annotator/manga_line/__init__.py

import os
import torch
import torch.nn as nn
import cv2

import numpy as np
from einops import rearrange

from annotator.annotator_path import models_path, DEVICE


class _bn_relu_conv(nn.Module):
    def __init__(self, in_filters, nb_filters, fw, fh, subsample=1):
        super(_bn_relu_conv, self).__init__()
        self.model = nn.Sequential(
            nn.BatchNorm2d(in_filters, eps=1e-3),
            nn.LeakyReLU(0.2),
            nn.Conv2d(in_filters, nb_filters, (fw, fh), stride=subsample, padding=(fw//2, fh//2), padding_mode='zeros')
        )

    def forward(self, x):
        return self.model(x)

        # the following are for debugs
        print("****", np.max(x.cpu().numpy()), np.min(x.cpu().numpy()), np.mean(x.cpu().numpy()), np.std(x.cpu().numpy()), x.shape)
        for i,layer in enumerate(self.model):
            if i != 2:
                x = layer(x)
            else:
                x = layer(x)
                #x = nn.functional.pad(x, (1, 1, 1, 1), mode='constant', value=0)
            print("____", np.max(x.cpu().numpy()), np.min(x.cpu().numpy()), np.mean(x.cpu().numpy()), np.std(x.cpu().numpy()), x.shape)
            print(x[0])
        return x

class _u_bn_relu_conv(nn.Module):
    def __init__(self, in_filters, nb_filters, fw, fh, subsample=1):
        super(_u_bn_relu_conv, self).__init__()
        self.model = nn.Sequential(
            nn.BatchNorm2d(in_filters, eps=1e-3),
            nn.LeakyReLU(0.2),
            nn.Conv2d(in_filters, nb_filters, (fw, fh), stride=subsample, padding=(fw//2, fh//2)),
            nn.Upsample(scale_factor=2, mode='nearest')
        )

    def forward(self, x):
        return self.model(x)



class _shortcut(nn.Module):
    def __init__(self, in_filters, nb_filters, subsample=1):
        super(_shortcut, self).__init__()
        self.process = False
        self.model = None
        if in_filters != nb_filters or subsample != 1:
            self.process = True
            self.model = nn.Sequential(
                    nn.Conv2d(in_filters, nb_filters, (1, 1), stride=subsample)
                )

    def forward(self, x, y):
        #print(x.size(), y.size(), self.process)
        if self.process:
            y0 = self.model(x)
            #print("merge+", torch.max(y0+y), torch.min(y0+y),torch.mean(y0+y), torch.std(y0+y), y0.shape)
            return y0 + y
        else:
            #print("merge", torch.max(x+y), torch.min(x+y),torch.mean(x+y), torch.std(x+y), y.shape)
            return x + y

class _u_shortcut(nn.Module):
    def __init__(self, in_filters, nb_filters, subsample):
        super(_u_shortcut, self).__init__()
        self.process = False
        self.model = None
        if in_filters != nb_filters:
            self.process = True
            self.model = nn.Sequential(
                nn.Conv2d(in_filters, nb_filters, (1, 1), stride=subsample, padding_mode='zeros'),
                nn.Upsample(scale_factor=2, mode='nearest')
            )

    def forward(self, x, y):
        if self.process:
            return self.model(x) + y
        else:
            return x + y


class basic_block(nn.Module):
    def __init__(self, in_filters, nb_filters, init_subsample=1):
        super(basic_block, self).__init__()
        self.conv1 = _bn_relu_conv(in_filters, nb_filters, 3, 3, subsample=init_subsample)
        self.residual = _bn_relu_conv(nb_filters, nb_filters, 3, 3)
        self.shortcut = _shortcut(in_filters, nb_filters, subsample=init_subsample)

    def forward(self, x):
        x1 = self.conv1(x)
        x2 = self.residual(x1)
        return self.shortcut(x, x2)

class _u_basic_block(nn.Module):
    def __init__(self, in_filters, nb_filters, init_subsample=1):
        super(_u_basic_block, self).__init__()
        self.conv1 = _u_bn_relu_conv(in_filters, nb_filters, 3, 3, subsample=init_subsample)
        self.residual = _bn_relu_conv(nb_filters, nb_filters, 3, 3)
        self.shortcut = _u_shortcut(in_filters, nb_filters, subsample=init_subsample)

    def forward(self, x):
        y = self.residual(self.conv1(x))
        return self.shortcut(x, y)


class _residual_block(nn.Module):
    def __init__(self, in_filters, nb_filters, repetitions, is_first_layer=False):
        super(_residual_block, self).__init__()
        layers = []
        for i in range(repetitions):
            init_subsample = 1
            if i == repetitions - 1 and not is_first_layer:
                init_subsample = 2
            if i == 0:
                l = basic_block(in_filters=in_filters, nb_filters=nb_filters, init_subsample=init_subsample)
            else:
                l = basic_block(in_filters=nb_filters, nb_filters=nb_filters, init_subsample=init_subsample)
            layers.append(l)

        self.model = nn.Sequential(*layers)

    def forward(self, x):
        return self.model(x)


class _upsampling_residual_block(nn.Module):
    def __init__(self, in_filters, nb_filters, repetitions):
        super(_upsampling_residual_block, self).__init__()
        layers = []
        for i in range(repetitions):
            l = None
            if i == 0: 
                l = _u_basic_block(in_filters=in_filters, nb_filters=nb_filters)#(input)
            else:
                l = basic_block(in_filters=nb_filters, nb_filters=nb_filters)#(input)
            layers.append(l)

        self.model = nn.Sequential(*layers)

    def forward(self, x):
        return self.model(x)


class res_skip(nn.Module):

    def __init__(self):
        super(res_skip, self).__init__()
        self.block0 = _residual_block(in_filters=1, nb_filters=24, repetitions=2, is_first_layer=True)#(input)
        self.block1 = _residual_block(in_filters=24, nb_filters=48, repetitions=3)#(block0)
        self.block2 = _residual_block(in_filters=48, nb_filters=96, repetitions=5)#(block1)
        self.block3 = _residual_block(in_filters=96, nb_filters=192, repetitions=7)#(block2)
        self.block4 = _residual_block(in_filters=192, nb_filters=384, repetitions=12)#(block3)
        
        self.block5 = _upsampling_residual_block(in_filters=384, nb_filters=192, repetitions=7)#(block4)
        self.res1 = _shortcut(in_filters=192, nb_filters=192)#(block3, block5, subsample=(1,1))

        self.block6 = _upsampling_residual_block(in_filters=192, nb_filters=96, repetitions=5)#(res1)
        self.res2 = _shortcut(in_filters=96, nb_filters=96)#(block2, block6, subsample=(1,1))

        self.block7 = _upsampling_residual_block(in_filters=96, nb_filters=48, repetitions=3)#(res2)
        self.res3 = _shortcut(in_filters=48, nb_filters=48)#(block1, block7, subsample=(1,1))

        self.block8 = _upsampling_residual_block(in_filters=48, nb_filters=24, repetitions=2)#(res3)
        self.res4 = _shortcut(in_filters=24, nb_filters=24)#(block0,block8, subsample=(1,1))

        self.block9 = _residual_block(in_filters=24, nb_filters=16, repetitions=2, is_first_layer=True)#(res4)
        self.conv15 = _bn_relu_conv(in_filters=16, nb_filters=1, fh=1, fw=1, subsample=1)#(block7)

    def forward(self, x):
        x0 = self.block0(x)
        x1 = self.block1(x0)
        x2 = self.block2(x1)
        x3 = self.block3(x2)
        x4 = self.block4(x3)

        x5 = self.block5(x4)
        res1 = self.res1(x3, x5)

        x6 = self.block6(res1)
        res2 = self.res2(x2, x6)

        x7 = self.block7(res2)
        res3 = self.res3(x1, x7)

        x8 = self.block8(res3)
        res4 = self.res4(x0, x8)

        x9 = self.block9(res4)
        y = self.conv15(x9)

        return y


class MangaLineExtration:
    model_dir = os.path.join(models_path, "manga_line")

    def __init__(self):
        self.model = None
        self.device = DEVICE

    def load_model(self):
        remote_model_path = "https://huggingface.co/lllyasviel/Annotators/resolve/main/erika.pth"
        modelpath = os.path.join(self.model_dir, "erika.pth")
        if not os.path.exists(modelpath):
            from basicsr.utils.download_util import load_file_from_url
            load_file_from_url(remote_model_path, model_dir=self.model_dir)
        #norm_layer = functools.partial(nn.InstanceNorm2d, affine=False, track_running_stats=False)
        net = res_skip()
        ckpt = torch.load(modelpath)
        for key in list(ckpt.keys()):
            if 'module.' in key:
                ckpt[key.replace('module.', '')] = ckpt[key]
                del ckpt[key]
        net.load_state_dict(ckpt)
        net.eval()
        self.model = net.to(self.device)

    def unload_model(self):
        if self.model is not None:
            self.model.cpu()

    def __call__(self, input_image):
        if self.model is None:
            self.load_model()
        self.model.to(self.device)
        img = cv2.cvtColor(input_image, cv2.COLOR_RGB2GRAY)
        img = np.ascontiguousarray(img.copy()).copy()
        with torch.no_grad():
            image_feed = torch.from_numpy(img).float().to(self.device)
            image_feed = rearrange(image_feed, 'h w -> 1 1 h w')
            line = self.model(image_feed)
            line = 255 - line.cpu().numpy()[0, 0]
            return line.clip(0, 255).astype(np.uint8)