preprocessor/sketchKeras.py

import cv2
import numpy as np

import torch
import torch.nn as nn


def postprocess(pred, thresh=0.18):
    assert thresh <= 1.0 and thresh >= 0.0

    pred = torch.amax(pred, 0)
    pred[pred < thresh] = 0
    pred -= 0.5
    pred *= 2
    return pred


class SketchKeras(nn.Module):
    def __init__(self):
        super(SketchKeras, self).__init__()

        self.downblock_1 = nn.Sequential(
            nn.ReflectionPad2d((1, 1, 1, 1)),
            nn.Conv2d(1, 32, kernel_size=3, stride=1),
            nn.BatchNorm2d(32, eps=1e-3, momentum=0),
            nn.ReLU(),
        )
        self.downblock_2 = nn.Sequential(
            nn.ReflectionPad2d((1, 1, 1, 1)),
            nn.Conv2d(32, 64, kernel_size=4, stride=2),
            nn.BatchNorm2d(64, eps=1e-3, momentum=0),
            nn.ReLU(),
            nn.ReflectionPad2d((1, 1, 1, 1)),
            nn.Conv2d(64, 64, kernel_size=3, stride=1),
            nn.BatchNorm2d(64, eps=1e-3, momentum=0),
            nn.ReLU(),
        )
        self.downblock_3 = nn.Sequential(
            nn.ReflectionPad2d((1, 1, 1, 1)),
            nn.Conv2d(64, 128, kernel_size=4, stride=2),
            nn.BatchNorm2d(128, eps=1e-3, momentum=0),
            nn.ReLU(),
            nn.ReflectionPad2d((1, 1, 1, 1)),
            nn.Conv2d(128, 128, kernel_size=3, stride=1),
            nn.BatchNorm2d(128, eps=1e-3, momentum=0),
            nn.ReLU(),
        )
        self.downblock_4 = nn.Sequential(
            nn.ReflectionPad2d((1, 1, 1, 1)),
            nn.Conv2d(128, 256, kernel_size=4, stride=2),
            nn.BatchNorm2d(256, eps=1e-3, momentum=0),
            nn.ReLU(),
            nn.ReflectionPad2d((1, 1, 1, 1)),
            nn.Conv2d(256, 256, kernel_size=3, stride=1),
            nn.BatchNorm2d(256, eps=1e-3, momentum=0),
            nn.ReLU(),
        )
        self.downblock_5 = nn.Sequential(
            nn.ReflectionPad2d((1, 1, 1, 1)),
            nn.Conv2d(256, 512, kernel_size=4, stride=2),
            nn.BatchNorm2d(512, eps=1e-3, momentum=0),
            nn.ReLU(),
        )
        self.downblock_6 = nn.Sequential(
            nn.ReflectionPad2d((1, 1, 1, 1)),
            nn.Conv2d(512, 512, kernel_size=3, stride=1),
            nn.BatchNorm2d(512, eps=1e-3, momentum=0),
            nn.ReLU(),
        )

        self.upblock_1 = nn.Sequential(
            nn.Upsample(scale_factor=2, mode="bicubic"),
            nn.ReflectionPad2d((1, 2, 1, 2)),
            nn.Conv2d(1024, 512, kernel_size=4, stride=1),
            nn.BatchNorm2d(512, eps=1e-3, momentum=0),
            nn.ReLU(),
            nn.ReflectionPad2d((1, 1, 1, 1)),
            nn.Conv2d(512, 256, kernel_size=3, stride=1),
            nn.BatchNorm2d(256, eps=1e-3, momentum=0),
            nn.ReLU(),
        )

        self.upblock_2 = nn.Sequential(
            nn.Upsample(scale_factor=2, mode="bicubic"),
            nn.ReflectionPad2d((1, 2, 1, 2)),
            nn.Conv2d(512, 256, kernel_size=4, stride=1),
            nn.BatchNorm2d(256, eps=1e-3, momentum=0),
            nn.ReLU(),
            nn.ReflectionPad2d((1, 1, 1, 1)),
            nn.Conv2d(256, 128, kernel_size=3, stride=1),
            nn.BatchNorm2d(128, eps=1e-3, momentum=0),
            nn.ReLU(),
        )

        self.upblock_3 = nn.Sequential(
            nn.Upsample(scale_factor=2, mode="bicubic"),
            nn.ReflectionPad2d((1, 2, 1, 2)),
            nn.Conv2d(256, 128, kernel_size=4, stride=1),
            nn.BatchNorm2d(128, eps=1e-3, momentum=0),
            nn.ReLU(),
            nn.ReflectionPad2d((1, 1, 1, 1)),
            nn.Conv2d(128, 64, kernel_size=3, stride=1),
            nn.BatchNorm2d(64, eps=1e-3, momentum=0),
            nn.ReLU(),
        )

        self.upblock_4 = nn.Sequential(
            nn.Upsample(scale_factor=2, mode="bicubic"),
            nn.ReflectionPad2d((1, 2, 1, 2)),
            nn.Conv2d(128, 64, kernel_size=4, stride=1),
            nn.BatchNorm2d(64, eps=1e-3, momentum=0),
            nn.ReLU(),
            nn.ReflectionPad2d((1, 1, 1, 1)),
            nn.Conv2d(64, 32, kernel_size=3, stride=1),
            nn.BatchNorm2d(32, eps=1e-3, momentum=0),
            nn.ReLU(),
        )

        self.last_pad = nn.ReflectionPad2d((1, 1, 1, 1))
        self.last_conv = nn.Conv2d(64, 1, kernel_size=3, stride=1)

    def forward(self, x):
        d1 = self.downblock_1(x)
        d2 = self.downblock_2(d1)
        d3 = self.downblock_3(d2)
        d4 = self.downblock_4(d3)
        d5 = self.downblock_5(d4)
        d6 = self.downblock_6(d5)

        u1 = torch.cat((d5, d6), dim=1)
        u1 = self.upblock_1(u1)
        u2 = torch.cat((d4, u1), dim=1)
        u2 = self.upblock_2(u2)
        u3 = torch.cat((d3, u2), dim=1)
        u3 = self.upblock_3(u3)
        u4 = torch.cat((d2, u3), dim=1)
        u4 = self.upblock_4(u4)
        u5 = torch.cat((d1, u4), dim=1)

        out = self.last_conv(self.last_pad(u5))

        return out

    def proceed(self, img):
        img = np.array(img)
        blurred = cv2.GaussianBlur(img, (0, 0), 3)
        img = img.astype(int) - blurred.astype(int)
        img = img.astype(np.float32) / 127.5
        img /= np.max(img)
        img = torch.tensor(img).unsqueeze(0).permute(3, 0, 1, 2).cuda()
        img = self(img)
        img = postprocess(img, thresh=0.1).unsqueeze(1)
        return img