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cam_2x2.py
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cam_2x2.py
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from PIL import Image
import numpy as np
import torch
import matplotlib.pyplot as plt
import math
import random
import glob
import pickle
from tools.utils import *
import argparse
from tools.config import *
parser = argparse.ArgumentParser(description='cam')
parser.add_argument('--model', default='vgg16', type=str, help='model: [vgg16, resnet50, densenet121, darts].')
parser.add_argument('--dataset', default='imagenet', type=str, help='dataset: [imagenet, place].')
parser.add_argument('--epoch', default=20, type=int, help='epoch: from 0 to 20.')
parser.add_argument('--mask_rate', default=0.05, type=float, help='the mask rate of cam.')
args = parser.parse_args()
if args.dataset == 'imagenet' and args.model == 'vgg16':
from models.imagenet.vgg16 import *
net = vgg16(finding_masks=False).cuda().eval()
if args.dataset == 'imagenet' and args.model == 'resnet50':
from models.imagenet.resnet50 import *
net = resnet50(finding_masks=False).cuda().eval()
if args.dataset == 'imagenet' and args.model == 'densenet121':
from models.imagenet.densenet121 import *
net = densenet121(finding_masks=False).cuda().eval()
if args.dataset == 'imagenet' and args.model == 'darts':
from models.imagenet.darts import *
net = darts(finding_masks=False).cuda().eval()
if args.dataset == 'place' and args.model == 'vgg16':
from models.place.vgg16 import *
net = vgg16(finding_masks=False).cuda().eval()
if args.dataset == 'place' and args.model == 'resnet50':
from models.place.resnet50 import *
net = resnet50(finding_masks=False).cuda().eval()
if args.dataset == 'place' and args.model == 'densenet121':
from models.place.densenet121 import *
net = densenet121(finding_masks=False).cuda().eval()
if args.dataset == 'place' and args.model == 'darts':
from models.place.darts import *
net = darts(finding_masks=False).cuda().eval()
class CamExtractor():
def __init__(self, model, target_layer):
self.model = model
self.target_layer = target_layer
self.conv_out = None
def hook_mask(self, layer):
def hook_function(module, input, output):
self.conv_out = output
return layer.register_forward_hook(hook_function)
def forward_pass(self, x):
self.hook_mask(self.target_layer)
x = self.model(x)
return self.conv_out, x
class GradCam():
def __init__(self, model, target_layer):
self.model = model
self.model.eval()
self.extractor = CamExtractor(self.model, target_layer)
def generate_cam(self, input_image):
conv_output, model_output = self.extractor.forward_pass(input_image)
target = conv_output.data.cpu().numpy()[0]
cam = np.zeros(target.shape[1:], dtype=np.float32)
for i in range(target.shape[0]):
cam += target[i, :, :]
cam = (cam - np.min(cam)) / (np.max(cam) - np.min(cam))
cam = np.uint8(cam * 255)
# cam = np.uint8(Image.fromarray(cam).resize((input_image.shape[2],
# input_image.shape[3]), Image.ANTIALIAS)) / 255
cam = np.uint8(Image.fromarray(cam).resize((input_image.shape[2],
input_image.shape[3]), Image.BICUBIC)) / 255
return cam
# 生成2x2图像部分
def merge(img_list):
IMAGE_ROW = 2
IMAGE_COLUMN = 2
IMAGE_SIZE = 224
merge_img = Image.new('RGB', (IMAGE_COLUMN * IMAGE_SIZE, IMAGE_ROW * IMAGE_SIZE))
for idx in range(IMAGE_ROW):
for jdx in range(IMAGE_COLUMN):
img_num = idx * IMAGE_COLUMN + jdx
merge_img.paste(img_list[img_num], (jdx*IMAGE_SIZE, idx*IMAGE_SIZE))
return merge_img
def rand_img(class_idx=0):
IMAGE_SIZE = 224
if args.dataset == 'imagenet':
imagenet_root = '{}/val'.format(get_dataset_dir("imagenet"))
else:
imagenet_root = '{}/val'.format(get_dataset_dir("place"))
dirs = glob.glob('{}/*'.format(imagenet_root))
dirs.sort()
imgs = glob.glob('{}/*'.format(dirs[class_idx]))
return Image.open(imgs[random.randint(0, len(imgs)-1)]).resize((IMAGE_SIZE, IMAGE_SIZE), Image.ANTIALIAS)
def generate_2x2_images():
if not os.path.exists('./results'):
os.makedirs('./results')
if os.path.exists('./results/{}_2x2'.format(args.dataset)):
return
if not os.path.exists('./results/{}_2x2'.format(args.dataset)):
os.makedirs('./results/{}_2x2'.format(args.dataset))
if args.dataset == "imagenet":
class_nums = 1000
else:
class_nums = 365
for idx in range(100):
rand_class = random.sample(range(class_nums), 4)
img_list = [rand_img(rand_class[idx]) for idx in range(4)]
merge_img = merge(img_list)
merge_img.save('./results/{}_2x2/{:03d}_{:03d}_{:03d}_{:03d}.png'.format(args.dataset, rand_class[0], rand_class[1], rand_class[2], rand_class[3]))
def return_binary(img, cam, cnt, mask_rate=0.05, is_focus=True):
if is_focus:
threshold = sorted(cam.copy().reshape(-1))[-int(mask_rate*224*224)]
cam = np.where(cam.copy() > threshold, 0, 0.5)
else:
threshold = sorted(cam.copy().reshape(-1))[int(mask_rate*224*224)]
cam = np.where(cam.copy() < threshold, 0, 0.5)
img_copy = np.array(img.copy()).transpose(2, 0, 1)
th = [0, 0, 0]
for i in range(3):
for j in range(224):
for k in range(224):
if cam[j][k] != 0:
img_copy[i][j][k] = int(float(img_copy[i][j][k]) + (th[i] - img_copy[i][j][k]) * 0.8)
img_copy = img_copy.transpose(1, 2, 0)
return Image.fromarray(img_copy)
def draw_binary(net, img, mask_rate=0.2, is_focus=True, class_list=[1,2,3,4]):
IMAGE_SIZE = 224
file_name = '{:03d}_{:03d}_{:03d}_{:03d}'.format(class_list[0], class_list[1], class_list[2], class_list[3])
if not os.path.exists('./results'):
os.makedirs('./results')
if not os.path.exists('./results/cam_2x2'):
os.makedirs('./results/cam_2x2')
if not os.path.exists('./results/cam_2x2/{}'.format(args.dataset)):
os.makedirs('./results/cam_2x2/{}'.format(args.dataset))
if not os.path.exists('./results/cam_2x2/{}/{}'.format(args.dataset, args.model)):
os.makedirs('./results/cam_2x2/{}/{}'.format(args.dataset, args.model))
if not os.path.exists('./results/cam_2x2/{}/{}/{}'.format(args.dataset, args.model, file_name)):
os.makedirs('./results/cam_2x2/{}/{}/{}'.format(args.dataset, args.model, file_name))
img.save('./results/cam_2x2/{}/{}/{}/origin_00.png'.format(args.dataset, args.model, file_name))
origin_img, img = preprocess_image(img)
img.requires_grad = False
cnt = 0
if args.model == 'vgg16':
target_layers = [net.features[2], net.features[6], net.features[9], net.features[13], net.features[16],
net.features[19], net.features[23], net.features[26], net.features[29], net.features[33],
net.features[36], net.features[39], net.mask]
if args.model == 'resnet50':
target_layers = [net.mask, net.layer1[0].mask, net.layer1[1].mask, net.layer1[2].mask, net.layer2[0].mask,
net.layer2[1].mask, net.layer2[2].mask, net.layer2[3].mask, net.layer3[0].mask,
net.layer3[1].mask, net.layer3[2].mask, net.layer3[3].mask, net.layer3[4].mask,
net.layer3[5].mask, net.layer4[0].mask, net.layer4[1].mask, net.layer4[2].mask]
if args.model == 'densenet121':
target_layers = [net.features.mask0, net.features.mask1, net.features.transition1.mask, net.features.mask2,
net.features.transition2.mask, net.features.mask3, net.features.transition3.mask,
net.features.mask5]
if args.model == 'darts':
target_layers = [net.mask0, net.mask1, net.cells[0].mask, net.cells[1].mask, net.cells[2].mask,
net.cells[3].mask, net.cells[4].mask, net.cells[5].mask, net.cells[6].mask, net.cells[7].mask,
net.cells[8].mask, net.cells[9].mask, net.cells[10].mask, net.cells[11].mask,
net.cells[12].mask, net.cells[13].mask]
accuracy = [[] for i in range(len(target_layers))]
for layer_num in range(len(target_layers)):
cnt += 1
img_list = []
for idx in range(4):
mask_dir = './checkpoint/{}/{}/{:03d}/net_iter{:03d}.pth'.format(args.dataset, args.model, class_list[idx], args.epoch)
# mask_dir = '../NAD_CVPR/resnet50/checkpoints/resnet50_imagenet/{:03d}/net_iter{:03d}.pth'.format(class_list[idx], args.epoch)
# mask_dir = '../CFPv6/checkpoints/{}/{}/{:03d}/net_iter{:03d}.pth'.format(args.dataset, args.model, class_list[idx], args.epoch)
# mask_dir = './checkpoint/imagenet/resnet50/{:03d}/net_iter{:03d}.pth'.format(class_list[idx], args.epoch)
net.load_masks(mask_dir)
with torch.no_grad():
masks = net.get_masks()
for key in masks.keys():
mask = (masks[key].data[...])
masks[key].mask[...] = torch.sigmoid(mask).gt(0.5)
grad_cam = GradCam(net, target_layer=target_layers[layer_num])
cam = grad_cam.generate_cam(img.cuda())
threshold = sorted(cam.copy().reshape(-1))[-int(mask_rate*224*224)]
cam_copy = np.where(cam.copy() > threshold, 1, 0)
accuracy[layer_num].append(cam_copy[IMAGE_SIZE//2*(idx//2):IMAGE_SIZE//2*(idx//2+1), IMAGE_SIZE//2*(idx%2):IMAGE_SIZE//2*(idx%2+1)].sum()/cam_copy.sum())
img_list.append(return_binary(origin_img, cam, cnt, mask_rate, is_focus))
mix_img = merge(img_list)
mix_img.save('./results/cam_2x2/{}/{}/{}/layer_{:02d}.png'.format(args.dataset, args.model, file_name, cnt))
res = [np.array(tmp).mean() for tmp in accuracy]
return res
def test():
my_list = []
ff = open('./results/cam_2x2/{}/{}/000_000_000_000/ave_list.pkl'.format(args.dataset, args.model), 'rb')
my_list = pickle.load(ff)
print(np.array(my_list).shape)
sum_1 = sum_2 = sum_3 = 0
for idx in range(100):
sum_1 += my_list[np.array(my_list).shape[0]-1][idx]
sum_2 += my_list[np.array(my_list).shape[0]-2][idx]
sum_3 += my_list[np.array(my_list).shape[0]-3][idx]
sum_1 /= 100
sum_2 /= 100
sum_3 /= 100
print('{:.5f} {:.5f} {:.5f}'.format(sum_1, sum_2, sum_3))
def main():
IMAGE_SIZE = 224
if args.model == 'vgg16':
layer_numbers = 13
if args.model == 'resnet50':
layer_numbers = 17
if args.model == 'densenet121':
layer_numbers = 8
if args.model == 'darts':
layer_numbers = 16
ave_list = [[] for i in range(layer_numbers)]
image_name_list = glob.glob('./results/{}_2x2/*'.format(args.dataset))
for tt in range(len(image_name_list)):
rand_class = image_name_list[tt].split('/')[-1].split('.')[0].split('_')
for _ in range(len(rand_class)):
rand_class[_] = int(rand_class[_])
merge_img = Image.open(image_name_list[tt])
accuracy = draw_binary(net, merge_img, mask_rate=0.1, is_focus=True, class_list=rand_class)
# ave_list.append(accuracy)
for i in range(layer_numbers):
ave_list[i].append(accuracy[i])
print('{:02d} {}'.format(tt, accuracy))
all_ave_accuracy = []
all_err_std = []
for i in range(layer_numbers):
all_ave_accuracy.append(np.array(ave_list[i]).mean())
all_err_std.append(np.array(ave_list[i]).std())
print('ave {}'.format(all_ave_accuracy))
print('std {}'.format(all_err_std))
if not os.path.exists('./results/cam_2x2/{}/{}/000_000_000_000'.format(args.dataset, args.model)):
os.makedirs('./results/cam_2x2/{}/{}/000_000_000_000'.format(args.dataset, args.model))
ff = open('./results/cam_2x2/{}/{}/000_000_000_000/ave_list.pkl'.format(args.dataset, args.model), 'wb')
pickle.dump(ave_list, ff)
ff.close()
if __name__ == '__main__':
# img 001 320 487 489
# goldfish | damselfly | mobile phone | chainlink fence
# pls 007 257 066 247
# amusement park | parking lot | bridge | oilrig
generate_2x2_images()
print("generate complete")
main()
test()