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| import math | |
| import torch | |
| import torch.nn as nn | |
| import torch.nn.functional as F | |
| from torch.nn import init as init | |
| from torch.nn.modules.batchnorm import _BatchNorm | |
| from insir_models.nafnet_utils import Local_Base, LayerNorm2d | |
| from insir_models.nafnet import SimpleGate, NAFBlock | |
| class ICB(nn.Module): | |
| """ | |
| Instruction Condition Block (ICB) | |
| Paper Section 3.3 | |
| """ | |
| def __init__(self, feature_dim, text_dim=768): | |
| super(ICB, self).__init__() | |
| self.fc = nn.Linear(text_dim, feature_dim) | |
| self.block = NAFBlock(feature_dim) | |
| self.beta = nn.Parameter(torch.zeros((1, feature_dim, 1, 1)), requires_grad=True) | |
| self.gamma = nn.Parameter(torch.zeros((1, feature_dim, 1, 1)), requires_grad=True) | |
| def forward(self, x, text_embedding): | |
| gating_factors = torch.sigmoid(self.fc(text_embedding)) | |
| gating_factors = gating_factors.unsqueeze(-1).unsqueeze(-1) | |
| f = x * self.gamma + self.beta # 1) learned feature scaling/modulation | |
| f = f * gating_factors # 2) (soft) feature routing based on text | |
| f = self.block(f) # 3) block feature enhancement | |
| return f + x | |
| class InstructIR(nn.Module): | |
| """ | |
| InstructIR model using NAFNet (ECCV 2022) as backbone. | |
| The model takes as input an RGB image and a text embedding (encoded instruction). | |
| Described in Paper Section 3.3 | |
| """ | |
| def __init__(self, img_channel=3, width=16, middle_blk_num=1, enc_blk_nums=[], dec_blk_nums=[], txtdim=768): | |
| super().__init__() | |
| self.intro = nn.Conv2d(in_channels=img_channel, out_channels=width, kernel_size=3, padding=1, stride=1, groups=1, | |
| bias=True) | |
| self.ending = nn.Conv2d(in_channels=width, out_channels=img_channel, kernel_size=3, padding=1, stride=1, groups=1, | |
| bias=True) | |
| self.encoders = nn.ModuleList() | |
| self.decoders = nn.ModuleList() | |
| self.middle_blks = nn.ModuleList() | |
| self.ups = nn.ModuleList() | |
| self.downs = nn.ModuleList() | |
| self.enc_cond = nn.ModuleList() | |
| self.dec_cond = nn.ModuleList() | |
| chan = width | |
| for num in enc_blk_nums: | |
| self.encoders.append( | |
| nn.Sequential( | |
| *[NAFBlock(chan) for _ in range(num)] | |
| ) | |
| ) | |
| self.enc_cond.append(ICB(chan, txtdim)) | |
| self.downs.append( | |
| nn.Conv2d(chan, 2*chan, 2, 2) | |
| ) | |
| chan = chan * 2 | |
| self.middle_blks = nn.Sequential( | |
| *[NAFBlock(chan) for _ in range(middle_blk_num)] | |
| ) | |
| for num in dec_blk_nums: | |
| self.ups.append( | |
| nn.Sequential( | |
| nn.Conv2d(chan, chan * 2, 1, bias=False), | |
| nn.PixelShuffle(2) | |
| ) | |
| ) | |
| chan = chan // 2 | |
| self.decoders.append( | |
| nn.Sequential( | |
| *[NAFBlock(chan) for _ in range(num)] | |
| ) | |
| ) | |
| # Add text embedding as modulation | |
| self.dec_cond.append(ICB(chan, txtdim)) | |
| self.padder_size = 2 ** len(self.encoders) | |
| def forward(self, inp, txtembd): | |
| B, C, H, W = inp.shape | |
| inp = self.check_image_size(inp) | |
| x = self.intro(inp) | |
| encs = [] | |
| for encoder, enc_mod, down in zip(self.encoders, self.enc_cond, self.downs): | |
| x = encoder(x) | |
| x = enc_mod(x, txtembd) | |
| encs.append(x) | |
| x = down(x) | |
| x = self.middle_blks(x) | |
| for decoder, up, enc_skip, dec_mod in zip(self.decoders, self.ups, encs[::-1], self.dec_cond): | |
| x = up(x) | |
| x = x + enc_skip | |
| x = decoder(x) | |
| x = dec_mod(x, txtembd) | |
| x = self.ending(x) | |
| x = x + inp | |
| return x[:, :, :H, :W] | |
| def check_image_size(self, x): | |
| _, _, h, w = x.size() | |
| mod_pad_h = (self.padder_size - h % self.padder_size) % self.padder_size | |
| mod_pad_w = (self.padder_size - w % self.padder_size) % self.padder_size | |
| x = F.pad(x, (0, mod_pad_w, 0, mod_pad_h)) | |
| return x | |
| def create_model(input_channels = 3, width = 32, enc_blks = [2, 2, 4, 8], middle_blk_num = 12, dec_blks = [2, 2, 2, 2], txtdim=768): | |
| net = InstructIR(img_channel=input_channels, width=width, middle_blk_num=middle_blk_num, | |
| enc_blk_nums=enc_blks, dec_blk_nums=dec_blks, txtdim=txtdim) | |
| return net |