663 KiB
663 KiB
!pip install torch_snippets
from torch_snippets import inspect, show, np, torch, nn
from torchvision.models import resnet50
model = resnet50(pretrained=True)
for param in model.parameters():
param.requires_grad = False
model = model.eval()
import requests
from PIL import Image
url = 'https://lionsvalley.co.za/wp-content/uploads/2015/11/african-elephant-square.jpg'
original_image = Image.open(requests.get(url, stream=True).raw).convert('RGB')
original_image = np.array(original_image)
original_image = torch.Tensor(original_image)Collecting torch_snippets Downloading https://files.pythonhosted.org/packages/50/13/302867fc4189c33290179a92e745cbfe6132c3120f5cbad245026a7eccf9/torch_snippets-0.234-py3-none-any.whl Requirement already satisfied: tqdm in /usr/local/lib/python3.6/dist-packages (from torch_snippets) (4.41.1) Requirement already satisfied: matplotlib in /usr/local/lib/python3.6/dist-packages (from torch_snippets) (3.2.2) Requirement already satisfied: pandas in /usr/local/lib/python3.6/dist-packages (from torch_snippets) (1.1.3) Collecting opencv-python-headless [?25l Downloading https://files.pythonhosted.org/packages/08/e9/57d869561389884136be65a2d1bc038fe50171e2ba348fda269a4aab8032/opencv_python_headless-4.4.0.46-cp36-cp36m-manylinux2014_x86_64.whl (36.7MB) [K |████████████████████████████████| 36.7MB 81kB/s [?25hCollecting loguru [?25l Downloading https://files.pythonhosted.org/packages/6d/48/0a7d5847e3de329f1d0134baf707b689700b53bd3066a5a8cfd94b3c9fc8/loguru-0.5.3-py3-none-any.whl (57kB) [K |████████████████████████████████| 61kB 8.2MB/s [?25hRequirement already satisfied: dill in /usr/local/lib/python3.6/dist-packages (from torch_snippets) (0.3.2) Requirement already satisfied: Pillow in /usr/local/lib/python3.6/dist-packages (from torch_snippets) (7.0.0) Requirement already satisfied: numpy in /usr/local/lib/python3.6/dist-packages (from torch_snippets) (1.18.5) Requirement already satisfied: python-dateutil>=2.1 in /usr/local/lib/python3.6/dist-packages (from matplotlib->torch_snippets) (2.8.1) Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.6/dist-packages (from matplotlib->torch_snippets) (0.10.0) Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.6/dist-packages (from matplotlib->torch_snippets) (1.2.0) Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /usr/local/lib/python3.6/dist-packages (from matplotlib->torch_snippets) (2.4.7) Requirement already satisfied: pytz>=2017.2 in /usr/local/lib/python3.6/dist-packages (from pandas->torch_snippets) (2018.9) Collecting aiocontextvars>=0.2.0; python_version < "3.7" Downloading https://files.pythonhosted.org/packages/db/c1/7a723e8d988de0a2e623927396e54b6831b68cb80dce468c945b849a9385/aiocontextvars-0.2.2-py2.py3-none-any.whl Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.6/dist-packages (from python-dateutil>=2.1->matplotlib->torch_snippets) (1.15.0) Collecting contextvars==2.4; python_version < "3.7" Downloading https://files.pythonhosted.org/packages/83/96/55b82d9f13763be9d672622e1b8106c85acb83edd7cc2fa5bc67cd9877e9/contextvars-2.4.tar.gz Collecting immutables>=0.9 [?25l Downloading https://files.pythonhosted.org/packages/99/e0/ea6fd4697120327d26773b5a84853f897a68e33d3f9376b00a8ff96e4f63/immutables-0.14-cp36-cp36m-manylinux1_x86_64.whl (98kB) [K |████████████████████████████████| 102kB 12.1MB/s [?25hBuilding wheels for collected packages: contextvars Building wheel for contextvars (setup.py) ... [?25l[?25hdone Created wheel for contextvars: filename=contextvars-2.4-cp36-none-any.whl size=7666 sha256=82b083b29ca7f215851c639ca6bd172408728dbae264ecd941c03fe23f1d0c69 Stored in directory: /root/.cache/pip/wheels/a5/7d/68/1ebae2668bda2228686e3c1cf16f2c2384cea6e9334ad5f6de Successfully built contextvars Installing collected packages: opencv-python-headless, immutables, contextvars, aiocontextvars, loguru, torch-snippets Successfully installed aiocontextvars-0.2.2 contextvars-2.4 immutables-0.14 loguru-0.5.3 opencv-python-headless-4.4.0.46 torch-snippets-0.234
Downloading: "https://download.pytorch.org/models/resnet50-19c8e357.pth" to /root/.cache/torch/hub/checkpoints/resnet50-19c8e357.pth
HBox(children=(FloatProgress(value=0.0, max=102502400.0), HTML(value='')))
image_net_classes = 'https://gist.githubusercontent.com/yrevar/942d3a0ac09ec9e5eb3a/raw/238f720ff059c1f82f368259d1ca4ffa5dd8f9f5/imagenet1000_clsidx_to_labels.txt'
image_net_classes = requests.get(image_net_classes).text
image_net_ids = eval(image_net_classes)
image_net_classes = {i:j for j,i in image_net_ids.items()}from torchvision import transforms as T
from torch.nn import functional as F
normalize = T.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
denormalize = T.Normalize([-0.485/0.229, -0.456/0.224, -0.406/0.225], [1/0.229, 1/0.224, 1/0.225])
def image2tensor(input):
x = normalize(input.clone().permute(2,0,1)/255.)[None]
return x
def tensor2image(input):
x = (denormalize(input[0].clone()).permute(1,2,0)*255.).type(torch.uint8)
return xdef predict_on_image(input):
model.eval()
show(input)
input = image2tensor(input)
pred = model(input)
pred = F.softmax(pred, dim=-1)[0]
prob, clss = torch.max(pred, 0)
clss = image_net_ids[clss.item()]
print(f'PREDICTION: `{clss}` @ {prob.item()}')from tqdm import trange
losses = []
def attack(image, model, target, epsilon=1e-6):
input = image2tensor(image)
input.requires_grad = True
pred = model(input)
loss = nn.CrossEntropyLoss()(pred, target)
loss.backward()
losses.append(loss.mean().item())
output = input - epsilon * input.grad.sign()
output = tensor2image(output)
del input
return output.detach()modified_images = []
desired_targets = ['lemon', 'comic book', 'sax, saxophone']for target in desired_targets:
target = torch.tensor([image_net_classes[target]])
image_to_attack = original_image.clone()
for _ in trange(10):
image_to_attack = attack(image_to_attack, model, target)
modified_images.append(image_to_attack)
100%|██████████| 10/10 [00:07<00:00, 1.35it/s] 100%|██████████| 10/10 [00:07<00:00, 1.38it/s] 100%|██████████| 10/10 [00:07<00:00, 1.40it/s]
for image in [original_image, *modified_images]:
predict_on_image(image)
inspect(image)PREDICTION: `African elephant, Loxodonta africana` @ 0.5234400033950806 ================================================================== Tensor Shape: torch.Size([237, 270, 3]) Min: 17.000 Max: 255.000 Mean: 110.237 dtype: torch.float32 ==================================================================
PREDICTION: `lemon` @ 0.9999923706054688 ================================================================== Tensor Shape: torch.Size([237, 270, 3]) Min: 11.000 Max: 248.000 Mean: 105.229 dtype: torch.uint8 ==================================================================
PREDICTION: `comic book` @ 0.9999936819076538 ================================================================== Tensor Shape: torch.Size([237, 270, 3]) Min: 9.000 Max: 245.000 Mean: 105.236 dtype: torch.uint8 ==================================================================
PREDICTION: `sax, saxophone` @ 0.9999990463256836 ================================================================== Tensor Shape: torch.Size([237, 270, 3]) Min: 11.000 Max: 247.000 Mean: 105.237 dtype: torch.uint8 ==================================================================