FlipCAM-master/obtain_CAM_masking.py at master · NJU-LHRS/FlipCAM-master · GitHub

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
import torch
from torch import multiprocessing, cuda
from torch.utils.data import DataLoader
import torch.nn.functional as F
from torch.backends import cudnn
import torch.nn as nn
import numpy as np
import importlib
import argparse
import os
from numpy.linalg import lstsq
from scipy.linalg import orth
import voc12.dataloader
from misc import torchutils, imutils
import cv2
from gradCAM import GradCAM

cudnn.enabled = True

parser = argparse.ArgumentParser()

# Environment
parser.add_argument("--num_workers", default=os.cpu_count()//2, type=int)
parser.add_argument("--voc12_root", default=r'D:\Experiments\AIRS\data\train/', type=str,
                    help="Path to VOC 2012 Devkit, must contain ./JPEGImages as subdirectory.")
# Dataset
parser.add_argument("--train_list", default=r"D:\Experiments\AIRS\data\train/train.txt", type=str)
parser.add_argument("--val_list", default=r"D:\Experiments\AIRS\data\train/train.txt", type=str)
parser.add_argument("--infer_list", default=r"D:\Experiments\AIRS\data\train/train.txt", type=str,
                    help="voc12/train_aug.txt to train a fully supervised model, "
                         "voc12/train.txt or voc12/val.txt to quickly check the quality of the labels.")
parser.add_argument("--chainer_eval_set", default="train", type=str)

# Class Activation Map
# parser.add_argument("--cam_network", default="net.resnet50_cam", type=str)
parser.add_argument("--cam_network", default="core.networks", type=str)
parser.add_argument("--cam_crop_size", default=256, type=int)
parser.add_argument("--cam_batch_size", default=16, type=int) # original: 16
parser.add_argument("--cam_num_epoches", default=5, type=int)
parser.add_argument("--cam_learning_rate", default=0.01, type=float)
parser.add_argument("--cam_weight_decay", default=1e-4, type=float)
parser.add_argument("--cam_eval_thres", default=0.15, type=float)
parser.add_argument("--cam_scales", default=(1.0, 0.5, 1.5, 2.0),
                    help="Multi-scale inferences")
# parser.add_argument("--cam_scales", default=(1.0,),
#                     help="Single-scale inferences")

# parser.add_argument("--cam_weights_name", default=r"D:\WSSS_code\PuzzleCAM-master\experiments\building_test2_resnet50\models/building_test2_resnet50_fg.pth", type=str)
parser.add_argument("--target_layer", default="stage5")
parser.add_argument("--adv_iter", default=3, type=int)
parser.add_argument("--cam_out_dir", default="AdvCAM_result/test_Airs_PZCAM_resnet50_RGB_adv_mask_iter=3", type=str)
parser.add_argument("--AD_coeff", default=7, type=int) ##27,200
parser.add_argument("--AD_stepsize", default=0.8, type=float)##0.008,0.08
parser.add_argument("--score_th", default=0.5, type=float)

args = parser.parse_args()
torch.set_num_threads(1)
if not os.path.exists(args.cam_out_dir):
    os.makedirs(args.cam_out_dir)

def adv_climb(image, epsilon, data_grad):
    sign_data_grad = data_grad / (torch.max(torch.abs(data_grad))+1e-12)
    perturbed_image = image + epsilon*sign_data_grad
    perturbed_image = torch.clamp(perturbed_image, image.min().data.cpu().float(), image.max().data.cpu().float()) # min, max from data normalization
    return perturbed_image

def add_discriminative(expanded_mask, regions, score_th):
    region_ = regions / regions.max()
    expanded_mask[region_>score_th]=1
    return expanded_mask

def _work(process_id, model, dataset, args):
    databin = dataset[process_id]
    n_gpus = torch.cuda.device_count()
    data_loader = DataLoader(databin, shuffle=False, num_workers=args.num_workers // n_gpus, pin_memory=True)
    cam_sizes = [[], [], [], []] # scale 0,1,2,3
    with cuda.device(process_id):
        model.cuda()
        gcam = GradCAM(model=model, candidate_layers=[args.target_layer])
        for iter, pack in enumerate(data_loader):
            img_name = pack['name'][0]
            if os.path.exists(os.path.join(args.cam_out_dir, img_name + '.npy')):
                continue
            size = pack['size']
            strided_size = imutils.get_strided_size(size, 4)
            strided_up_size = imutils.get_strided_up_size(size, 16)
            outputs_cam = []
            n_classes = len(list(torch.nonzero(pack['label'][0])[:, 0]))
            for s_count, size_idx in enumerate([1, 0, 2, 3]):
            # for s_count, size_idx in enumerate([0]):
                orig_img = pack['img'][size_idx].clone()
                for c_idx, c in enumerate(list(torch.nonzero(pack['label'][0])[:, 0])):
                    pack['img'][size_idx] = orig_img
                    img_single = pack['img'][size_idx].detach()[0]  # [:, 1]: flip

                    if size_idx != 1:
                        total_adv_iter = args.adv_iter
                    else:
                        if args.adv_iter > 10:
                            total_adv_iter = args.adv_iter // 2
                            mul_for_scale = 2
                        elif args.adv_iter < 6:
                            total_adv_iter = args.adv_iter
                            mul_for_scale = 1
                        else:
                            total_adv_iter = 5
                            mul_for_scale = float(total_adv_iter) / 5

                    for it in range(total_adv_iter):
                        img_single.requires_grad = True

                        outputs = gcam.forward(img_single.cuda(non_blocking=True))##

                        if c_idx == 0 and it == 0:
                            cam_all_classes = torch.zeros([n_classes, outputs.shape[2], outputs.shape[3]])

                        gcam.backward(ids=c)##/
                        #
                        # regions = gcam.generate(target_layer=args.target_layer)
                        # regions = regions[0] + regions[1].flip(-1)

                        _,regions = model(img_single.cuda(),with_cam = True)
                        regions = F.relu(regions)
                        regions = regions[0] + regions[1].flip(-1)
                        regions = regions[-1].reshape(1,outputs.shape[2],outputs.shape[3]) ##CAM

                        if it == 0:
                            init_cam = regions.detach()

                        cam_all_classes[c_idx] += regions[0].data.cpu() * mul_for_scale


                        logit = outputs
                        logit = F.relu(logit)
                        logit = torchutils.gap2d(logit, keepdims=True)[:, :, 0, 0]

                        valid_cat = torch.nonzero(pack['label'][0])[:, 0]
                        logit_loss = - 2 * (logit[:, c]).sum() + torch.sum(logit)

                        expanded_mask = torch.zeros(regions.shape)
                        expanded_mask = add_discriminative(expanded_mask, regions, score_th=args.score_th)

                        L_AD = torch.sum((torch.abs(regions - init_cam))*expanded_mask.cuda())

                        loss = - logit_loss - L_AD * args.AD_coeff

                        model.zero_grad()
                        img_single.grad.zero_()
                        loss.backward()

                        data_grad = img_single.grad.data

                        perturbed_data = adv_climb(img_single, args.AD_stepsize, data_grad)
                        img_single = perturbed_data.detach()

                outputs_cam.append(cam_all_classes)

            strided_cam = torch.sum(torch.stack(
                [F.interpolate(torch.unsqueeze(o, 0), strided_size, mode='bilinear', align_corners=False)[0] for o
                 in outputs_cam]), 0)
            highres_cam = [F.interpolate(torch.unsqueeze(o, 1), strided_up_size,
                                         mode='bilinear', align_corners=False) for o in outputs_cam]

            highres_cam = torch.sum(torch.stack(highres_cam, 0), 0)[:, 0, :size[0], :size[1]]
            strided_cam /= F.adaptive_max_pool2d(strided_cam, (1, 1)) + 1e-5
            highres_cam /= F.adaptive_max_pool2d(highres_cam, (1, 1)) + 1e-5

            np.save(os.path.join(args.cam_out_dir, img_name + '.npy'),
                    {"keys": valid_cat, "cam": strided_cam.cpu(), "high_res": highres_cam.cpu().numpy()})


if __name__ == '__main__':
    model = getattr(importlib.import_module(args.cam_network), 'Classifier')()
    model_dict = model.state_dict()
    state_dict = torch.load(r'D:\WSSS_code\PuzzleCAM-master\experiments\building_airs_PZCAM_resnet50_RGB\models\building_airs_PZCAM_resnet50_RGB_fg.pth')


    # pretrained_dict = {k: v for k, v in state_dict.items() if k in model_dict}

    # model_dict.update(pretrained_dict)  # 利用预训练模型的参数，更新模型
    model.load_state_dict(state_dict,strict = True)

    model.eval()

    n_gpus = torch.cuda.device_count()
    print(n_gpus)
    dataset = voc12.dataloader.VOC12ClassificationDatasetMSF(args.train_list,
                                                             voc12_root=args.voc12_root, scales=args.cam_scales)
    dataset = torchutils.split_dataset(dataset, n_gpus)
    _work(0, model, dataset, args)
    # multiprocessing.spawn(_work, nprocs=n_gpus, args=(model, dataset, args), join=True)