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metodologia_recuperacion.py
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metodologia_recuperacion.py
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import torch
import os
import random
import shutil
import time
import numpy as np
import matplotlib.pyplot as plt
from srblib import abs_path
import torchvision.transforms as transforms
import torchvision.datasets as datasets
import torchvision.models as models
import skimage
from PIL import ImageFilter, Image
from tqdm import tqdm
# buenos resultados con MP, V2, V4?
# results_path = './googlenet_v2_gen'
results_path = './output_v4_0.05'
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, '{}.JPEG'.format(i)) for i in img_list]
self.mask_filenames = [os.path.join(results_path, '{}_mask.npy'.format(i)) for i in img_list]
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]))
mask = np.load(self.mask_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))
img = self.transform(img)
return img, mask.reshape(1, 224, 224), 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()
im_label_map = imagenet_label_mappings()
val_dataset = DataProcessing(base_img_dir, transform_val, img_idxs=[0, 25], if_noise=0, noise_var=0.0)
val_loader = torch.utils.data.DataLoader(val_dataset, batch_size=25, shuffle=False, num_workers=10, pin_memory=True)
torch.cuda.set_device(0)
model = models.googlenet(pretrained=True)
# model = models.resnet50(pretrained=True)
# model = models.vgg16(pretrained=True)
# model = models.alexnet(pretrained=True)
# model = torch.nn.DataParallel(model, device_ids=[0,1])
model.cuda()
model.eval()
for p in model.parameters():
p.requires_grad = False
########## Se carga el batch de imágenes adversarias ############
imgs_adv = np.load('adv_im_224.npy')
adv_batch = torch.from_numpy(imgs_adv).cuda()
adv_batch = transforms.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])(adv_batch)
preds_adv = torch.nn.Softmax(dim=1)(model(adv_batch[0:val_loader.batch_size])) # tensor(200,1000)
probs_adv, labels_adv = torch.topk(preds_adv, 10) # Top 10 predicciones adversarias (200, 10)
##################################################################
print('etiquetas adv')
for i in range(val_loader.batch_size):
prob = probs_adv.cpu().detach().numpy()[i]
pred_target = labels_adv.cpu().detach().numpy()[i]
pred_list = [i for i in pred_target]
print(pred_list)
iterator = tqdm(enumerate(val_loader), total=len(val_loader), desc='batch')
# top_cnt_full = torch.empty((val_loader.batch_size, 10), dtype=torch.bool)
top_cnt_full = []
for i, (images, masks, targets, file_names) in iterator:
images_orig = images.cuda()
targets_orig = targets.numpy() # las y originales
# predicciones originales
preds_orig = torch.nn.Softmax(dim=1)(model(images_orig))
probs_orig, labels_orig = torch.topk(preds_orig, 10) # Top 10 predicciones originales
# _, orig_labels = torch.max(preds_orig, 1)
# np.save('adv_orig_labels.npy', orig_labels.cpu().detach().numpy())
# reenmascaramiento de img adv con explicacion
extended_mask = masks.expand(masks.size(0), 3, 224, 224)
adv_masked = adv_batch[0:masks.size(0)].mul(extended_mask.cuda())
adv_masked = adv_masked.to(torch.float32)
# prediccion reenmascaramiento
preds_masked = torch.nn.Softmax(dim=1)(model(adv_masked)) # tensor(10,1000)
probs_masked, labels_masked = torch.topk(preds_masked, 1000) # predicciones recuperadas
for i in range(val_loader.batch_size): # se itera en el tamaño del batch
prob_orig = probs_orig.cpu().detach()[i] # (10, ) topk = 10
label_orig = labels_orig.cpu().detach()[i] # (10, ) topk = 10
prob_masked = probs_masked.cpu().detach()[i] # (1000,)
label_masked = labels_masked.cpu().detach()[i] # (1000,)
# pred_list = [im_label_map.get(label) for label in label_orig]
# label_orig_list = [label.item() for label in label_orig]
# label_masked_list = [label.item() for label in label_masked[0:10]]
pos_list = [torch.where(label_masked == label_orig_item)[0].item() for label_orig_item in label_orig]
pos_list_tensor = torch.tensor(pos_list)
top_cnt = [torch.where(pos_list_tensor <= i)[0].nelement() >= 1 for i in range(10)]
top_cnt_full.append(torch.tensor(top_cnt))
print('orig label, muestra', i, ': ', label_orig.tolist())
print('pos orig en recuperado ', pos_list)
print('lista top 10 recuperados acum ', top_cnt)
print('lista de recuperados ', label_masked[0:10].tolist())
print('')
# print(torch.stack(top_cnt_full))
print(torch.stack(top_cnt_full).sum(0)/val_loader.batch_size)
# inp = im[0].numpy().transpose((1, 2, 0))
# plt.imshow(inp)
# plt.axis('off')
# plt.show()