Hi, I want to visualize the .pt file as patch img. E.g., I download “TextureDict_ImageNet_0.zip”， and I use the following code to visualize "E:\Code\TextureDict_ImageNet_0\TextureDict_ImageNet_0\attention-style_t-label_1\conv_5_cam-thred_0.8_n-clusters_30\cls-w_0_scale_1\cluster_0\iter_9999.pt"

a = torch.load('iter_9999.pt')
img = a.permute(1,2,0).numpy()
print(img.mean())
print(img.max())
plt.imshow(img)
plt.show()

But I notice that img.mean is equal to -0.5, and img.max() is equal to 2.6. I suppose this is the reason why my visualization result is different from that in your paper, so I would like to consult you, so as to show the same result as that in your paper.

Thanks~

the code I use is this ``configure_PA(
t_name='/home/host/mounted2/imagenet-/TextureDict/TextureDict_ImageNet',
t_labels = np.arange(1000).tolist(),
area_occlu = 0.1,
n_occlu = 1,
rl_batch=500, steps=50,
TPA_n_agents=4,
)
TPA = PA.TPA(dir_title)
criterion = torch.nn.CrossEntropyLoss()
metric_logger = utils.MetricLogger(delimiter=" ")
header = 'Test:'
for images, target in metric_logger.log_every(data_loader, 1000, header):
input_tensor = images.to(device, non_blocking=True)
label_tensor = target.to(device, non_blocking=True)
adv_img, rcd_list = TPA.attack(
model = model,
target = 723,
input_tensor = input_tensor[0,:,:,:],
label_tensor = label_tensor,
)`
I use dataloadr load data from imagenet-1k, the batch_size is 1 , so i use input_tensor[0,;.;.;] to get the picture and use for my model.
But the TPA code general picture in the same place with same patch, could you help me about this?
Thanks for your help

In the released dictionaries of patches (AdvPatchDict_ImageNet.zip), it looks like that the masks are always located in the top-left side of the images. Is it correct?

Hi, I have download TextureDict_ImageNet_0, I want to ask how to visualize .pt files as patch patterns? Thanks~

Hey there, sorry for posting an issue, I didn't find an Email address in your paper.
Quick question: when you refer to a "shape-biased network", which network are you referring to?
(https://github.com/rgeirhos/texture-vs-shape/blob/master/models/load_pretrained_models.py lists three different ResNet's with different degrees of stylized training)

Very interesting work! When will the code be released?