diff --git a/notebooks/huggingface_notebook.ipynb b/notebooks/huggingface_notebook.ipynb index 030611a586..c6c6ce6335 100644 --- a/notebooks/huggingface_notebook.ipynb +++ b/notebooks/huggingface_notebook.ipynb @@ -5,9 +5,11 @@ "id": "8093e27a-33f6-4cd9-a47b-ea94c3d0c514", "metadata": {}, "source": [ - "# Huggingface with ART\n", "\n", - "In this notebook we will go over how to use the Huggingface AIP with ART. This can enable us to train robust foundation models which act over images. \n", + "

Huggingface with ART

\n", + "\n", + "\n", + "In this notebook we will go over how to use the Huggingface API with ART. This can enable us to train robust foundation models which act over images. \n", "\n", "Currently this is a developing feature, and so not all ART tools are supported. Further tools and development is planned. As of ART 1.16 we support: \n", "+ Using a Pytorch backend.\n", @@ -15,6 +17,14 @@ "\n", "If you have a use case that is not supported (or find a bug in this new feature!) please raise an issiue on ART.\n", "\n", + "In addition to the core ART dependancies you will need to install Pytorch and the Transformers library:\n", + "\n", + "`pip install transformers`\n", + "\n", + "`pip3 install torch torchvision`\n", + "\n", + "This notebook was tested with the transformers library version 4.30.2, and torch==2.1.0/torchvision==0.16.0\n", + "\n", "Let's look at how we can use ART to secure Huggingface models!\n" ] }, @@ -26,6 +36,7 @@ "outputs": [], "source": [ "# Relevant imports for the notebook\n", + "\n", "import transformers\n", "import torch\n", "from torch.optim import Adam\n", @@ -46,7 +57,7 @@ "outputs": [], "source": [ "# We will use CIFAR data for the notebook.\n", - "def get_cifar_data():\n", + "def get_cifar_data(fetch_subset=False):\n", " \"\"\"\n", " Get CIFAR-10 data.\n", " :return: cifar train/test data.\n", @@ -65,10 +76,23 @@ "\n", " x_train = x_train / 255.0\n", " x_test = x_test / 255.0\n", - "\n", + " \n", + " if fetch_subset: \n", + " return (x_train[0:2500], y_train[0:2500]), (x_test[0:2500], y_test[0:2500])\n", + " \n", " return (x_train, y_train), (x_test, y_test)" ] }, + { + "cell_type": "markdown", + "id": "f7b5ebcb", + "metadata": {}, + "source": [ + "# Regular Training with ART\n", + "\n", + "We will first see how to load a model into ART's HuggingFaceClassifierPyTorch, train it, and attack it with PGD." + ] + }, { "cell_type": "code", "execution_count": 3, @@ -76,12 +100,12 @@ "metadata": {}, "outputs": [], "source": [ - "def train_base_model(architecture='google/vit-base-patch16-224'):\n", + "def train_base_model(architecture='google/vit-base-patch16-224', train_on_subset=False):\n", " \"\"\"\n", " Train a cifar classifier\n", " \"\"\"\n", "\n", - " (x_train, y_train), (x_test, y_test) = get_cifar_data()\n", + " (x_train, y_train), (x_test, y_test) = get_cifar_data(train_on_subset)\n", "\n", " # Here we load a Huggingface model using the transformers library.\n", " model = transformers.AutoModelForImageClassification.from_pretrained(architecture,\n", @@ -110,8 +134,7 @@ " nb_classes=10,\n", " clip_values=(0, 1),\n", " processor=upsampler)\n", - "\n", - " hf_model.fit(x_train, y_train, nb_epochs=2)\n", + " hf_model.fit(x_train, y_train, nb_epochs=2, verbose=True)\n", " return hf_model" ] }, @@ -138,10 +161,63 @@ "- classifier.weight: found shape torch.Size([1000, 768]) in the checkpoint and torch.Size([10, 768]) in the model instantiated\n", "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n" ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "7794a0d05b504c48a8d70d1c0596e3a1", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Epochs: 0%| | 0/2 [00:00" ] @@ -281,7 +357,17 @@ } ], "source": [ - "test_pgd(architecture='google/vit-base-patch16-224', model_to_test='hf_base_model.pt')" + "test_pgd(architecture=model_to_examine, model_to_test='hf_base_model.pt')" + ] + }, + { + "cell_type": "markdown", + "id": "0bc9c9df", + "metadata": {}, + "source": [ + "# Adversarial Training with ART\n", + "\n", + "Now, rather than using regular training, we employ robust PGD training and evaluate the robust model." ] }, { @@ -293,10 +379,10 @@ "source": [ "# We can see that we can attack the Huggingface transformer, so now let's use one of the defences in ART!\n", "\n", - "def adversarial_train():\n", + "def adversarial_train(model_to_examine):\n", " from art.defences.trainer import AdversarialTrainerMadryPGD\n", " (x_train, y_train), (x_test, y_test) = get_cifar_data()\n", - " model = transformers.AutoModelForImageClassification.from_pretrained('google/vit-base-patch16-224',\n", + " model = transformers.AutoModelForImageClassification.from_pretrained(model_to_examine,\n", " ignore_mismatched_sizes=True,\n", " num_labels=10)\n", "\n", @@ -333,7 +419,7 @@ "# Uncomment the below to run the adverarial training, it can take some time depending on available hardware. \n", "# The expected runtime is around 15 hours using a Nvidia V100 GPU. More training could be conducted if better performance is desired.\n", "\n", - "# adversarial_train()" + "# adversarial_train(model_to_examine)" ] }, { @@ -399,7 +485,7 @@ }, { "data": { - "image/png": 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", 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", "text/plain": [ "
" ] @@ -410,7 +496,7 @@ ], "source": [ "# We now test the adversariallty trained model and we can see we have done from 0% robustness to 43%.\n", - "test_pgd(architecture='google/vit-base-patch16-224', model_to_test='hf_adv_model.pt')" + "test_pgd(architecture=model_to_examine, model_to_test='hf_adv_model.pt')" ] }, { @@ -436,12 +522,59 @@ "- classifier.weight: found shape torch.Size([1000, 768]) in the checkpoint and torch.Size([10, 768]) in the model instantiated\n", "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n" ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "d1e1c1da709f43279594cebafaf48077", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Epochs: 0%| | 0/2 [00:00" ] @@ -519,7 +652,27 @@ } ], "source": [ - "test_pgd(architecture='microsoft/swin-tiny-patch4-window7-224', model_to_test='./swin_tiny_base_model.pt')" + "test_pgd(architecture='microsoft/swin-tiny-patch4-window7-224', \n", + " model_to_test='./swin_tiny_base_model.pt')" + ] + }, + { + "cell_type": "markdown", + "id": "7e9a80e5", + "metadata": {}, + "source": [ + "# Training and Defending timm models\n", + "\n", + "PyTorch Image Models (timm) is a poular repository for SOTA implementations of image models and Huggingface \n", + "is hosting many of the models and weights. \n", + "\n", + "We can use timm models here with the same wrapper. \n", + "\n", + "To run this part of the notebook we need to install the timm library\n", + "\n", + "`pip install timm`\n", + "\n", + "This notebook was ran with timm==0.9.8" ] }, { @@ -529,11 +682,12 @@ "metadata": {}, "outputs": [], "source": [ - "# We can also try different architectures, for example one of the most popular models on Huggingface is the resn\n", - "def train_using_timm_model():\n", + "# We can also try different architectures, \n", + "# for example one of the most popular models on Huggingface is the resnet-50\n", + "def train_using_timm_model(train_on_subset, model_type):\n", " import timm\n", " \n", - " model = timm.create_model('resnet50.a1_in1k', pretrained=True)\n", + " model = timm.create_model(model_type, pretrained=True)\n", " model = model.to(device)\n", " upsampler = torch.nn.Upsample(scale_factor=7, mode='nearest')\n", " \n", @@ -546,8 +700,8 @@ " optimizer=optimizer,\n", " clip_values=(0, 1),\n", " processor=upsampler)\n", - " (x_train, y_train), (x_test, y_test) = get_cifar_data()\n", - " hf_model.fit(x_train, y_train, nb_epochs=2)\n", + " (x_train, y_train), (x_test, y_test) = get_cifar_data(train_on_subset)\n", + " hf_model.fit(x_train, y_train, nb_epochs=2, verbose=True)\n", "\n", " return hf_model" ] @@ -565,11 +719,61 @@ "Files already downloaded and verified\n", "Files already downloaded and verified\n" ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "eceb751607a642da8c06b35bb2ea1aba", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Epochs: 0%| | 0/2 [00:00