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import argparse
import torch
import torch.nn as nn
from pathlib import Path
import os
from tqdm import tqdm
import torchvision.transforms as transforms
from torchvision.utils import save_image
import os
import zipfile
from PIL import Image
#
# class Sample_Test_Net(nn.Module):
# def __init__(self, encoder, decoder, transModule, patch_size=8):
# super(Sample_Test_Net, self).__init__()
# self.encoder = encoder
# self.decoder = decoder
# self.transModule = transModule
# self.patch_size = patch_size
#
# def forward(self, i_c, i_s, arbitrary_input=False):
# _, _, H, W = i_c.size()
# self.decoder.img_H = H
# self.decoder.img_W = W
# f_c = self.encoder(i_c, arbitrary_input)
# f_s = self.encoder(i_s, arbitrary_input)
# f_c, f_c_reso = f_c[0], f_c[2]
# f_s, f_s_reso = f_s[0], f_s[2]
# f_cs = self.transModule(f_c, f_s)
# i_cs = self.decoder(f_cs, f_c_reso)
# return i_cs
#
#
# def content_style_transTo_pt(i_c_path, i_s_path, i_c_size=None):
# """Resize the pics of arbitrary size to the shape of content image
# """
# i_c_pil = Image.open(i_c_path)
# i_s_pil = Image.open(i_s_path)
#
# if not i_c_size is None:
# i_c_tf = transforms.Compose([
# transforms.Resize(i_c_size),
# transforms.ToTensor()
# ])
# else:
# i_c_tf = transforms.Compose([
# transforms.ToTensor()
# ])
#
# i_s_size = min(i_c_pil.size[1], i_c_pil.size[0])
# i_s_tf = transforms.Compose([
# transforms.Resize(i_s_size),
# transforms.ToTensor()
# ])
#
# i_c_pt = i_c_tf(i_c_pil).unsqueeze(dim=0)
# i_s_pt = i_s_tf(i_s_pil).unsqueeze(dim=0)
#
# return i_c_pt, i_s_pt
#
# @torch.no_grad()
# def save_transferred_imgs(network, samples_path, img_saved_path, device=torch.device('cpu')):
# print('Image generation starts:')
#
# i_c_names = os.listdir(os.path.join(samples_path, 'Content'))
# i_s_names = os.listdir(os.path.join(samples_path, 'Style'))
# for i_c_name in tqdm(i_c_names):
# for i_s_name in tqdm(i_s_names):
# i_c_path = os.path.join(samples_path, 'Content', i_c_name)
# i_s_path = os.path.join(samples_path, 'Style', i_s_name)
# i_c, i_s = content_style_transTo_pt(i_c_path, i_s_path)
# i_cs = network(i_c.to(device), i_s.to(device), arbitrary_input=True)
#
# stem_c, suffix_c = os.path.splitext(i_c_name)
# stem_s, suffix_s = os.path.splitext(i_s_name)
# output_name = os.path.join(img_saved_path, f'{stem_c}_+_{stem_s}.{suffix_c}')
# save_image(i_cs, output_name)
#
#
# parser = argparse.ArgumentParser()
# # Basic options
# parser.add_argument('--input_dir', type=str, default='./input/Test',
# help='Directory path to a batch of content and style images ' + \
# 'which are loaded in "Content"/"Style" subfolders respectively.')
# parser.add_argument('--output_dir', type=str, default='./output',
# help='Directory to save the output image(s)')
# parser.add_argument('--checkpoint_import_path', type=str,
# default='./pre_trained_models/checkpoint/checkpoint_40000_epoch.pkl',
# help='Directory path to the importing checkpoint')
#
# args = parser.parse_args()
from models.pix2pix_model import Pix2PixModel
from options.test_options import TestOptions
import numpy as np
opt = TestOptions().parse()
def test_transform(size, crop):
transform_list = []
if size != 0:
transform_list.append(transforms.Resize(size))
if crop:
transform_list.append(transforms.CenterCrop(size))
transform_list.append(transforms.ToTensor())
transform = transforms.Compose(transform_list)
return transform
from os.path import basename
from os.path import splitext
def style_transform(h, w):
k = (h, w)
size = int(np.max(k))
print(type(size))
transform_list = []
transform_list.append(transforms.CenterCrop((h, w)))
transform_list.append(transforms.ToTensor())
transform = transforms.Compose(transform_list)
return transform
def content_transform():
transform_list = []
transform_list.append(transforms.ToTensor())
transform = transforms.Compose(transform_list)
return transform
# Advanced options
content_size = 512
style_size = 512
crop = 'store_true'
save_ext = '.jpg'
output_path = opt.output_dir
preserve_color = 'store_true'
alpha = opt.a
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
# Either --content or --content_dir should be given.
if opt.content:
content_paths = [Path(opt.content)]
else:
content_dir = Path(opt.content_dir)
content_paths = [f for f in content_dir.glob('*')]
# Either --style or --style_dir should be given.
if opt.style:
style_paths = [Path(opt.style)]
else:
style_dir = Path(opt.style_dir)
style_paths = [f for f in style_dir.glob('*')]
if not os.path.exists(output_path):
os.mkdir(output_path)
network=torch.load(opt.network_path)
network = Pix2PixModel(opt)
print(network)
network.eval()
network.to(device)
content_tf = test_transform(content_size, crop)
style_tf = test_transform(style_size, crop)
import torch.nn.functional as F
for content_path in content_paths:
for style_path in style_paths:
print(content_path)
content_tf1 = content_transform()
content = content_tf(Image.open(content_path).convert("RGB"))
h, w, c = np.shape(content)
style_tf1 = style_transform(h, w)
style = style_tf(Image.open(style_path).convert("RGB"))
style = style.to(device).unsqueeze(0)
content = content.to(device).unsqueeze(0)
contents = F.interpolate(content, size=(224, 224), mode='bilinear', align_corners=False)
styles = F.interpolate(style, size=(224, 224), mode='bilinear', align_corners=False)
model_out = network(data=None, mode="inference",iters=0,progress=None,epochs=None,images_iters=None)
with torch.no_grad():
_, _, _, _, output = model_out(contents, styles)
print("OUTPUT",output.shape)
upsample_layer = nn.Sequential(nn.Upsample(scale_factor=8 / 7, mode='bilinear', align_corners=False))
fake_image = upsample_layer(output)
output_name = '{:s}/{:s}_stylized_{:s}{:s}'.format(
output_path, splitext(basename(content_path))[0],
splitext(basename(style_path))[0], save_ext
)
save_image(fake_image, output_name) |