########################################################################################################
# The RWKV Language Model - https://github.com/BlinkDL/RWKV-LM
########################################################################################################
import torch, types, os
import numpy as np
from PIL import Image
import torch.nn as nn
from torch.nn import functional as F
import torchvision as vision
import torchvision.transforms as transforms
np.set_printoptions(precision=4, suppress=True, linewidth=200)
print(f'loading...')
########################################################################################################
# model_prefix = 'out-v7c_d8_256-224-13bit-OB32x0.5-745'
# model_prefix = 'out-v7d_d16_512-224-13bit-OB32x0.5-2487'
model_prefix = 'out-v7d_d32_1024-224-13bit-OB32x0.5-5560'
input_imgs = ['lena.png', 'genshin.png', 'kodim14-modified.png', 'kodim19-modified.png', 'kodim24-modified.png']
device = 'cpu' # cpu cuda
########################################################################################################
class ToBinary(torch.autograd.Function):
@staticmethod
def forward(ctx, x):
return torch.floor(x + 0.5) # no need for noise when we have plenty of data
@staticmethod
def backward(ctx, grad_output):
return grad_output.clone() # pass-through
class ResBlock(nn.Module):
def __init__(self, c_x, c_hidden):
super().__init__()
self.B0 = nn.BatchNorm2d(c_x)
self.C0 = nn.Conv2d(c_x, c_hidden, kernel_size=3, padding=1)
self.C1 = nn.Conv2d(c_hidden, c_x, kernel_size=3, padding=1)
self.C2 = nn.Conv2d(c_x, c_hidden, kernel_size=3, padding=1)
self.C3 = nn.Conv2d(c_hidden, c_x, kernel_size=3, padding=1)
def forward(self, x):
ACT = F.mish
x = x + self.C1(ACT(self.C0(ACT(self.B0(x)))))
x = x + self.C3(ACT(self.C2(x)))
return x
if model_prefix == 'out-v7c_d8_256-224-13bit-OB32x0.5-745':
class R_ENCODER(nn.Module):
def __init__(self, args):
super().__init__()
self.args = args
dd = 8
self.Bxx = nn.BatchNorm2d(dd*64)
self.CIN = nn.Conv2d(3, dd, kernel_size=3, padding=1)
self.Cx0 = nn.Conv2d(dd, 32, kernel_size=3, padding=1)
self.Cx1 = nn.Conv2d(32, dd, kernel_size=3, padding=1)
self.B00 = nn.BatchNorm2d(dd*4)
self.C00 = nn.Conv2d(dd*4, 256, kernel_size=3, padding=1)
self.C01 = nn.Conv2d(256, dd*4, kernel_size=3, padding=1)
self.C02 = nn.Conv2d(dd*4, 256, kernel_size=3, padding=1)
self.C03 = nn.Conv2d(256, dd*4, kernel_size=3, padding=1)
self.B10 = nn.BatchNorm2d(dd*16)
self.C10 = nn.Conv2d(dd*16, 256, kernel_size=3, padding=1)
self.C11 = nn.Conv2d(256, dd*16, kernel_size=3, padding=1)
self.C12 = nn.Conv2d(dd*16, 256, kernel_size=3, padding=1)
self.C13 = nn.Conv2d(256, dd*16, kernel_size=3, padding=1)
self.B20 = nn.BatchNorm2d(dd*64)
self.C20 = nn.Conv2d(dd*64, 256, kernel_size=3, padding=1)
self.C21 = nn.Conv2d(256, dd*64, kernel_size=3, padding=1)
self.C22 = nn.Conv2d(dd*64, 256, kernel_size=3, padding=1)
self.C23 = nn.Conv2d(256, dd*64, kernel_size=3, padding=1)
self.COUT = nn.Conv2d(dd*64, args.my_img_bit, kernel_size=3, padding=1)
def forward(self, img):
ACT = F.mish
x = self.CIN(img)
xx = self.Bxx(F.pixel_unshuffle(x, 8))
x = x + self.Cx1(ACT(self.Cx0(x)))
x = F.pixel_unshuffle(x, 2)
x = x + self.C01(ACT(self.C00(ACT(self.B00(x)))))
x = x + self.C03(ACT(self.C02(x)))
x = F.pixel_unshuffle(x, 2)
x = x + self.C11(ACT(self.C10(ACT(self.B10(x)))))
x = x + self.C13(ACT(self.C12(x)))
x = F.pixel_unshuffle(x, 2)
x = x + self.C21(ACT(self.C20(ACT(self.B20(x)))))
x = x + self.C23(ACT(self.C22(x)))
x = self.COUT(x + xx)
return torch.sigmoid(x)
class R_DECODER(nn.Module):
def __init__(self, args):
super().__init__()
self.args = args
dd = 8
self.CIN = nn.Conv2d(args.my_img_bit, dd*64, kernel_size=3, padding=1)
self.B00 = nn.BatchNorm2d(dd*64)
self.C00 = nn.Conv2d(dd*64, 256, kernel_size=3, padding=1)
self.C01 = nn.Conv2d(256, dd*64, kernel_size=3, padding=1)
self.C02 = nn.Conv2d(dd*64, 256, kernel_size=3, padding=1)
self.C03 = nn.Conv2d(256, dd*64, kernel_size=3, padding=1)
self.B10 = nn.BatchNorm2d(dd*16)
self.C10 = nn.Conv2d(dd*16, 256, kernel_size=3, padding=1)
self.C11 = nn.Conv2d(256, dd*16, kernel_size=3, padding=1)
self.C12 = nn.Conv2d(dd*16, 256, kernel_size=3, padding=1)
self.C13 = nn.Conv2d(256, dd*16, kernel_size=3, padding=1)
self.B20 = nn.BatchNorm2d(dd*4)
self.C20 = nn.Conv2d(dd*4, 256, kernel_size=3, padding=1)
self.C21 = nn.Conv2d(256, dd*4, kernel_size=3, padding=1)
self.C22 = nn.Conv2d(dd*4, 256, kernel_size=3, padding=1)
self.C23 = nn.Conv2d(256, dd*4, kernel_size=3, padding=1)
self.Cx0 = nn.Conv2d(dd, 32, kernel_size=3, padding=1)
self.Cx1 = nn.Conv2d(32, dd, kernel_size=3, padding=1)
self.COUT = nn.Conv2d(dd, 3, kernel_size=3, padding=1)
def forward(self, code):
ACT = F.mish
x = self.CIN(code)
x = x + self.C01(ACT(self.C00(ACT(self.B00(x)))))
x = x + self.C03(ACT(self.C02(x)))
x = F.pixel_shuffle(x, 2)
x = x + self.C11(ACT(self.C10(ACT(self.B10(x)))))
x = x + self.C13(ACT(self.C12(x)))
x = F.pixel_shuffle(x, 2)
x = x + self.C21(ACT(self.C20(ACT(self.B20(x)))))
x = x + self.C23(ACT(self.C22(x)))
x = F.pixel_shuffle(x, 2)
x = x + self.Cx1(ACT(self.Cx0(x)))
x = self.COUT(x)
return torch.sigmoid(x)
else:
class R_ENCODER(nn.Module):
def __init__(self, args):
super().__init__()
self.args = args
if 'd16_512' in model_prefix:
dd, ee, ff = 16, 64, 512
elif 'd32_1024' in model_prefix:
dd, ee, ff = 32, 128, 1024
self.CXX = nn.Conv2d(3, dd, kernel_size=3, padding=1)
self.BXX = nn.BatchNorm2d(dd)
self.CX0 = nn.Conv2d(dd, ee, kernel_size=3, padding=1)
self.CX1 = nn.Conv2d(ee, dd, kernel_size=3, padding=1)
self.R0 = ResBlock(dd*4, ff)
self.R1 = ResBlock(dd*16, ff)
self.R2 = ResBlock(dd*64, ff)
self.CZZ = nn.Conv2d(dd*64, args.my_img_bit, kernel_size=3, padding=1)
def forward(self, x):
ACT = F.mish
x = self.BXX(self.CXX(x))
x = x + self.CX1(ACT(self.CX0(x)))
x = F.pixel_unshuffle(x, 2)
x = self.R0(x)
x = F.pixel_unshuffle(x, 2)
x = self.R1(x)
x = F.pixel_unshuffle(x, 2)
x = self.R2(x)
x = self.CZZ(x)
return torch.sigmoid(x)
class R_DECODER(nn.Module):
def __init__(self, args):
super().__init__()
self.args = args
if 'd16_512' in model_prefix:
dd, ee, ff = 16, 64, 512
elif 'd32_1024' in model_prefix:
dd, ee, ff = 32, 128, 1024
self.CZZ = nn.Conv2d(args.my_img_bit, dd*64, kernel_size=3, padding=1)
self.BZZ = nn.BatchNorm2d(dd*64)
self.R0 = ResBlock(dd*64, ff)
self.R1 = ResBlock(dd*16, ff)
self.R2 = ResBlock(dd*4, ff)
self.CX0 = nn.Conv2d(dd, ee, kernel_size=3, padding=1)
self.CX1 = nn.Conv2d(ee, dd, kernel_size=3, padding=1)
self.CXX = nn.Conv2d(dd, 3, kernel_size=3, padding=1)
def forward(self, x):
ACT = F.mish
x = self.BZZ(self.CZZ(x))
x = self.R0(x)
x = F.pixel_shuffle(x, 2)
x = self.R1(x)
x = F.pixel_shuffle(x, 2)
x = self.R2(x)
x = F.pixel_shuffle(x, 2)
x = x + self.CX1(ACT(self.CX0(x)))
x = self.CXX(x)
return torch.sigmoid(x)
########################################################################################################
print(f'building model {model_prefix}...')
args = types.SimpleNamespace()
args.my_img_bit = 13
encoder = R_ENCODER(args).eval().to(device)
decoder = R_DECODER(args).eval().to(device)
zpow = torch.tensor([2**i for i in range(0,13)]).reshape(13,1,1).to(device).long()
encoder.load_state_dict(torch.load(f'{model_prefix}-E.pth'))
decoder.load_state_dict(torch.load(f'{model_prefix}-D.pth'))
########################################################################################################
img_transform = transforms.Compose([
transforms.PILToTensor(),
transforms.ConvertImageDtype(torch.float),
transforms.Resize((224, 224))
])
for input_img in input_imgs:
print(f'test image {input_img}...')
with torch.no_grad():
img = img_transform(Image.open(f'img_test/{input_img}')).unsqueeze(0).to(device)
z = encoder(img)
z = ToBinary.apply(z)
zz = torch.sum(z.squeeze().long() * zpow, dim=0)
print(f'Code shape = {zz.shape}\n{zz.cpu().numpy()}\n')
out = decoder(z)
vision.utils.save_image(out, f"img_test/{input_img.split('.')[0]}-{model_prefix}.png")