def main():
args = get_args()
root_dir = args.root_dir
imgs = list(os.walk(root_dir))[0][2]
save_dir = args.save_dir
num_classes = 100 # CIFAR100
model = ResNet.resnet(arch='resnet50', pretrained=False, num_classes=num_classes,
use_att=args.use_att, att_mode=args.att_mode)
#model = nn.DataParallel(model)
#print(model)
if args.resume:
if os.path.isfile(args.resume):
print(f'=> loading checkpoint {args.resume}')
checkpoint = torch.load(args.resume)
best_acc5 = checkpoint['best_acc5']
model.load_state_dict(checkpoint['state_dict'], strict=False)
print(f"=> loaded checkpoint {args.resume} (epoch {checkpoint['epoch']})")
print(f'=> best accuracy {best_acc5}')
else:
print(f'=> no checkpoint found at {args.resume}')
model_dict = get_model_dict(model, args.type)
normalizer = Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
for img_name in imgs:
img_path = os.path.join(root_dir, img_name)
pil_img = PIL.Image.open(img_path)
torch_img = torch.from_numpy(np.asarray(pil_img))
torch_img = torch_img.permute(2, 0, 1).unsqueeze(0)
torch_img = torch_img.float().div(255)
torch_img = F.interpolate(torch_img, size=(224, 224), mode='bilinear', align_corners=False)
normalized_torch_img = normalizer(torch_img)
gradcam = GradCAM(model_dict, True)
gradcam_pp = GradCAMpp(model_dict, True)
mask, _ = gradcam(normalized_torch_img)
heatmap, result = visualize_cam(mask, torch_img)
mask_pp, _ = gradcam_pp(normalized_torch_img)
heatmap_pp, result_pp = visualize_cam(mask_pp, torch_img)
images = torch.stack([torch_img.squeeze().cpu(), heatmap, heatmap_pp, result, result_pp], 0)
images = make_grid(images, nrow=1)
if args.use_att:
save_dir = os.path.join(args.save_dir, 'att')
else:
save_dir = os.path.join(args.save_dir, 'no_att')
os.makedirs(save_dir, exist_ok=True)
output_name = img_name
output_path = os.path.join(save_dir, output_name)
save_image(images, output_path)
def grad_cam(img, model, layer):
configs = [dict(model_type='resnet', arch=model, layer_name=layer)]
for config in configs:
config['arch'].to(device).eval()
torch_img = transforms.Compose([transforms.ToTensor()])(img).to(device)
normed_torch_img = transforms.Normalize(
[0.485, 0.456, 0.406], [0.229, 0.224, 0.225])(torch_img)[None]
cams = [[cls.from_config(**config) for cls in (GradCAM, GradCAMpp)]
for config in configs]
images = []
for gradcam, gradcam_pp in cams:
mask, _ = gradcam(normed_torch_img)
heatmap, result = visualize_cam(mask, torch_img)
mask_pp, _ = gradcam_pp(normed_torch_img)
heatmap_pp, result_pp = visualize_cam(mask_pp, torch_img)
images.extend(
[torch_img.cpu(), heatmap, heatmap_pp, result, result_pp])
return images
def generate_saliency_map(img, img_name):
start = time.time()
normalizer = Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])
torch_img = torch.from_numpy(np.asarray(img)).permute(
2, 0, 1).unsqueeze(0).float().div(255)
torch_img = F.upsample(torch_img,
size=(512, 512),
mode='bilinear',
align_corners=False)
normed_torch_img = normalizer(torch_img)
resnet = models.resnet101(pretrained=True)
resnet.eval()
cam_dict = dict()
model_dict = dict(type='resnet',
arch=resnet,
layer_name='layer4',
input_size=(512, 512))
gradcam = GradCAM(model_dict, True)
gradcam_pp = GradCAMpp(model_dict, True)
images = []
mask, _ = gradcam(normed_torch_img)
heatmap, result = visualize_cam(mask, torch_img)
mask_pp, _ = gradcam_pp(normed_torch_img)
heatmap_pp, result_pp = visualize_cam(mask_pp, torch_img)
images.append(
torch.stack([
torch_img.squeeze().cpu(), heatmap, heatmap_pp, result, result_pp
], 0))
images = make_grid(torch.cat(images, 0), nrow=1)
# Only going to use result_pp
output_dir = 'outputs'
os.makedirs(output_dir, exist_ok=True)
output_name = img_name
output_path = os.path.join(output_dir, output_name)
save_image(result_pp, output_path)
end = time.time()
duration = round(end - start, 2)
return output_path
def show_map():
target_model = ResNet18()
gradcam = GradCAM.from_config(model_type='resnet', arch=target_model, layer_name='layer4')
img=PIL.Image.open('test.jpg')
img = transforms.Compose([transforms.Resize((32, 32)), transforms.ToTensor()])(img)
img = transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])(img)[None]
mask, logit = gradcam(img)#class_idx=10
heatmap, cam_result = visualize_cam(mask, img)
return heatmap, cam_result
def show_map(img, model):
target_model = model
gradcam = GradCAM.from_config(model_type='resnet',
arch=target_model,
layer_name='layer4')
img = transforms.Normalize([0.485, 0.456, 0.406],
[0.229, 0.224, 0.225])(img)[None]
mask, logit = gradcam(img)
heatmap, cam_result = visualize_cam(mask, img)
return heatmap, cam_result
target_layer = 'conv_fc'
# use_conv_fc = False
# checkpoint = 'checkpoints/fc_checkpoints/epoch_26.pth'
# target_layer = 'layer4'
root_dir = '/home1/share/pascal_voc/VOCdevkit'
ann_file = 'VOC2007/ImageSets/Main/trainval.txt'
img_dir = 'VOC2007'
imgdir = os.path.join(root_dir, img_dir, 'JPEGImages')
dataset = VOCClsDataset(root_dir=root_dir,
ann_file=ann_file,
img_dir=img_dir,
phase='test')
model = resnet50(pretrained=False, num_classes=20, use_conv_fc=use_conv_fc)
model.load_state_dict(
torch.load(checkpoint, map_location=lambda storage, loc: storage))
cam = CAM(model, target_layer, dataset.label2cat)
# cam = GradCAMPlus(model, target_layer, dataset.label2cat)
for i in range(20):
img, label, file_name = dataset[i]
heatmap, cats = cam(img.unsqueeze(0), label, use_gt_label)
visualize_cam(imgdir,
file_name,
heatmap,
save_dir,
cats,
is_split=is_split)
weights = alpha.view(b, k, 1, 1)
saliency_map = (weights*activations).sum(1, keepdim=True)
saliency_map = F.relu(saliency_map)
saliency_map = F.upsample(saliency_map, size=(h, w), mode='bilinear', align_corners=False)
saliency_map_min, saliency_map_max = saliency_map.min(), saliency_map.max()
saliency_map = (saliency_map - saliency_map_min).div(saliency_map_max - saliency_map_min).data
return saliency_map, logit
def __call__(self, input, class_idx=None, retain_graph=False):
return self.forward(input, class_idx, retain_graph)
def show_map(img,model):
<<<<<<< HEAD
target_model = model
=======
target_model = model# ResNet18()
>>>>>>> 901a3b5cac6f7a8579010e301ee09bdcb2dae693
gradcam = GradCAM.from_config(model_type='resnet', arch=target_model, layer_name='layer4')
img = transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])(img)[None] #
mask, logit = gradcam(img)
heatmap, cam_result = visualize_cam(mask, img)
return heatmap, cam_result
resnet.load_state_dict(resnet101_dict) #加载参数
resnet.eval(), resnet.cuda();
###
cam_dict = dict()
resnet_model_dict = dict(type='resnet', arch=resnet, layer_name='layer4', input_size=(224, 224))
resnet_gradcam = GradCAM(resnet_model_dict, True)
resnet_gradcampp = GradCAMpp(resnet_model_dict, True)
cam_dict['resnet'] = [resnet_gradcam, resnet_gradcampp]
images = []
for gradcam, gradcam_pp in cam_dict.values():
mask, _ = gradcam(normed_torch_img)
heatmap, result = visualize_cam(mask.cpu(), torch_img.cpu())
mask_pp, _ = gradcam_pp(normed_torch_img)
heatmap_pp, result_pp = visualize_cam(mask_pp.cpu(), torch_img.cpu())
images.append(torch.stack([torch_img.squeeze().cpu(), heatmap, heatmap_pp, result, result_pp], 0))
# images = make_grid(torch.cat(images, 0), nrow=5)
output_dir = 'outputs'
os.makedirs(output_dir, exist_ok=True)
output_name = img_name