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LIME_batch_gpu0.py
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LIME_batch_gpu0.py
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from __future__ import absolute_import
import warnings
warnings.simplefilter('ignore')
import time, os, sys, cv2, time, argparse
import torch
import random
from srblib import abs_path
import numpy as np
from formal_utils import *
from PIL import Image
import torch.nn as nn
import torch.nn.functional as F
from torchvision import models, transforms
from lime import lime_image
from lime.wrappers.scikit_image import SegmentationAlgorithm
import matplotlib.pyplot as plt
from tqdm import tqdm, trange
import skimage
print('Explicacion LIME GPU 0')
# Fixing for deterministic results
torch.backends.cudnn.deterministic = True
torch.backends.cudnn.benchmark = False
lime_background_pixel = 0
lime_superpixel_num = 50
lime_num_samples = 1000
lime_superpixel_seed = 0
lime_explainer_seed = 0
batch_size = 100
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.cuda()
model.eval()
for p in model.parameters():
p.requires_grad = 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'
img_name_list = []
with open(text_file, 'r') as f:
for line in f:
img_name_list.append(line.split('\n')[0])
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)
img = np.array(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
def get_pytorch_preprocess_transform():
normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])
transf = transforms.Compose([
transforms.ToTensor(),
normalize
])
return transf
def get_pil_transform():
transf = transforms.Compose([
transforms.Resize((256, 256)),
transforms.CenterCrop(224)
])
return transf
pytorch_explainer = lime_image.LimeImageExplainer(random_state=lime_explainer_seed)
slic_parameters = {'n_segments': lime_superpixel_num, 'compactness': 30, 'sigma': 3}
segmenter = SegmentationAlgorithm('slic', **slic_parameters)
pill_transf = get_pil_transform()
#########################################################
# Function to compute probabilities
# Pytorch
pytorch_preprocess_transform = get_pytorch_preprocess_transform()
def pytorch_batch_predict(images):
batch = torch.stack(tuple(pytorch_preprocess_transform(i) for i in images), dim=0)
batch = batch.cuda()
probs = model(batch)
return probs.cpu().numpy()
def LIME_explanation(img, target):
# This image will be passed to Lime Explainer
labels = (target,)
# LIME analysis
lime_img = np.squeeze(img.numpy())
pytorch_lime_explanation = pytorch_explainer.explain_instance(lime_img, pytorch_batch_predict,
batch_size=batch_size,
#segmentation_fn=segmenter,
top_labels=None, labels=labels,
hide_color=None,
num_samples=lime_num_samples,
random_seed=lime_superpixel_seed,
)
pytorch_segments = pytorch_lime_explanation.segments
pytorch_heatmap = np.zeros(pytorch_segments.shape)
local_exp = pytorch_lime_explanation.local_exp
exp = local_exp[target]
for i, (seg_idx, seg_val) in enumerate(exp):
pytorch_heatmap[pytorch_segments == seg_idx] = seg_val
return pytorch_heatmap
random.seed(0)
init_time = time.time()
val_dataset = DataProcessing(base_img_dir, pill_transf, 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)
# save_path = './resnet50_LIME'
# save_path = './vgg16_LIME'
save_path = './alexnet_LIME'
iterator = tqdm(enumerate(val_loader), total=len(val_loader), desc='batch')
for i, (image, target, file_name) in iterator:
mask = LIME_explanation(image, target.item())
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)), mask)
print('Time taken: {:.3f} secs'.format(time.time() - init_time))