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embed_res20s_cifar10.py
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embed_res20s_cifar10.py
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import torch
import numpy as np
from models.resnets import resnet20
a = torch.load("ownership/res20s_cifar10_extreme.pth.tar", map_location="cpu")
a.keys()
def check_sparsity(mask, conv1=True):
sum_list = 0
zero_sum = 0
for name in mask:
if 'mask' in name:
mask_ = mask[name]
sum_list = sum_list+float(mask_.nelement())
zero_sum = zero_sum+float(torch.sum(mask_ == 0))
print(1 - zero_sum / sum_list)
np.random.seed(2)
def extract_mask(model_dict):
new_dict = {}
for key in model_dict.keys():
if 'mask' in key:
new_dict[key] = model_dict[key]
return new_dict
mask = extract_mask(a['state_dict'])
check_sparsity(mask)
import qrcode
qr = qrcode.QRCode(
version=3,
error_correction=qrcode.constants.ERROR_CORRECT_H,
box_size=1,
border=0,
)
qr.add_data('signature')
qr.make()
img = qr.make_image(fill_color="black", back_color="white")
code = np.array(img)
import matplotlib.pyplot as plt
plt.imshow(img, cmap='gray', vmin=0, vmax=1)
plt.axis("off")
plt.savefig("qrcode.svg", bbox_inches='tight',pad_inches = 0)
# assert False
from scipy.signal import correlate2d
h,w = code.shape[0],code.shape[1]
max_sim = 0
for name in mask:
if not 'layer2' in name:
continue
mask_ = mask[name].sum((2,3)).numpy() > 0
mask_ = mask_.astype(float)
if (mask_.shape[0] - code.shape[0] < 0) or (mask_.shape[1] - code.shape[1] < 0):
continue
sim = np.zeros((mask_.shape[0] - code.shape[0] + 1, mask_.shape[1] - code.shape[1] + 1))
for i in range(sim.shape[0]):
for j in range(sim.shape[1]):
sim[i,j] = (mask_[i:i+h,j:j+w] == code).mean()
if np.max(sim) > max_sim:
max_name = name
max_sim = np.max(sim)
# print(max_name)
# print(max_sim)
#max_name = 'layer2.0.conv2.weight_mask' # override
import sys
if len(sys.argv) > 1:
max_name = sys.argv[1]
# print(mask.keys())
print("Position:", '.'.join(max_name.split(".")[:-1]))
mask_ = mask[max_name].sum((2,3)).numpy() > 0
mask_ = mask_.astype(float)
sim = np.zeros((mask_.shape[0] - code.shape[0] + 1, mask_.shape[1] - code.shape[1] + 1))
for i in range(sim.shape[0]):
for j in range(sim.shape[1]):
sim[i,j] = (mask_[i:i+h,j:j+w] == code).mean()
r, c = np.where(sim == np.max(sim))
#r=2
#c=3
r = r[0]
c = c[0]
print(f"start x: {r}")
print(f"start y: {c}")
real_mask = mask[max_name].numpy()[r:r+h, c:c+w].copy()
real_mask_one = (real_mask == 1).sum()
real_mask_flat = ((real_mask).sum((2,3)) > 0).astype(float)
# print(real_mask_flat.shape)
for i in range(code.shape[0]):
for j in range(code.shape[1]):
if code[i,j] == 1 and real_mask_flat[i,j] == 0:
_ = np.array([0] * 9)
_[0] = 1
new_mask = np.random.permutation(_)
real_mask[i,j] = new_mask.reshape((3, 3))
real_mask_flat[i,j] == 1
elif code[i,j] == 0 and real_mask_flat[i,j] == 1:
real_mask[i,j] = 0
real_mask_flat[i,j] == 0
original_mask = mask[max_name][r:r+h, c:c+w].clone().numpy()
real_mask[0:9, 0:9] = original_mask[0:9, 0:9]
real_mask[-9:,:9] = original_mask[-9:,:9]
real_mask[:9,-9:] = original_mask[:9,-9:]
real_mask[20:25, 20:25] = original_mask[20:25, 20:25]
real_mask[-8, 4 * 3 + 9] = 1
real_mask[6] = original_mask[6]
real_mask[:, 6] = original_mask[:, 6]
real_mask_one_new = (real_mask == 1).sum()
real_mask_flat_new = (real_mask).sum((2,3))
diff = real_mask_one_new - real_mask_one
# print(diff)
if (diff > 0):
# remove some connections
real_mask_flat_greater_0 = np.where(real_mask_flat_new > 1)
else:
# recover some connections
pos = np.expand_dims((code == 1), (2, 3)) * np.expand_dims(real_mask_flat == 1, (2,3)) * (real_mask == 0)
pos = np.where(pos)
pos = np.stack(pos)
# print(pos.shape)
pos = pos[:, np.random.permutation(pos.shape[1])[:(-diff)]]
# print(pos.shape)
for i in range(pos.shape[1]):
p = pos[:, i]
real_mask[p[0], p[1], p[2], p[3]] = 1
import matplotlib.pyplot as plt
mask[max_name][r:r+h, c:c+w] = torch.from_numpy(real_mask)
vis = mask[max_name].sum((2,3)).numpy() > 0
plt.imshow(vis)
plt.savefig(f"ownership/res20s_cifar10_vis_{max_name}.pdf")
plt.close()
torch.save(mask, f'ownership/res20s_cifar10_qrcode_{max_name}.pth.tar')
check_sparsity(mask)
'''
vis = mask[max_name].sum((2,3)).numpy() > 0
plt.imshow(vis)
plt.savefig("vis2.png")
plt.close()
torch.save(mask, 'ownership/res20s_cifar10_extreme.pth.tar')
'''