-
Notifications
You must be signed in to change notification settings - Fork 14
/
test_enhance.py
169 lines (129 loc) · 6.43 KB
/
test_enhance.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
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
from __future__ import print_function
import argparse
import os
import torch
from modules import VAE_SR, VAE_denoise_vali, VGGFeatureExtractor
import torch.nn.functional as F
import torchvision.transforms as transforms
from torch.utils.data import DataLoader
import numpy as np
from os.path import join
import time
from collections import OrderedDict
import math
from datasets import is_image_file
from image_utils import *
from PIL import Image, ImageOps
from os import listdir
from prepare_images import *
import torch.utils.data as utils
from torch.autograd import Variable
import os
# Training settings
parser = argparse.ArgumentParser(description='PyTorch Super Res Example')
parser.add_argument('--upscale_factor', type=int, default=4, help="super resolution upscale factor")
parser.add_argument('--testBatchSize', type=int, default=64, help='testing batch size')
parser.add_argument('--gpu_mode', type=bool, default=True)
parser.add_argument('--chop_forward', type=bool, default=True)
parser.add_argument('--patch_size', type=int, default=128, help='0 to use original frame size')
parser.add_argument('--stride', type=int, default=8, help='0 to use original patch size')
parser.add_argument('--threads', type=int, default=6, help='number of threads for data loader to use')
parser.add_argument('--seed', type=int, default=123, help='random seed to use. Default=123')
parser.add_argument('--gpus', default=2, type=int, help='number of gpu')
parser.add_argument('--image_dataset', type=str, default='Test')
parser.add_argument('--model_type', type=str, default='VAE')
parser.add_argument('--output', default='Result', help='Location to save checkpoint models')
parser.add_argument('--model_denoiser', default='models/VAE_denoiser.pth', help='sr pretrained base model')
parser.add_argument('--model_SR', default='models/VAE_SR.pth', help='feature sr pretrained base model')
opt = parser.parse_args()
gpus_list = range(opt.gpus)
print(opt)
cuda = opt.gpu_mode
if cuda and not torch.cuda.is_available():
raise Exception("No GPU found, please run without --cuda")
torch.manual_seed(opt.seed)
if cuda:
torch.cuda.manual_seed(opt.seed)
print('===> Building model ', opt.model_type)
denoiser = VAE_denoise_vali(input_dim=3, dim=32, feat_size=8, z_dim=512, prior='standard')
model = VAE_SR(input_dim=3, dim=64, scale_factor=opt.upscale_factor)
denoiser = torch.nn.DataParallel(denoiser, device_ids=gpus_list)
model = torch.nn.DataParallel(model, device_ids=gpus_list)
if cuda:
denoiser = denoiser.cuda(gpus_list[0])
model = model.cuda(gpus_list[0])
print('===> Loading datasets')
if os.path.exists(opt.model_denoiser):
# denoiser.load_state_dict(torch.load(opt.model_denoiser, map_location=lambda storage, loc: storage))
pretrained_dict = torch.load(opt.model_denoiser, map_location=lambda storage, loc: storage)
model_dict = denoiser.state_dict()
pretrained_dict = {k: v for k, v in pretrained_dict.items() if k in model_dict}
model_dict.update(pretrained_dict)
denoiser.load_state_dict(model_dict)
print('Pre-trained Denoiser model is loaded.')
if os.path.exists(opt.model_SR):
model.load_state_dict(torch.load(opt.model_SR, map_location=lambda storage, loc: storage))
print('Pre-trained SR model is loaded.')
def eval():
denoiser.eval()
model.eval()
LR_image = [join(opt.input, x) for x in listdir(opt.input) if is_image_file(x)]
SR_image = [join(opt.output, x) for x in listdir(opt.input) if is_image_file(x)]
avg_psnr_predicted = 0.0
for i in range(LR_image.__len__()):
t0 = time.time()
LR = Image.open(LR_image[i]).convert('RGB')
LR_90 = LR.transpose(Image.ROTATE_90)
LR_180 = LR.transpose(Image.ROTATE_180)
LR_270 = LR.transpose(Image.ROTATE_270)
LR_f = LR.transpose(Image.FLIP_LEFT_RIGHT)
LR_90f = LR_90.transpose(Image.FLIP_LEFT_RIGHT)
LR_180f = LR_180.transpose(Image.FLIP_LEFT_RIGHT)
LR_270f = LR_270.transpose(Image.FLIP_LEFT_RIGHT)
with torch.no_grad():
pred = chop_forward(LR)
pred_90 = chop_forward(LR_90)
pred_180 = chop_forward(LR_180)
pred_270 = chop_forward(LR_270)
pred_f = chop_forward(LR_f)
pred_90f = chop_forward(LR_90f)
pred_180f = chop_forward(LR_180f)
pred_270f = chop_forward(LR_270f)
pred_90 = np.rot90(pred_90, 3)
pred_180 = np.rot90(pred_180, 2)
pred_270 = np.rot90(pred_270, 1)
pred_f = np.fliplr(pred_f)
pred_90f = np.rot90(np.fliplr(pred_90f), 3)
pred_180f = np.rot90(np.fliplr(pred_180f), 2)
pred_270f = np.rot90(np.fliplr(pred_270f), 1)
prediction = (pred + pred_90 + pred_180 + pred_270 + pred_f + pred_90f + pred_180f + pred_270f) * 255.0 / 8.0
t1 = time.time()
print("===> Processing: %s || Timer: %.4f sec." % (str(i), (t1 - t0)))
prediction = prediction.clip(0, 255)
Image.fromarray(np.uint8(prediction)).save(SR_image[i])
def chop_forward(img):
img = transform(img).unsqueeze(0)
testset = utils.TensorDataset(img)
test_dataloader = utils.DataLoader(testset, num_workers=opt.threads,
drop_last=False, batch_size=opt.testBatchSize, shuffle=False)
for iteration, batch in enumerate(test_dataloader, 1):
input = Variable(batch[0]).cuda(gpus_list[0])
batch_size, channels, img_height, img_width = input.size()
lowres_patches = patchify_tensor(input, patch_size=opt.patch_size, overlap=opt.stride)
n_patches = lowres_patches.size(0)
out_box = []
with torch.no_grad():
for p in range(n_patches):
LR_input = lowres_patches[p:p + 1]
std_z = torch.from_numpy(np.random.normal(0, 1, (input.shape[0], 512))).float()
z = Variable(std_z, requires_grad=False).cuda(gpus_list[0])
Denoise_LR = denoiser(LR_input, z)
SR = model(Denoise_LR)
out_box.append(SR)
out_box = torch.cat(out_box, 0)
SR = recompose_tensor(out_box, opt.upscale_factor * img_height, opt.upscale_factor * img_width,
overlap=opt.upscale_factor * opt.stride)
SR = SR.data[0].cpu().permute(1, 2, 0).numpy()
return SR
##Eval Start!!!!
eval()