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RISE_batch_gpu0.py
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RISE_batch_gpu0.py
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import os
import numpy as np
from matplotlib import pyplot as plt
from tqdm import tqdm
import sys
from srblib import abs_path
import torch
import torch.nn as nn
import torch.backends.cudnn as cudnn
import torch.utils.data
import torchvision.transforms as transforms
import torchvision.datasets as datasets
import torchvision.models as models
import skimage
#bibliotecas RISE
sys.path.insert(0, './RISE')
from utilsrise import *
from explanations import RISE
print('Explicacion RISE GPU 0')
torch.backends.cudnn.deterministic = True
torch.backends.cudnn.benchmark = False
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'
torch.manual_seed(0)
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
im_label_map = imagenet_label_mappings()
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]
# 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))
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]),
])
# Size of imput images.
input_size = (224, 224)
# Size of batches for GPU.
# Use maximum number that the GPU allows.
gpu_batch = 200 # 125 #Máxima cantidad para una GPU (200 para alexnet)
# gpu_batch = 20 #Máxima cantidad para una GPU para VGG16
# Load black box model for explanations
torch.cuda.set_device(0) # especificar cual gpu 0 o 1
# model = models.googlenet(pretrained=True)
# model = models.resnet50(pretrained=True)
# model = models.vgg16(pretrained=True)
model = models.alexnet(pretrained=True)
model = nn.Sequential(model, nn.Softmax(dim=1))
model = model.eval()
model = model.cuda()
for p in model.parameters():
p.requires_grad = False
explainer = RISE(model, input_size, gpu_batch)
# Generate masks for RISE or use the saved ones.
maskspath = 'masks.npy'
generate_new = True
if generate_new or not os.path.isfile(maskspath):
explainer.generate_masks(N=6000, s=8, p1=0.1, savepath=maskspath)
else:
explainer.load_masks(maskspath)
print('Masks are loaded.')
val_dataset = DataProcessing(base_img_dir, transform_val, img_idxs=[0, 100], if_noise=0, noise_var=0.0)
val_loader = torch.utils.data.DataLoader(val_dataset, batch_size=1, shuffle=False, num_workers=10,
pin_memory=True)
print(' {: >5} images will be explained.'.format(len(val_loader) * val_loader.batch_size))
# # Get all predicted labels first
# target = np.empty(len(val_loader), int)
# for i, (img, _) in enumerate(tqdm(val_loader, total=len(val_loader), desc='Predicting labels')):
# p, c = torch.max(model(img.cuda()), dim=1)
# target[i] = c[0]
# save_path = './resnet50_RISE'
# save_path = './vgg16_RISE'
save_path = './alexnet_RISE'
# Get saliency maps for all images in val loader
explanations = np.empty((len(val_loader), *input_size))
for i, (img, target, file_name) in enumerate(tqdm(val_loader, total=len(val_loader), desc='Explaining images')):
saliency_maps = explainer(img.cuda())
explanations[i] = saliency_maps[target.item()].cpu().numpy()
mask_file = ('{}_mask.npy'.format(file_name[0].split('/')[-1].split('.JPEG')[0]))
np.save(os.path.abspath(os.path.join(save_path, mask_file)), explanations[i])
# Rutina para graficar las explicaciones
# for i, (img, _) in enumerate(data_loader):
# p, c = torch.max(model(img.cuda()), dim=1)
# p, c = p[0].item(), c[0].item()
#
# plt.figure(figsize=(10, 5))
# plt.subplot(121)
# plt.axis('off')
# plt.title('{:.2f}% {}'.format(100 * p, get_class_name(c)))
# tensor_imshow(img[0])
#
# plt.subplot(122)
# plt.axis('off')
# plt.title(get_class_name(c))
# tensor_imshow(img[0])
# sal = explanations[i]
# plt.imshow(sal, cmap='jet', alpha=0.5)
# # plt.colorbar(fraction=0.046, pad=0.04)
#
# plt.show()