多模态对比语言图像预训练:CLIP

# CLIP [[Blog]](https://openai.com/blog/clip/) [[Paper]](https://arxiv.org/abs/2103.00020) [[Model Card]](model-card.md) [[Colab]](https://colab.research.google.com/github/openai/clip/blob/master/notebooks/Interacting_with_CLIP.ipynb) CLIP (Contrastive Language-Image Pre-Training) is a neural network trained on a variety of (image, text) pairs. It can be instructed in natural language to predict the most relevant text snippet, given an image, without directly optimizing for the task, similarly to the zero-shot capabilities of GPT-2 and 3. We found CLIP matches the performance of the original ResNet50 on ImageNet “zero-shot” without using any of the original 1.28M labeled examples, overcoming several major challenges in computer vision. ## Approach  ## Usage First, [install PyTorch 1.7.1](https://pytorch.org/get-started/locally/) (or later) and torchvision, as well as small additional dependencies, and then install this repo as a Python package. On a CUDA GPU machine, the following will do the trick: ```bash $ conda install --yes -c pytorch pytorch=1.7.1 torchvision cudatoolkit=11.0 $ pip install ftfy regex tqdm $ pip install git+https://github.com/openai/CLIP.git ``` Replace `cudatoolkit=11.0` above with the appropriate CUDA version on your machine or `cpuonly` when installing on a machine without a GPU. ```python import torch import clip from PIL import Image device = "cuda" if torch.cuda.is_available() else "cpu" model, preprocess = clip.load("ViT-B/32", device=device) image = preprocess(Image.open("CLIP.png")).unsqueeze(0).to(device) text = clip.tokenize(["a diagram", "a dog", "a cat"]).to(device) with torch.no_grad(): image_features = model.encode_image(image) text_features = model.encode_text(text) logits_per_image, logits_per_text = model(image, text) probs = logits_per_image.softmax(dim=-1).cpu().numpy() print("Label probs:", probs) # prints: [[0.9927937 0.00421068 0.00299572]] ``` ## API The CLIP module `clip` provides the following methods: #### `clip.available_models()` Returns the names of the available CLIP models. #### `clip.load(name, device=..., jit=False)` Returns the model and the TorchVision transform needed by the model, specified by the model name returned by `clip.available_models()`. It will download the model as necessary. The `name` argument can also be a path to a local checkpoint. The device to run the model can be optionally specified, and the default is to use the first CUDA device if there is any, otherwise the CPU. When `jit` is `False`, a non-JIT version of the model will be loaded. #### `clip.tokenize(text: Union[str, List[str]], context_length=77)` Returns a LongTensor containing tokenized sequences of given text input(s). This can be used as the input to the model --- The model returned by `clip.load()` supports the following methods: #### `model.encode_image(image: Tensor)` Given a batch of images, returns the image features encoded by the vision portion of the CLIP model. #### `model.encode_text(text: Tensor)` Given a batch of text tokens, returns the text features encoded by the language portion of the CLIP model. #### `model(image: Tensor, text: Tensor)` Given a batch of images and a batch of text tokens, returns two Tensors, containing the logit scores corresponding to each image and text input. The values are cosine similarities between the corresponding image and text features, times 100. ## More Examples ### Zero-Shot Prediction The code below performs zero-shot prediction using CLIP, as shown in Appendix B in the paper. This example takes an image from the [CIFAR-100 dataset](https://www.cs.toronto.edu/~kriz/cifar.html), and predicts the most likely labels among the 100 textual labels from the dataset. ```python import os import clip import torch from torchvision.datasets import CIFAR100 # Load the model device = "cuda" if torch.cuda.is_available() else "cpu" model, preprocess = clip.load('ViT-B/32', device) # Download the dataset cifar100 = CIFAR100(root=os.path.expanduser("~/.cache"), download=True, train=False) # Prepare the inputs image, class_id = cifar100[3637] image_input = preprocess(image).unsqueeze(0).to(device) text_inputs = torch.cat([clip.tokenize(f"a photo of a {c}") for c in cifar100.classes]).to(device) # Calculate features with torch.no_grad(): image_features = model.encode_image(image_input) text_features = model.encode_text(text_inputs) # Pick the top 5 most similar labels for the image image_features /= image_features.norm(dim=-1, keepdim=True) text_features /= text_features.norm(dim=-1, keepdim=True) similarity = (100.0 * image_features @ text_features.T).softmax(dim=-1) values, indices = similarity[0].topk(5) # Print the result print("\nTop predictions:\n") for value, index in zip(values, indices): print(f"{cifar100.classes[index]:>16s}: {100 * value.item():.2f}%") ``` The output will look like the following (the exact numbers may be slightly different depending on the compute device): ``` Top predictions: snake: 65.31% turtle: 12.29% sweet_pepper: 3.83% lizard: 1.88% crocodile: 1.75% ``` Note that this example uses the `encode_image()` and `encode_text()` methods that return the encoded features of given inputs. ### Linear-probe evaluation The example below uses [scikit-learn](https://scikit-learn.org/) to perform logistic regression on image features. ```python import os import clip import torch import numpy as np from sklearn.linear_model import LogisticRegression from torch.utils.data import DataLoader from torchvision.datasets import CIFAR100 from tqdm import tqdm # Load the model device = "cuda" if torch.cuda.is_available() else "cpu" model, preprocess = clip.load('ViT-B/32', device) # Load the dataset root = os.path.expanduser("~/.cache") train = CIFAR100(root, download=True, train=True, transform=preprocess) test = CIFAR100(root, download=True, train=False, transform=preprocess) def get_features(dataset): all_features = [] all_labels = [] with torch.no_grad(): for images, labels in tqdm(DataLoader(dataset, batch_size=100)): features = model.encode_image(images.to(device)) all_features.append(features) all_labels.append(labels) return torch.cat(all_features).cpu().numpy(), torch.cat(all_labels).cpu().numpy() # Calculate the image features train_features, train_labels = get_features(train) test_features, test_labels = get_features(test) # Perform logistic regression classifier = LogisticRegression(random_state=0, C=0.316, max_iter=1000, verbose=1) classifier.fit(train_features, train_labels) # Evaluate using the logistic regression classifier predictions = classifier.predict(test_features) accuracy = np.mean((test_labels == predictions).astype(float)) * 100. print(f"Accuracy = {accuracy:.3f}") ``` Note that the `C` value should be determined via a hyperparameter sweep using a validation split. ## See Also * [OpenCLIP](https://github.com/mlfoundations/open_clip): includes larger and independently trained CLIP models up to ViT-G/14 * [Hugging Face implementation of CLIP](https://huggingface.co/docs/transformers/model_doc/clip): for easier integration with the HF ecosystem