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