-
Notifications
You must be signed in to change notification settings - Fork 21
/
demo_16x.py
192 lines (149 loc) · 6.57 KB
/
demo_16x.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
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
from __future__ import print_function
import argparse
import os
import torch
from model import ABPN_v3
import torchvision.transforms as transforms
from collections import OrderedDict
import logging
import numpy as np
from os.path import join
import time
import math
from dataset import is_image_file
import utils_logger
from PIL import Image, ImageOps
from os import listdir
from prepare_images import *
import torch.utils.data as utils
from torch.autograd import Variable
# Training settings
parser = argparse.ArgumentParser(description='PyTorch Super Res Example')
parser.add_argument('--upscale_factor', type=int, default=16, help="super resolution upscale factor")
parser.add_argument('--testBatchSize', type=int, default=4, 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=64, help='0 to use original frame size')
parser.add_argument('--stride', type=int, default=64, help='0 to use original patch size')
parser.add_argument('--threads', type=int, default=0, 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='LR')
parser.add_argument('--model_type', type=str, default='ABPN')
parser.add_argument('--image_output', default='Result', help='Location to save checkpoint models')
parser.add_argument('--model', default='Model/ABPN_16x.pth', help='sr pretrained base model')
opt = parser.parse_args()
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
print('===> Building model ', opt.model_type)
model = ABPN_v3(input_dim=3, dim=32)
model = model.to(device)
model = torch.nn.DataParallel(model)
model_name = os.path.join(opt.model)
if os.path.exists(model_name):
model.load_state_dict(torch.load(model_name, map_location=lambda storage, loc: storage))
print('Pre-trained SR model is loaded.')
img_splitter = ImageSplitter(opt.patch_size, opt.upscale_factor, opt.stride)
def eval():
utils_logger.logger_info('AIM-track', log_path='AIM-track.log')
logger = logging.getLogger('AIM-track')
model.eval()
for k, v in model.named_parameters():
v.requires_grad = False
# number of parameters
number_parameters = sum(map(lambda x: x.numel(), model.parameters()))
logger.info('Params number: {}'.format(number_parameters))
LR_filename = opt.image_dataset
LR_image = [join(LR_filename, x) for x in listdir(LR_filename) if is_image_file(x)]
SR_image = [join(os.path.join(opt.image_dataset, 'SR'), x) for x in listdir(LR_filename) if is_image_file(x)]
# record PSNR, runtime
test_results = OrderedDict()
test_results['runtime'] = []
logger.info(opt.image_dataset)
logger.info(opt.image_output)
idx = 0
for i in range(LR_image.__len__()):
idx += 1
img_name, ext = os.path.splitext(LR_image[i])
logger.info('{:->4d}--> {:>10s}'.format(idx, img_name+ext))
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, model)
pred_90 = chop_forward(LR_90, model)
pred_180 = chop_forward(LR_180, model)
pred_270 = chop_forward(LR_270, model)
pred_f = chop_forward(LR_f, model)
pred_90f = chop_forward(LR_90f, model)
pred_180f = chop_forward(LR_180f, model)
pred_270f = chop_forward(LR_270f, model)
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
prediction = prediction.clip(0, 255)
Image.fromarray(np.uint8(prediction)).save(SR_image[i])
# print("PSNR_predicted=", avg_psnr_predicted / count)
def modcrop(img, modulo):
(ih, iw) = img.size
ih = ih - (ih % modulo)
iw = iw - (iw % modulo)
img = img.crop((0, 0, ih, iw))
#y, cb, cr = img.split()
return img
def rgb2ycbcr(img, only_y=True):
'''same as matlab rgb2ycbcr
only_y: only return Y channel
Input:
float32, [0, 255]
float32, [0, 255]
'''
img.astype(np.float32)
# convert
if only_y:
rlt = np.dot(img, [65.481, 128.553, 24.966]) / 255.0 + 16.0
else:
rlt = np.matmul(img, [[65.481, -37.797, 112.0], [128.553, -74.203, -93.786],
[24.966, 112.0, -18.214]]) / 255.0 + [16, 128, 128]
rlt = rlt.round()
return rlt
def PSNR(pred, gt, shave_border):
pred = pred[shave_border:-shave_border, shave_border:-shave_border]
gt = gt[shave_border:-shave_border, shave_border:-shave_border]
imdff = pred - gt
rmse = math.sqrt(np.mean(imdff ** 2))
if rmse == 0:
return 100
return 20 * math.log10(255.0 / rmse)
transform = transforms.Compose([
transforms.ToTensor(), # range [0, 255] -> [0.0,1.0]
]
)
def chop_forward(img, network):
channel_swap = (1, 2, 0)
img = transform(img).unsqueeze(0)
img_patch = img_splitter.split_img_tensor(img)
testset = utils.TensorDataset(img_patch)
test_dataloader = utils.DataLoader(testset, num_workers=opt.threads,
drop_last=False, batch_size=opt.testBatchSize, shuffle=False)
out_box = []
for iteration, batch in enumerate(test_dataloader, 1):
input = Variable(batch[0]).to(device)
with torch.no_grad():
prediction = network(input)
for j in range(prediction.shape[0]):
out_box.append(prediction[j,:,:,:])
SR = img_splitter.merge_img_tensor(out_box)
SR = SR.data[0].numpy().transpose(channel_swap)
return SR
##Eval Start!!!!
eval()