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local_xai_attr.py
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#!/usr/bin/env python3
# -*- coding: utf-8 -*-
# File : local_xai_attr.py
# Modified : 08.03.2022
# By : Sandra Carrasco <sandra.carrasco@ai.se>
from torchvision import transforms
import torch
import os
from PIL import Image
from efficientnet_pytorch import EfficientNet
from torchvision.models import resnet50
from utils_xai import Net
import json
import random
import cv2
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.colors import LinearSegmentedColormap
# Captum
from captum.attr import GuidedGradCam, IntegratedGradients, GradientShap, Occlusion, NoiseTunnel, Saliency
from captum.attr import visualization as viz
# Pytorch GradCam <https://github.com/jacobgil/pytorch-grad-cam>
from pytorch_grad_cam import GradCAM, ScoreCAM, GradCAMPlusPlus, AblationCAM, XGradCAM, EigenCAM, FullGrad, LayerCAM
from pytorch_grad_cam.utils.model_targets import ClassifierOutputTarget
from pytorch_grad_cam.utils.image import show_cam_on_image
torch.manual_seed(0)
np.random.seed(0)
# Setting up GPU for processing or CPU if GPU isn't available
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
default_cmap = LinearSegmentedColormap.from_list('custom blue',
[(0, '#ffffff'),
(0.25, '#000000'),
(1, '#000000')], N=256)
transform = transforms.ToTensor()
testing_transforms = transforms.Compose([transforms.Resize(256),
transforms.CenterCrop(256),
transforms.ToTensor(),
transforms.Normalize([0.485, 0.456, 0.406],
[0.229, 0.224, 0.225])])
# Path to the images
directories = ["/workspace/stylegan2-ada-pytorch/processed_dataset_256_SAM","/workspace/stylegan2-ada-pytorch/processed_dataset_256"]
filename = "dataset.json"
# input_images = [str(f) for f in sorted(Path(directories[0]).rglob('*')) if os.path.isfile(f)]
# y = [1 for n in range(len(input_images))] #[0 if f.split('.jpg')[0][-1] == '0' else 1 for f in input_images]
# data_df = pd.DataFrame({'image_name': input_images, 'target': y})
"""
# For testing with ISIC dataset
df = pd.read_csv(os.path.join('/workspace/melanoma_isic_dataset' , 'train_concat.csv'))
train_img_dir = os.path.join('/workspace/melanoma_isic_dataset' ,'train/train/')
train_split, valid_split = train_test_split (df, stratify=df.target, test_size = 0.20, random_state=42)
validation_df=pd.DataFrame(valid_split)
validation_df['image_name'] = [os.path.join(train_img_dir, validation_df.iloc[index]['image_name'] + '.jpg') for index in range(len(validation_df))]
dataset = CustomDataset(df = validation_df, train = False, transforms = testing_transforms )
dataloader = torch.utils.data.DataLoader(dataset, batch_size=1, num_workers=4, shuffle=True)
"""
arch_r = resnet50(pretrained=True)
arch_ef = EfficientNet.from_pretrained('efficientnet-b2')
model_r = Net(arch=arch_r)
model_ef = Net(arch=arch_ef)
# summary(model, (3, 256, 256), device='cpu')
model_ef.load_state_dict(torch.load('/workspace/stylegan2-ada-pytorch/CNN_trainings/melanoma_model_0_0.9225_16_12_train_reals+15melanoma.pth'))
model_r.load_state_dict(torch.load('/workspace/stylegan2-ada-pytorch/training_classifiers_events/12_29/melanoma_model_0_0.9818_2021-12-29-resnet.pth'))
model_r.eval().to(device)
model_ef.eval().to(device)
# Captum - Construct different Attribution Methods objects
guided_gc_ef = GuidedGradCam(model_ef,model_ef.arch._conv_head)
guided_gc_r = GuidedGradCam(model_r, model_r.arch.layer4[-1])
integrated_gradients_ef = IntegratedGradients(model_ef)
integrated_gradients_r = IntegratedGradients(model_r)
saliency_ef = Saliency(model_ef)
saliency_r = Saliency(model_r)
occlusion_ef = Occlusion(model_ef)
occlusion_r = Occlusion(model_r)
gradient_shap_ef = GradientShap(model_ef)
gradient_shap_r = GradientShap(model_r)
# grad-cam library
target_layers_ef = [model_ef.arch._conv_head]
target_layers_r = [model_r.arch.layer4[-1]]
# Construct the CAM object once, and then re-use it on many images:
cam_ef = EigenCAM(model=model_ef, target_layers=target_layers_ef, use_cuda=True)
cam_r = EigenCAM(model=model_r, target_layers=target_layers_r, use_cuda=True)
for directory in directories:
with open(os.path.join(directory, filename)) as file:
data = json.load(file)['labels']
random.shuffle(data)
for i, (img, label) in enumerate(data):
img_dir = os.path.join(directory,img)
# for img_dir, image, label in dataloader:
# img_dir=img_dir[0]
# image=image.to(device)
image = torch.tensor(testing_transforms(Image.open(img_dir)).unsqueeze(0),
dtype=torch.float32).to(device)
pred_resnet = torch.sigmoid(model_r(image))
pred_effnet = torch.sigmoid(model_ef(image))
print(pred_resnet, pred_effnet, label)
#plot_diagnosis(img_dir, model_ef, label)
transposed_image = np.transpose(transform(Image.open(img_dir)).numpy(), (1,2,0))
rgb_img = cv2.imread(img_dir, 1)[:, :, ::-1]
rgb_img = np.float32(rgb_img) / 255
# The input tensor can be a batch tensor with several images.
grayscale_cam_ef = cam_ef(image, aug_smooth=True, eigen_smooth=True)
grayscale_cam_r = cam_r(image, aug_smooth=True, eigen_smooth=True)
visualization = show_cam_on_image(rgb_img, grayscale_cam_ef[0,:], use_rgb=True)
cv2.imwrite(f'/workspace/stylegan2-ada-pytorch/explainability_examples/grad_cam_ef'+ img_dir.split('/')[-1].split('.')[0] + '.jpg', visualization)
visualization = show_cam_on_image(rgb_img, grayscale_cam_r[0,:], use_rgb=True)
cv2.imwrite(f'/workspace/stylegan2-ada-pytorch/explainability_examples/grad_cam_r'+ img_dir.split('/')[-1].split('.')[0] + '.jpg', visualization)
# Occlusion based attribution
attr_occ = occlusion_r.attribute(image,
strides = (3, 8, 8),
sliding_window_shapes=(3,15, 15),
baselines=0)
_ = viz.visualize_image_attr_multiple(np.transpose(attr_occ.squeeze().detach().cpu().numpy(), (1,2,0)),
transposed_image,
["original_image", "blended_heat_map"],
["all", "all"],
show_colorbar=True,
outlier_perc=2,
)
plt.savefig('/workspace/stylegan2-ada-pytorch/explainability_examples/occlusion_' + img_dir.split('/')[-1].split('.')[0] + '.png')
# Defining baseline distribution of images
rand_img_dist = torch.cat([image * 0, image * 1])
attributions_gs = gradient_shap_ef.attribute(image,
n_samples=50,
stdevs=0.0001,
baselines=rand_img_dist)
_ = viz.visualize_image_attr_multiple(np.transpose(attributions_gs.squeeze().cpu().detach().numpy(), (1,2,0)),
transposed_image,
["original_image", "heat_map"],
["all", "absolute_value"],
#cmap=default_cmap,
show_colorbar=True)
plt.savefig('/workspace/stylegan2-ada-pytorch/explainability_examples/gradshap_' + img_dir.split('/')[-1].split('.')[0] + '.png')
attr_saliency = saliency_ef.attribute(image)
attr_guidedGC = guided_gc_ef.attribute(image)
attr_ig = integrated_gradients_ef.attribute(image)
transposed_attr_ig = np.transpose(attr_ig.squeeze().detach().cpu().numpy(), (1,2,0))
transposed_attr_guidedGC = np.transpose(attr_guidedGC.squeeze().detach().cpu().numpy(), (1,2,0))
transposed_attr_saliency = np.transpose(attr_saliency.squeeze().detach().cpu().numpy(), (1,2,0))
_ = viz.visualize_image_attr(transposed_attr_ig,
transposed_image,
method='heat_map',
#cmap=default_cmap,
show_colorbar=True,
sign='all',
outlier_perc=1)
plt.savefig('/workspace/stylegan2-ada-pytorch/explainability_examples/attr_ig.png')
# _ = viz.visualize_image_attr(transposed_attr_guidedGC,
# transposed_image,
# method='heat_map',
# #cmap=default_cmap,
# show_colorbar=True,
# sign='absolute_value',
# outlier_perc=1)
# plt.savefig('/workspace/stylegan2-ada-pytorch/attr_guidedGC_' + img_dir.split('/')[-1].split('.')[0] + '.png')
_ = viz.visualize_image_attr(transposed_attr_saliency,
transposed_image,
method='heat_map',
#cmap=default_cmap,
show_colorbar=True,
sign='all',
outlier_perc=1)
plt.savefig('/workspace/stylegan2-ada-pytorch/explainability_examples/saliency.png')
"""
# For a better visual of the attribution, the images between baseline and target are sampled
# using a noise tunnel (by adding gaussian noise). And when the gradients are calulcated,
# we smoothe them by calculating their mean squared.
noise_tunnel = NoiseTunnel(saliency_ef)
attributions_ig_nt = noise_tunnel.attribute(image, nt_samples=5, nt_type='smoothgrad')
transposed_attr_ig_nt = np.transpose(attributions_ig_nt.squeeze().detach().cpu().numpy(), (1,2,0))
_ = viz.visualize_image_attr_multiple(transposed_attr_ig_nt,
transposed_image,
["original_image", "heat_map"],
["all", "positive"],
#cmap=default_cmap,
show_colorbar=True)
plt.savefig('/workspace/stylegan2-ada-pytorch/explainability_examples/attr_ig_noisetunnel_' + img_dir.split('/')[-1].split('.')[0] + '.png')
noise_tunnel = NoiseTunnel(guided_gc)
attributions_guidedgc_nt = noise_tunnel.attribute(image, nt_samples=5, nt_type='smoothgrad')
transposed_attr_guidedgc_nt = np.transpose(attributions_guidedgc_nt.squeeze().detach().cpu().numpy(), (1,2,0))
_ = viz.visualize_image_attr_multiple(transposed_attr_guidedgc_nt,
transposed_image,
["original_image", "heat_map"],
["all", "all"],
#cmap=default_cmap,
show_colorbar=True)
plt.savefig('/workspace/stylegan2-ada-pytorch/explainability_examples/attr_guidedgc_noisetunnel_' + img_dir.split('/')[-1].split('.')[0] + '.png')
"""