transfer.py

import os
import torch
import argparse
from PIL import Image
import torchvision.utils as vutils
from utils import Reformer
import time
import numpy as np
import torch.nn.functional as function

import torchvision.transforms as transforms

parser = argparse.ArgumentParser(description='Texture Reformer Pytorch')

# Specify inputs and outputs
parser.add_argument('-content', type=str, default='inputs/doodles/Seth.jpg', help="File path to the content image, valid for style transfer and invalid for texture transfer")
parser.add_argument('-style', type=str, default='inputs/doodles/Gogh.jpg', help="File path to the style/source image")
parser.add_argument('-content_sem', type=str, default='inputs/doodles/Seth_sem.png', help="File path to the semantic map of content/target image")
parser.add_argument('-style_sem', type=str, default='inputs/doodles/Gogh_sem.png', help="File path to the semantic map of style/source image")
parser.add_argument('-outf', type=str, default='outputs', help="Folder to save output images")
parser.add_argument('-content_size', type=int, default=0, help="Resize content/target, leave it to 0 if not resize")
parser.add_argument('-style_size', type=int, default=0, help="Resize style/source, leave it to 0 if not resize")
parser.add_argument('-style_transfer', action="store_true", help="Activate it if you want style transfer rather than texture transfer")

# Runtime controls
parser.add_argument('-coarse_alpha', type=float, default=1, help="Hyperparameter to blend transformed feature with content feature in coarse level (level 5)")
parser.add_argument('-fine_alpha', type=float, default=1, help="Hyperparameter to blend transformed feature with content feature in fine level (level 4)")

parser.add_argument('-semantic', type=str, default='concat', choices=['add', 'concat', 'concat_ds'], help="Choose different modes to embed semantic maps, 'add': our addition, 'concat': our concatenation, 'concat_ds': concat downsampled semantic maps")
parser.add_argument('-concat_weight', type=float, default=50, help="Hyperparameter to control the semantic guidance/awareness weight for '-semantic concat' mode and '-semantic concat_ds' mode, range 0-inf")
parser.add_argument('-add_weight', type=float, default=0.6, help="Hyperparameter to control the semantic guidance/awareness weight for '-semantic add' mode, range 0-1")

parser.add_argument('-coarse_psize', type=int, default=0, help="Patch size in coarse level (level 5), 0 means using global view")
parser.add_argument('-fine_psize', type=int, default=3, help="Patch size in fine level (level 4)")

parser.add_argument('-enhance', type=str, default='adain', choices=['adain', 'wct'], help="Choose different enhancement modes in level 3, level 2, and level 1. 'adain': first-order statistics enhancement, 'wct': second-order statistics enhancement.")
parser.add_argument('-enhance_alpha', type=float, default=1, help="Hyperparameter to control the enhancement degree in level 3, level 2, and level 1")

# Compress models
parser.add_argument('-compress', action="store_true", help="Use the compressed models for faster inference")

args = parser.parse_args()

args.cuda = torch.cuda.is_available()

if args.compress:
    args.e5 = 'small_models/SE5.pth'
    args.e4 = 'small_models/SE4.pth'
    args.e3 = 'small_models/SE3.pth'
    args.e2 = 'small_models/SE2.pth'
    args.e1 = 'small_models/SE1.pth'

    args.d5 = 'small_models/SD5.pth'
    args.d4 = 'small_models/SD4.pth'
    args.d3 = 'small_models/SD3.pth'
    args.d2 = 'small_models/SD2.pth'
    args.d1 = 'small_models/SD1.pth'

else:
    args.e5 = 'models/E5.pth'
    args.e4 = 'models/E4.pth'
    args.e3 = 'models/E3.pth'
    args.e2 = 'models/E2.pth'
    args.e1 = 'models/E1.pth'

    args.d5 = 'models/D5.pth'
    args.d4 = 'models/D4.pth'
    args.d3 = 'models/D3.pth'
    args.d2 = 'models/D2.pth'
    args.d1 = 'models/D1.pth'


print(args._get_kwargs())

# Set up data
# content/target data
# if the task is texture transfer, the content/target semantic map must be provided
if args.style_transfer:
    content = Image.open(args.content).convert('RGB')
    if args.content_size:
        content = transforms.Resize(args.content_size)(content)
    content = transforms.ToTensor()(content).unsqueeze(0)
    if args.content_sem:
        content_sem = Image.open(args.content_sem).convert('RGB')
        if args.content_size:
            content_sem = transforms.Resize(args.content_size)(content_sem)
        content_sem = transforms.ToTensor()(content_sem).unsqueeze(0)
    else:
        content_sem = torch.ones(content.shape)
else:
    content_sem = Image.open(args.content_sem).convert('RGB')
    if args.content_size:
        content_sem = transforms.Resize(args.content_size)(content_sem)
    content_sem = transforms.ToTensor()(content_sem).unsqueeze(0)
    # for texture transfer, use the content/target semantic map as the content
    content = content_sem


# style/source data
style = Image.open(args.style).convert('RGB')
if args.style_size:
    style = transforms.Resize(args.style_size)(style)
style = transforms.ToTensor()(style).unsqueeze(0)

if args.style_sem:
    style_sem = Image.open(args.style_sem).convert('RGB')
    if args.style_size:
        style_sem = transforms.Resize(args.style_size)(style_sem)
    style_sem = transforms.ToTensor()(style_sem).unsqueeze(0)
else:
    style_sem = torch.ones(style.shape)


# Set up model and Texture Reformer
if args.cuda:
    TR = Reformer(args).cuda()
else:
    TR = Reformer(args)


# View-Specific Texture Reformation (VSTR) operation
@torch.no_grad()
def VSTR(encoder, decoder, content, style, content_sem, style_sem, patch_size, alpha, semantic):
    # make the width and height of the temporary content/target image the same as the content/target semantic map
    if content_sem.shape[2] != content.shape[2] or content_sem.shape[3] != content.shape[3]:
        content = content.squeeze(0).cpu().clone()
        content = transforms.ToPILImage()(content)
        content = transforms.Resize([content_sem.shape[2],content_sem.shape[3]])(content)
        if args.cuda:
            content = transforms.ToTensor()(content).unsqueeze(0).cuda()
        else:
            content = transforms.ToTensor()(content).unsqueeze(0)

    sF  = encoder(style)
    cF  = encoder(content)

    # add the features of the semantic maps
    if semantic == "add":
        sF_sem  = encoder(style_sem)
        cF_sem  = encoder(content_sem)
        csF = TR.VSTR_add(cF, sF, cF_sem, sF_sem, patch_size, alpha, args.add_weight)

    # concatenate the features of the semantic maps
    elif semantic == "concat":
        sF_sem  = encoder(style_sem)
        cF_sem  = encoder(content_sem)
        csF = TR.VSTR_concat(cF, sF, cF_sem, sF_sem, patch_size, alpha, args.concat_weight)

    # directly concatenate with the downsampled semantic maps
    elif semantic == "concat_ds":
        sF_sem  = function.interpolate(style_sem, [sF.size(2), sF.size(3)], mode="nearest")
        cF_sem  = function.interpolate(content_sem, [cF.size(2), cF.size(3)], mode="nearest")
        csF = TR.VSTR_concat(cF, sF, cF_sem, sF_sem, patch_size, alpha, args.concat_weight)
    
    #csF = cF
    Img = decoder(csF)

    return Img



# Statistic-based Enhancement (SE) operation
@torch.no_grad()
def SE(encoder, decoder, content, style, enhance, enhance_alpha):
    sF = encoder(style)
    cF = encoder(content)

    # match the first-order statistics, i.e., channel-wise mean and standard deviation
    if enhance == "adain":
        csF = TR.adain(cF, sF, enhance_alpha)

    # match the second-order statistics, i.e., covariance
    elif enhance == "wct":
        sF  = sF.data.cpu().squeeze(0) # note: svd runs on CPU
        cF  = cF.data.cpu().squeeze(0)
        csF = TR.wct(cF, sF, enhance_alpha)
        if args.cuda:
            csF = csF.cuda()

    #csF = cF
    Img = decoder(csF)
    return Img


# Run
if args.cuda:
    content = content.cuda()
    style = style.cuda()
    content_sem = content_sem.cuda()
    style_sem = style_sem.cuda()

# Start texture/style transfer

if args.cuda:
    torch.cuda.synchronize() 
start_time = time.time()

# Global view structure alignment stage
print("Processing level 5"); content = VSTR(TR.e5, TR.d5, content, style, content_sem, style_sem, args.coarse_psize, args.coarse_alpha, args.semantic)

# Local view texture refinement stage
print("Processing level 4"); content = VSTR(TR.e4, TR.d4, content, style, content_sem, style_sem, args.fine_psize, args.fine_alpha, args.semantic)

# Holistic effect enhancement stage
print("Processing level 3"); content = SE(TR.e3, TR.d3, content, style, args.enhance, args.enhance_alpha)
print("Processing level 2"); content = SE(TR.e2, TR.d2, content, style, args.enhance, args.enhance_alpha)
print("Processing level 1"); content = SE(TR.e1, TR.d1, content, style, args.enhance, args.enhance_alpha)

if args.cuda:
    torch.cuda.synchronize()
end_time = time.time()
print('Elapsed time is: %.4f seconds' % (end_time - start_time))
  
out_path = os.path.join(args.outf, "output.jpg")
vutils.save_image(content.data.cpu(), out_path)