File size: 3,647 Bytes
52d252c
 
 
 
 
 
7b10f82
52d252c
 
 
0cc1558
 
 
 
52d252c
cd1eaaf
52d252c
 
0cc1558
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
52d252c
76ea7e0
52d252c
 
0cc1558
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
bc8701f
 
 
 
 
 
 
 
 
 
 
 
 
 
 
0cc1558
bc8701f
 
 
 
 
 
7b10f82
bc8701f
 
 
76ea7e0
0cc1558
52d252c
 
263f380
 
52d252c
 
 
 
a3348bb
0cc1558
 
 
 
 
 
 
 
 
bc8701f
adf5cfd
 
acf7bcd
7b10f82
52d252c
 
 
 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
import os
import torch
import gradio as gr
import numpy as np
import torchvision.transforms as transforms


from torch.autograd import Variable
from network.Transformer import Transformer

import logging

logger = logging.getLogger(__name__)

LOAD_SIZE = 1280
MODEL_PATH = "models"
COLOUR_MODEL = "RGB"

STYLE_SHINKAI = "Makoto Shinkai"
STYLE_HOSODA = "Mamoru Hosoda"
STYLE_MIYAZAKI = "Hayao Miyazaki"
STYLE_KON = "Satoshi Kon"
DEFAULT_STYLE = STYLE_SHINKAI
STYLE_CHOICE_LIST = [STYLE_SHINKAI, STYLE_HOSODA, STYLE_MIYAZAKI, STYLE_KON]

shinkai_model = Transformer()
hosoda_model = Transformer()
miyazaki_model = Transformer()
kon_model = Transformer()


shinkai_model.load_state_dict(
    torch.load(os.path.join(MODEL_PATH, "shinkai_makoto.pth"))
)
hosoda_model.load_state_dict(
    torch.load(os.path.join(MODEL_PATH, "hosoda_mamoru.pth"))
)
miyazaki_model.load_state_dict(
    torch.load(os.path.join(MODEL_PATH, "miyazaki_hayao.pth"))
)
kon_model.load_state_dict(
    torch.load(os.path.join(MODEL_PATH, "kon_satoshi.pth"))
)

shinkai_model.eval()
hosoda_model.eval()
miyazaki_model.eval()
kon_model.eval()

disable_gpu = True


def get_model(style):
    if style == STYLE_SHINKAI:
        return shinkai_model
    elif style == STYLE_HOSODA:
        return hosoda_model
    elif style == STYLE_MIYAZAKI:
        return miyazaki_model
    elif style == STYLE_KON:
        return kon_model
    else:
        logger.warning(
            f"Style {style} not found. Defaulting to Makoto Shinkai"
        )
        return shinkai_model


def inference(img, style):
    # load image
    input_image = img.convert(COLOUR_MODEL)
    input_image = np.asarray(input_image)
    # RGB -> BGR
    input_image = input_image[:, :, [2, 1, 0]]
    input_image = transforms.ToTensor()(input_image).unsqueeze(0)
    # preprocess, (-1, 1)
    input_image = -1 + 2 * input_image

    if disable_gpu:
        input_image = Variable(input_image).float()
    else:
        input_image = Variable(input_image).cuda()

    # forward
    model = get_model(style)
    output_image = model(input_image)
    output_image = output_image[0]
    # BGR -> RGB
    output_image = output_image[[2, 1, 0], :, :]
    output_image = output_image.data.cpu().float() * 0.5 + 0.5

    return transforms.ToPILImage()(output_image)


title = "Anime Background GAN"
description = "Gradio Demo for CartoonGAN by Chen Et. Al. Models are Shinkai Makoto, Hosoda Mamoru, Kon Satoshi, and Miyazaki Hayao."
article = "<p style='text-align: center'><a href='http://openaccess.thecvf.com/content_cvpr_2018/CameraReady/2205.pdf' target='_blank'>CartoonGAN Whitepaper from Chen et.al</a></p><p style='text-align: center'><a href='https://github.com/venture-anime/cartoongan-pytorch' target='_blank'>Github Repo</a></p><p style='text-align: center'><a href='https://github.com/Yijunmaverick/CartoonGAN-Test-Pytorch-Torch' target='_blank'>Original Implementation from Yijunmaverick</a></p><center><img src='https://visitor-badge.glitch.me/badge?page_id=akiyamasho' alt='visitor badge'></center></p>"

examples = [
    ["examples/garden_in.jpg", STYLE_SHINKAI],
    ["examples/library_in.jpg", STYLE_KON],
]


gr.Interface(
    fn=inference,
    inputs=[
        gr.inputs.Image(type="pil", label="Input Photo"),
        gradio.inputs.Dropdown(
            STYLE_CHOICE_LIST,
            type="value",
            default=DEFAULT_STYLE,
            label="Style",
        ),
    ],
    outputs=gr.outputs.Image(type="pil"),
    title=title,
    description=description,
    article=article,
    examples=examples,
    allow_flagging=False,
    allow_screenshot=False,
    enable_queue=True,
).launch()