-
Notifications
You must be signed in to change notification settings - Fork 0
/
prueba_dataset_fetcher.py
153 lines (125 loc) · 5.14 KB
/
prueba_dataset_fetcher.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
import argparse
import os
import random
import shutil
import time
import warnings
from srblib import abs_path
from PIL import ImageFilter, Image
import torch
import torch.nn as nn
import torch.nn.parallel
import torch.backends.cudnn as cudnn
import torch.optim
import torch.utils.data
import torch.utils.data.distributed
import torchvision.transforms as transforms
import torchvision.datasets as datasets
import torchvision.models as models
import numpy as np
import matplotlib.pyplot as plt
from tqdm import tqdm
import shutil
import skimage
val_dir = './val'
imagenet_val_xml_path = './val_bb'
imagenet_val_path = './val/'
base_img_dir = abs_path(imagenet_val_path)
input_dir_path = 'images_list.txt'
text_file = abs_path(input_dir_path)
imagenet_class_mappings = './imagenet_class_mappings'
img_name_list = []
with open(text_file, 'r') as f:
for line in f:
img_name_list.append(line.split('\n')[0])
def imagenet_label_mappings():
fileName = os.path.join(imagenet_class_mappings, 'imagenet_label_mapping')
with open(fileName, 'r') as f:
image_label_mapping = {int(x.split(":")[0]): x.split(":")[1].strip()
for x in f.readlines() if len(x.strip()) > 0}
return image_label_mapping
class DataProcessing:
def __init__(self, data_path, transform, img_idxs=[0, 1], if_noise=0, noise_var=0.0):
self.data_path = data_path
self.transform = transform
self.if_noise = if_noise
self.noise_mean = 0
self.noise_var = noise_var
img_list = img_name_list[img_idxs[0]:img_idxs[1]]
self.img_filenames = [os.path.join(data_path, f'{i}.JPEG') for i in img_list]
#print('img filenames=', os.path.join(self.img_filenames[0]))
#self.img_filenames.sort()
def __getitem__(self, index):
img = Image.open(os.path.join(self.data_path, self.img_filenames[index])).convert('RGB')
target = self.get_image_class(os.path.join(self.data_path, self.img_filenames[index]))
if self.if_noise == 1:
img = skimage.util.random_noise(np.asarray(img), mode='gaussian',
mean=self.noise_mean, var=self.noise_var,
) # numpy, dtype=float64,range (0, 1)
img = Image.fromarray(np.uint8(img * 255))
#print(img2.max(), img2.min())
img = self.transform(img)
return img, target, os.path.join(self.data_path, self.img_filenames[index])
#return img, target
def __len__(self):
return len(self.img_filenames)
def get_image_class(self, filepath):
# ImageNet 2012 validation set images?
with open(os.path.join(imagenet_class_mappings, "ground_truth_val2012")) as f:
ground_truth_val2012 = {x.split()[0]: int(x.split()[1])
for x in f.readlines() if len(x.strip()) > 0}
def get_class(f):
ret = ground_truth_val2012.get(f, None)
return ret
image_class = get_class(filepath.split('/')[-1])
return image_class
transform_val = transforms.Compose([
transforms.Resize((256,256)),
transforms.CenterCrop(224),
transforms.ToTensor(),
transforms.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225]),
])
# Plots image from tensor
def tensor_imshow(inp, title=None, **kwargs):
"""Imshow for Tensor."""
inp = inp.numpy().transpose((1, 2, 0))
# Mean and std for ImageNet
mean = np.array([0.485, 0.456, 0.406])
std = np.array([0.229, 0.224, 0.225])
inp = std * inp + mean
inp = np.clip(inp, 0, 1)
plt.imshow(inp, **kwargs)
if title is not None:
plt.title(title)
plt.axis('off')
plt.show()
init_time = time.time()
val_dataset = DataProcessing(base_img_dir, transform_val, img_idxs=[0, 25], if_noise=0, noise_var=0) #0.1 0.5 1.0 1.5
val_loader = torch.utils.data.DataLoader(val_dataset, batch_size=10, shuffle=False, num_workers=24, pin_memory=True)
# especificar cual gpu 0 o 1
torch.cuda.set_device(0)
model = models.googlenet(pretrained=True)
#model = torch.nn.DataParallel(model, device_ids=[0,1])
model.cuda()
model.eval()
im_label_map = imagenet_label_mappings()
iterator = tqdm(enumerate(val_loader), total=len(val_loader), desc='batch')
for i, (images, target, path) in iterator:
images = images.cuda()
pred = torch.nn.Softmax(dim=1)(model(images))
target = target.numpy()
# for i, file in enumerate(path):
# print('./dataset/{}.JPEG'.format(i))
# shutil.copyfile(file, './dataset/{}'.format(file.split('/')[-1]))
# shutil.copyfile(file, './dataset/{}.JPEG'.format(i))
for j, img in enumerate(images):
target_img = target[j].item()
pr, cl = torch.topk(pred[j], 1)
pr = pr.cpu().detach().numpy()[0]
cl = cl.cpu().detach().numpy()[0]
# title = 'p={:.2f} cat={} true={}'.format(pr, im_label_map.get(cl), im_label_map.get(target_img))
# title = 'p={:.2f} cat={}'.format(pr, im_label_map.get(cl))
title = path[j].split('/')[-1].split('.JPEG')[0]
tensor_imshow(img.cpu(), title=title)
print('Time taken: {:.3f}'.format(time.time() - init_time))