandrel/architectures/HVICIDNet/arch/LICENSE create mode 100644 libs/spandrel/spandrel/architectures/HVICIDNet/arch/cidnet.py create mode 100644 libs/spandrel/spandrel/architectures/HVICIDNet/arch/transformer_utils.py create mode 100644 tests/__snapshots__/test_HVICIDNet.ambr create mode 100644 tests/images/outputs/low-light-FiveK-a4501-DSC_0354/HVI-CIDNet_lolv1_w_perc.png create mode 100644 tests/test_HVICIDNet.py diff --git a/README.md b/README.md index 28434194..60f58098 100644 --- a/README.md +++ b/README.md @@ -144,6 +144,7 @@ Spandrel currently supports a limited amount of network architectures. If the ar #### Dehazing - [MixDehazeNet](https://github.com/AmeryXiong/MixDehazeNet) | [Models](https://drive.google.com/drive/folders/1ep6W4H3vNxshYjq71Tb3MzxrXGgaiM6C?usp=drive_link) +- [HVI-CIDNet](https://github.com/Fediory/HVI-CIDNet) | [Models](https://github.com/Fediory/HVI-CIDNet/tree/master?tab=readme-ov-file#weights-and-results-) #### Low-light Enhancement diff --git a/libs/spandrel/spandrel/__helpers/main_registry.py b/libs/spandrel/spandrel/__helpers/main_registry.py index c2bf8222..fccbd8c9 100644 --- a/libs/spandrel/spandrel/__helpers/main_registry.py +++ b/libs/spandrel/spandrel/__helpers/main_registry.py @@ -22,6 +22,7 @@ DnCNN, DRUNet, FFTformer, + HVICIDNet, KBNet, LaMa, MixDehazeNet, @@ -82,4 +83,5 @@ ArchSupport.from_architecture(ESRGAN.ESRGANArch()), ArchSupport.from_architecture(PLKSR.PLKSRArch()), ArchSupport.from_architecture(RetinexFormer.RetinexFormerArch()), + ArchSupport.from_architecture(HVICIDNet.HVICIDNetArch()), ) diff --git a/libs/spandrel/spandrel/architectures/HVICIDNet/__init__.py b/libs/spandrel/spandrel/architectures/HVICIDNet/__init__.py new file mode 100644 index 00000000..b880ca9d --- /dev/null +++ b/libs/spandrel/spandrel/architectures/HVICIDNet/__init__.py @@ -0,0 +1,94 @@ +from typing_extensions import override + +from spandrel.util import KeyCondition + +from ...__helpers.model_descriptor import ( + Architecture, + ImageModelDescriptor, + ModelTiling, + SizeRequirements, + StateDict, +) +from .arch.cidnet import CIDNet as HVICIDNet + + +class HVICIDNetArch(Architecture[HVICIDNet]): + def __init__(self) -> None: + super().__init__( + id="HVICIDNet", + name="HVI-CIDNet", + detect=KeyCondition.has_all( + "HVE_block0.1.weight", + "HVE_block1.prelu.weight", + "HVE_block1.down.0.weight", + "HVE_block3.down.0.weight", + "HVD_block3.prelu.weight", + "HVD_block3.up_scale.0.weight", + "HVD_block3.up.weight", + "HVD_block1.up.weight", + "HVD_block0.1.weight", + "IE_block0.1.weight", + "IE_block1.prelu.weight", + "IE_block1.down.0.weight", + "ID_block1.up.weight", + "ID_block0.1.weight", + "HV_LCA1.gdfn.project_in.weight", + "HV_LCA1.gdfn.dwconv.weight", + "HV_LCA1.gdfn.dwconv1.weight", + "HV_LCA1.gdfn.dwconv2.weight", + "HV_LCA1.gdfn.project_out.weight", + "HV_LCA1.norm.weight", + "HV_LCA1.ffn.temperature", + "HV_LCA1.ffn.q.weight", + "HV_LCA1.ffn.q_dwconv.weight", + "HV_LCA1.ffn.project_out.weight", + "HV_LCA2.gdfn.project_in.weight", + "HV_LCA6.gdfn.project_in.weight", + "I_LCA1.gdfn.project_in.weight", + "I_LCA6.ffn.project_out.weight", + "trans.density_k", + ), + ) + + @override + def load(self, state_dict: StateDict) -> ImageModelDescriptor[HVICIDNet]: + channels = [36, 36, 72, 144] + heads = [1, 2, 4, 8] + norm = False + + channels = [ + state_dict["HVE_block1.down.0.weight"].shape[1], + state_dict["HVE_block1.down.0.weight"].shape[0], + state_dict["HVE_block2.down.0.weight"].shape[0], + state_dict["HVE_block3.down.0.weight"].shape[0], + ] + + heads = [ + 1, # unused + state_dict["HV_LCA1.ffn.temperature"].shape[0], + state_dict["HV_LCA2.ffn.temperature"].shape[0], + state_dict["HV_LCA3.ffn.temperature"].shape[0], + ] + + norm = "HVE_block1.norm.weight" in state_dict + + model = HVICIDNet( + channels=channels, + heads=heads, + norm=norm, + ) + + return ImageModelDescriptor( + model, + state_dict, + architecture=self, + purpose="Restoration", + tags=[], + supports_half=False, # TODO: verify + supports_bfloat16=True, + scale=1, + input_channels=3, # hard-coded + output_channels=3, # hard-coded + size_requirements=SizeRequirements(multiple_of=8), + tiling=ModelTiling.DISCOURAGED, + ) diff --git a/libs/spandrel/spandrel/architectures/HVICIDNet/arch/HVI_transform.py b/libs/spandrel/spandrel/architectures/HVICIDNet/arch/HVI_transform.py new file mode 100644 index 00000000..fb9d61fd --- /dev/null +++ b/libs/spandrel/spandrel/architectures/HVICIDNet/arch/HVI_transform.py @@ -0,0 +1,135 @@ +from math import pi + +import torch +import torch.nn as nn + + +class RGB_HVI(nn.Module): + def __init__(self): + super().__init__() + self.density_k = torch.nn.Parameter( + torch.full([1], 0.2) + ) # k is reciprocal to the paper mentioned + self.gated = False + self.gated2 = False + self.alpha = 1.0 + self.this_k = 0 + + def HVIT(self, img): + eps = 1e-8 + device = img.device + dtypes = img.dtype + hue = ( + torch.Tensor(img.shape[0], img.shape[2], img.shape[3]).to(device).to(dtypes) + ) + value = img.max(1)[0].to(dtypes) + img_min = img.min(1)[0].to(dtypes) + hue[img[:, 2] == value] = ( + 4.0 + + ((img[:, 0] - img[:, 1]) / (value - img_min + eps))[img[:, 2] == value] + ) + hue[img[:, 1] == value] = ( + 2.0 + + ((img[:, 2] - img[:, 0]) / (value - img_min + eps))[img[:, 1] == value] + ) + hue[img[:, 0] == value] = ( + 0.0 + + ((img[:, 1] - img[:, 2]) / (value - img_min + eps))[img[:, 0] == value] + ) % 6 + + hue[img.min(1)[0] == value] = 0.0 + hue = hue / 6.0 + + saturation = (value - img_min) / (value + eps) + saturation[value == 0] = 0 + + hue = hue.unsqueeze(1) + saturation = saturation.unsqueeze(1) + value = value.unsqueeze(1) + + k = self.density_k + self.this_k = k.item() + + color_sensitive = ((value * 0.5 * pi).sin() + eps).pow(k) + cx = (2.0 * pi * hue).cos() + cy = (2.0 * pi * hue).sin() + X = color_sensitive * saturation * cx + Y = color_sensitive * saturation * cy + Z = value + xyz = torch.cat([X, Y, Z], dim=1) + return xyz + + def PHVIT(self, img): + eps = 1e-8 + H, V, I = img[:, 0, :, :], img[:, 1, :, :], img[:, 2, :, :] # noqa: E741 + + # clip + H = torch.clamp(H, -1, 1) + V = torch.clamp(V, -1, 1) + I = torch.clamp(I, 0, 1) # noqa: E741 + + v = I + k = self.this_k + color_sensitive = ((v * 0.5 * pi).sin() + eps).pow(k) + H = (H) / (color_sensitive + eps) + V = (V) / (color_sensitive + eps) + H = torch.clamp(H, -1, 1) + V = torch.clamp(V, -1, 1) + h = torch.atan2(V, H) / (2 * pi) + h = h % 1 + s = torch.sqrt(H**2 + V**2) + + if self.gated: + s = s * 1.3 + + s = torch.clamp(s, 0, 1) + v = torch.clamp(v, 0, 1) + + r = torch.zeros_like(h) + g = torch.zeros_like(h) + b = torch.zeros_like(h) + + hi = torch.floor(h * 6.0) + f = h * 6.0 - hi + p = v * (1.0 - s) + q = v * (1.0 - (f * s)) + t = v * (1.0 - ((1.0 - f) * s)) + + hi0 = hi == 0 + hi1 = hi == 1 + hi2 = hi == 2 + hi3 = hi == 3 + hi4 = hi == 4 + hi5 = hi == 5 + + r[hi0] = v[hi0] + g[hi0] = t[hi0] + b[hi0] = p[hi0] + + r[hi1] = q[hi1] + g[hi1] = v[hi1] + b[hi1] = p[hi1] + + r[hi2] = p[hi2] + g[hi2] = v[hi2] + b[hi2] = t[hi2] + + r[hi3] = p[hi3] + g[hi3] = q[hi3] + b[hi3] = v[hi3] + + r[hi4] = t[hi4] + g[hi4] = p[hi4] + b[hi4] = v[hi4] + + r[hi5] = v[hi5] + g[hi5] = p[hi5] + b[hi5] = q[hi5] + + r = r.unsqueeze(1) + g = g.unsqueeze(1) + b = b.unsqueeze(1) + rgb = torch.cat([r, g, b], dim=1) + if self.gated2: + rgb = rgb * self.alpha + return rgb diff --git a/libs/spandrel/spandrel/architectures/HVICIDNet/arch/LCA.py b/libs/spandrel/spandrel/architectures/HVICIDNet/arch/LCA.py new file mode 100644 index 00000000..9bba15d7 --- /dev/null +++ b/libs/spandrel/spandrel/architectures/HVICIDNet/arch/LCA.py @@ -0,0 +1,133 @@ +import torch +import torch.nn as nn +from einops import rearrange + +from .transformer_utils import LayerNorm + + +# Cross Attention Block +class CAB(nn.Module): + def __init__(self, dim, num_heads, bias): + super().__init__() + self.num_heads = num_heads + self.temperature = nn.Parameter(torch.ones(num_heads, 1, 1)) + + self.q = nn.Conv2d(dim, dim, kernel_size=1, bias=bias) + self.q_dwconv = nn.Conv2d( + dim, dim, kernel_size=3, stride=1, padding=1, groups=dim, bias=bias + ) + self.kv = nn.Conv2d(dim, dim * 2, kernel_size=1, bias=bias) + self.kv_dwconv = nn.Conv2d( + dim * 2, + dim * 2, + kernel_size=3, + stride=1, + padding=1, + groups=dim * 2, + bias=bias, + ) + self.project_out = nn.Conv2d(dim, dim, kernel_size=1, bias=bias) + + def forward(self, x, y): + _, _, h, w = x.shape + + q = self.q_dwconv(self.q(x)) + kv = self.kv_dwconv(self.kv(y)) + k, v = kv.chunk(2, dim=1) + + q = rearrange(q, "b (head c) h w -> b head c (h w)", head=self.num_heads) + k = rearrange(k, "b (head c) h w -> b head c (h w)", head=self.num_heads) + v = rearrange(v, "b (head c) h w -> b head c (h w)", head=self.num_heads) + + q = torch.nn.functional.normalize(q, dim=-1) + k = torch.nn.functional.normalize(k, dim=-1) + + attn = (q @ k.transpose(-2, -1)) * self.temperature + attn = attn.softmax(dim=-1) + + out = attn @ v + + out = rearrange( + out, "b head c (h w) -> b (head c) h w", head=self.num_heads, h=h, w=w + ) + + out = self.project_out(out) + return out + + +# Intensity Enhancement Layer +class IEL(nn.Module): + def __init__(self, dim, ffn_expansion_factor=2.66, bias=False): + super().__init__() + + hidden_features = int(dim * ffn_expansion_factor) + + self.project_in = nn.Conv2d(dim, hidden_features * 2, kernel_size=1, bias=bias) + + self.dwconv = nn.Conv2d( + hidden_features * 2, + hidden_features * 2, + kernel_size=3, + stride=1, + padding=1, + groups=hidden_features * 2, + bias=bias, + ) + self.dwconv1 = nn.Conv2d( + hidden_features, + hidden_features, + kernel_size=3, + stride=1, + padding=1, + groups=hidden_features, + bias=bias, + ) + self.dwconv2 = nn.Conv2d( + hidden_features, + hidden_features, + kernel_size=3, + stride=1, + padding=1, + groups=hidden_features, + bias=bias, + ) + + self.project_out = nn.Conv2d(hidden_features, dim, kernel_size=1, bias=bias) + + self.Tanh = nn.Tanh() + + def forward(self, x): + x = self.project_in(x) + x1, x2 = self.dwconv(x).chunk(2, dim=1) + x1 = self.Tanh(self.dwconv1(x1)) + x1 + x2 = self.Tanh(self.dwconv2(x2)) + x2 + x = x1 * x2 + x = self.project_out(x) + return x + + +# Lightweight Cross Attention +class HV_LCA(nn.Module): + def __init__(self, dim, num_heads, bias=False): + super().__init__() + self.gdfn = IEL(dim) # IEL and CDL have same structure + self.norm = LayerNorm(dim) + self.ffn = CAB(dim, num_heads, bias) + + def forward(self, x, y): + x = x + self.ffn(self.norm(x), self.norm(y)) + x = self.gdfn(self.norm(x)) + return x + + +class I_LCA(nn.Module): + def __init__(self, dim, num_heads, bias=False): + super().__init__() + self.norm = LayerNorm(dim) + self.gdfn = IEL(dim) + self.ffn = CAB(dim, num_heads, bias=bias) + + def forward(self, x, y): + x = x + self.ffn(self.norm(x), self.norm(y)) + x = x + self.gdfn(self.norm(x)) + return x diff --git a/libs/spandrel/spandrel/architectures/HVICIDNet/arch/LICENSE b/libs/spandrel/spandrel/architectures/HVICIDNet/arch/LICENSE new file mode 100644 index 00000000..1742f8a4 --- /dev/null +++ b/libs/spandrel/spandrel/architectures/HVICIDNet/arch/LICENSE @@ -0,0 +1,21 @@ +MIT License + +Copyright (c) 2024 Fediory Feng + +Permission is hereby granted, free of charge, to any person obtaining a copy +of this software and associated documentation files (the "Software"), to deal +in the Software without restriction, including without limitation the rights +to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +copies of the Software, and to permit persons to whom the Software is +furnished to do so, subject to the following conditions: + +The above copyright notice and this permission notice shall be included in all +copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +SOFTWARE. diff --git a/libs/spandrel/spandrel/architectures/HVICIDNet/arch/cidnet.py b/libs/spandrel/spandrel/architectures/HVICIDNet/arch/cidnet.py new file mode 100644 index 00000000..f44a7fe8 --- /dev/null +++ b/libs/spandrel/spandrel/architectures/HVICIDNet/arch/cidnet.py @@ -0,0 +1,131 @@ +import torch +import torch.nn as nn + +from spandrel.util import store_hyperparameters + +from .HVI_transform import RGB_HVI +from .LCA import HV_LCA, I_LCA +from .transformer_utils import NormDownsample, NormUpsample + + +@store_hyperparameters() +class CIDNet(nn.Module): + hyperparameters = {} + + def __init__( + self, + channels=[36, 36, 72, 144], + heads=[1, 2, 4, 8], + norm=False, + ): + super().__init__() + + [ch1, ch2, ch3, ch4] = channels + [_head1, head2, head3, head4] = heads + + # HV_ways + self.HVE_block0 = nn.Sequential( + nn.ReplicationPad2d(1), + nn.Conv2d(3, ch1, 3, stride=1, padding=0, bias=False), + ) + self.HVE_block1 = NormDownsample(ch1, ch2, use_norm=norm) + self.HVE_block2 = NormDownsample(ch2, ch3, use_norm=norm) + self.HVE_block3 = NormDownsample(ch3, ch4, use_norm=norm) + + self.HVD_block3 = NormUpsample(ch4, ch3, use_norm=norm) + self.HVD_block2 = NormUpsample(ch3, ch2, use_norm=norm) + self.HVD_block1 = NormUpsample(ch2, ch1, use_norm=norm) + self.HVD_block0 = nn.Sequential( + nn.ReplicationPad2d(1), + nn.Conv2d(ch1, 2, 3, stride=1, padding=0, bias=False), + ) + + # I_ways + self.IE_block0 = nn.Sequential( + nn.ReplicationPad2d(1), + nn.Conv2d(1, ch1, 3, stride=1, padding=0, bias=False), + ) + self.IE_block1 = NormDownsample(ch1, ch2, use_norm=norm) + self.IE_block2 = NormDownsample(ch2, ch3, use_norm=norm) + self.IE_block3 = NormDownsample(ch3, ch4, use_norm=norm) + + self.ID_block3 = NormUpsample(ch4, ch3, use_norm=norm) + self.ID_block2 = NormUpsample(ch3, ch2, use_norm=norm) + self.ID_block1 = NormUpsample(ch2, ch1, use_norm=norm) + self.ID_block0 = nn.Sequential( + nn.ReplicationPad2d(1), + nn.Conv2d(ch1, 1, 3, stride=1, padding=0, bias=False), + ) + + self.HV_LCA1 = HV_LCA(ch2, head2) + self.HV_LCA2 = HV_LCA(ch3, head3) + self.HV_LCA3 = HV_LCA(ch4, head4) + self.HV_LCA4 = HV_LCA(ch4, head4) + self.HV_LCA5 = HV_LCA(ch3, head3) + self.HV_LCA6 = HV_LCA(ch2, head2) + + self.I_LCA1 = I_LCA(ch2, head2) + self.I_LCA2 = I_LCA(ch3, head3) + self.I_LCA3 = I_LCA(ch4, head4) + self.I_LCA4 = I_LCA(ch4, head4) + self.I_LCA5 = I_LCA(ch3, head3) + self.I_LCA6 = I_LCA(ch2, head2) + + self.trans = RGB_HVI() + + def forward(self, x): + dtypes = x.dtype + hvi = self.trans.HVIT(x) + i = hvi[:, 2, :, :].unsqueeze(1).to(dtypes) + # low + i_enc0 = self.IE_block0(i) + i_enc1 = self.IE_block1(i_enc0) + hv_0 = self.HVE_block0(hvi) + hv_1 = self.HVE_block1(hv_0) + i_jump0 = i_enc0 + hv_jump0 = hv_0 + + i_enc2 = self.I_LCA1(i_enc1, hv_1) + hv_2 = self.HV_LCA1(hv_1, i_enc1) + v_jump1 = i_enc2 + hv_jump1 = hv_2 + i_enc2 = self.IE_block2(i_enc2) + hv_2 = self.HVE_block2(hv_2) + + i_enc3 = self.I_LCA2(i_enc2, hv_2) + hv_3 = self.HV_LCA2(hv_2, i_enc2) + v_jump2 = i_enc3 + hv_jump2 = hv_3 + i_enc3 = self.IE_block3(i_enc2) + hv_3 = self.HVE_block3(hv_2) + + i_enc4 = self.I_LCA3(i_enc3, hv_3) + hv_4 = self.HV_LCA3(hv_3, i_enc3) + + i_dec4 = self.I_LCA4(i_enc4, hv_4) + hv_4 = self.HV_LCA4(hv_4, i_enc4) + + hv_3 = self.HVD_block3(hv_4, hv_jump2) + i_dec3 = self.ID_block3(i_dec4, v_jump2) + i_dec2 = self.I_LCA5(i_dec3, hv_3) + hv_2 = self.HV_LCA5(hv_3, i_dec3) + + hv_2 = self.HVD_block2(hv_2, hv_jump1) + i_dec2 = self.ID_block2(i_dec3, v_jump1) + + i_dec1 = self.I_LCA6(i_dec2, hv_2) + hv_1 = self.HV_LCA6(hv_2, i_dec2) + + i_dec1 = self.ID_block1(i_dec1, i_jump0) + i_dec0 = self.ID_block0(i_dec1) + hv_1 = self.HVD_block1(hv_1, hv_jump0) + hv_0 = self.HVD_block0(hv_1) + + output_hvi = torch.cat([hv_0, i_dec0], dim=1) + hvi + output_rgb = self.trans.PHVIT(output_hvi) + + return output_rgb + + def HVIT(self, x): + hvi = self.trans.HVIT(x) + return hvi diff --git a/libs/spandrel/spandrel/architectures/HVICIDNet/arch/transformer_utils.py b/libs/spandrel/spandrel/architectures/HVICIDNet/arch/transformer_utils.py new file mode 100644 index 00000000..9d656a6d --- /dev/null +++ b/libs/spandrel/spandrel/architectures/HVICIDNet/arch/transformer_utils.py @@ -0,0 +1,81 @@ +import torch +import torch.nn as nn +import torch.nn.functional as F + + +class LayerNorm(nn.Module): + r"""LayerNorm that supports two data formats: channels_last (default) or channels_first. + The ordering of the dimensions in the inputs. channels_last corresponds to inputs with + shape (batch_size, height, width, channels) while channels_first corresponds to inputs + with shape (batch_size, channels, height, width). + """ + + 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 + + +class NormDownsample(nn.Module): + def __init__(self, in_ch, out_ch, scale=0.5, use_norm=False): + super().__init__() + self.use_norm = use_norm + if self.use_norm: + self.norm = LayerNorm(out_ch) + self.prelu = nn.PReLU() + self.down = nn.Sequential( + nn.Conv2d(in_ch, out_ch, kernel_size=3, stride=1, padding=1, bias=False), + nn.UpsamplingBilinear2d(scale_factor=scale), + ) + + def forward(self, x): + x = self.down(x) + x = self.prelu(x) + if self.use_norm: + x = self.norm(x) + return x + else: + return x + + +class NormUpsample(nn.Module): + def __init__(self, in_ch, out_ch, scale=2, use_norm=False): + super().__init__() + self.use_norm = use_norm + if self.use_norm: + self.norm = LayerNorm(out_ch) + self.prelu = nn.PReLU() + self.up_scale = nn.Sequential( + nn.Conv2d(in_ch, out_ch, kernel_size=3, stride=1, padding=1, bias=False), + nn.UpsamplingBilinear2d(scale_factor=scale), + ) + self.up = nn.Conv2d( + out_ch * 2, out_ch, kernel_size=1, stride=1, padding=0, bias=False + ) + + def forward(self, x, y): + x = self.up_scale(x) + x = torch.cat([x, y], dim=1) + x = self.up(x) + x = self.prelu(x) + if self.use_norm: + return self.norm(x) + else: + return x diff --git a/tests/__snapshots__/test_HVICIDNet.ambr 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