-
Notifications
You must be signed in to change notification settings - Fork 0
/
Copy pathutils.py
102 lines (86 loc) · 3.44 KB
/
utils.py
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
from torchvision import models, transforms
import torch
from PIL import Image
import torch.nn as nn
import streamlit as st
image_size = 64
batch_size = 32
stats = (0.5, 0.5, 0.5), (0.5, 0.5, 0.5)
latent_size = 150
def denorm(img_tensors):
return img_tensors * stats[1][0] + stats[0][0]
def new():
for i in range(1,2):
generator = torch.load("WEIGHTS/w1.pth", map_location = "cpu")
generator.eval()
device = torch.device('cpu')
generator.to(device);
y = generator(torch.randn(batch_size, latent_size, 1, 1))
gen_imgs = denorm(y.detach())
to_pil_image = transforms.ToPILImage()
result1 = to_pil_image(gen_imgs[0])
result2 = to_pil_image(gen_imgs[1])
result3 = to_pil_image(gen_imgs[2])
result4 = to_pil_image(gen_imgs[3])
result5 = to_pil_image(gen_imgs[4])
result6 = to_pil_image(gen_imgs[5])
result7 = to_pil_image(gen_imgs[6])
result8 = to_pil_image(gen_imgs[7])
result9 = to_pil_image(gen_imgs[8])
newsize = (70,70)
im1 = result1.resize(newsize)
im2 = result2.resize(newsize)
im3 = result3.resize(newsize)
im4 = result4.resize(newsize)
im5 = result5.resize(newsize)
im6 = result6.resize(newsize)
im7 = result7.resize(newsize)
im8 = result8.resize(newsize)
im9 = result9.resize(newsize)
im1 = im1.save("SAMPLES/new.jpg",quality=100)
im2 = im2.save("SAMPLES/new1.jpg",quality=100)
im3 = im3.save("SAMPLES/new2.jpg",quality=100)
im4 = im4.save("SAMPLES/new3.jpg",quality=100)
im5 = im5.save("SAMPLES/new4.jpg",quality=100)
im6 = im6.save("SAMPLES/new5.jpg",quality=100)
im7 = im7.save("SAMPLES/new6.jpg",quality=100)
im8 = im8.save("SAMPLES/new7.jpg",quality=100)
im9 = im9.save("SAMPLES/new8.jpg",quality=100)
st.image(["SAMPLES/new.jpg","SAMPLES/new1.jpg","SAMPLES/new2.jpg","SAMPLES/new3.jpg","SAMPLES/new4.jpg","SAMPLES/new5.jpg","SAMPLES/new6.jpg","SAMPLES/new7.jpg"],width = 70)
for i in range(1,2):
generator = torch.load("WEIGHTS/w2.pth", map_location = "cpu")
generator.eval()
device = torch.device('cpu')
generator.to(device);
y = generator(torch.randn(batch_size, latent_size, 1, 1))
gen_imgs = denorm(y.detach())
to_pil_image = transforms.ToPILImage()
result1 = to_pil_image(gen_imgs[0])
result2 = to_pil_image(gen_imgs[1])
result3 = to_pil_image(gen_imgs[2])
result4 = to_pil_image(gen_imgs[3])
result5 = to_pil_image(gen_imgs[4])
result6 = to_pil_image(gen_imgs[5])
result7 = to_pil_image(gen_imgs[6])
result8 = to_pil_image(gen_imgs[7])
result9 = to_pil_image(gen_imgs[8])
newsize = (70,70)
im1 = result1.resize(newsize)
im2 = result2.resize(newsize)
im3 = result3.resize(newsize)
im4 = result4.resize(newsize)
im5 = result5.resize(newsize)
im6 = result6.resize(newsize)
im7 = result7.resize(newsize)
im8 = result8.resize(newsize)
im9 = result9.resize(newsize)
im1 = im1.save("SAMPLES/new.jpg",quality=100)
im2 = im2.save("SAMPLES/new1.jpg",quality=100)
im3 = im3.save("SAMPLES/new2.jpg",quality=100)
im4 = im4.save("SAMPLES/new3.jpg",quality=100)
im5 = im5.save("SAMPLES/new4.jpg",quality=100)
im6 = im6.save("SAMPLES/new5.jpg",quality=100)
im7 = im7.save("SAMPLES/new6.jpg",quality=100)
im8 = im8.save("SAMPLES/new7.jpg",quality=100)
im9 = im9.save("SAMPLES/new8.jpg",quality=100)
st.image(["SAMPLES/new.jpg","SAMPLES/new1.jpg","SAMPLES/new2.jpg","SAMPLES/new3.jpg","SAMPLES/new4.jpg","SAMPLES/new5.jpg","SAMPLES/new6.jpg","SAMPLES/new7.jpg"],width = 70)