CLIP_on_Tesla_K20Xm/README.md

# CLIP using nVidia Tesla K20Xm

To use the old but cheap Tesla K20Xm with openAI Clip you need to downgrade torch and torchvision to a version that is still supported by this hardware. Sadly, building from source is the case.

## Prerequisite 
- Ubuntu 20.04
- miniconda from https://docs.conda.io/en/latest/miniconda.html
- `sudo apt -y install  gcc-8 g++-8 git build-essential` 


### CUDA
- nVidia Driver NVIDIA-Linux-x86_64-470.82.01.run 
- `sudo apt install nvidia-settings`
- Build `check_cuda.c` with `nvcc -o check_cuda check_cuda.c -lcuda`
- Verify the output of check_cuda: `Compute Capability: 3.5`    

## Installing

### Creating a Conda env

`conda create --name clip_Tesla`

`conda activate clip_Tesla`

### Building PyTorch 

switch to gcc8

`sudo update-alternatives --install /usr/bin/gcc gcc /usr/bin/gcc-8 8`

`sudo update-alternatives --install /usr/bin/g++ g++ /usr/bin/g++-8 8`

Verify
`gcc --version`

`conda install astunparse numpy ninja pyyaml mkl mkl-include setuptools cmake cffi typing_extensions future six requests dataclasses`

`conda install -c pytorch magma-cuda101`

change to a tmp dir 

`git clone --recursive https://github.com/pytorch/pytorch`

`cd pytorch`

downgrade to 1.7.1
`git checkout 57bffc3a8e4fee0cce31e1ff1f662ccf7b16db57`

`git submodule sync`

`git submodule update --init --recursive --jobs 0 `

build pytorch and install it
`clear && TORCH_CUDA_ARCH_LIST="3.5 6.1" python setup.py install`

run the `torch_test.py` to verify that CUDA is available and version: 

`True`
`1.7.0a0+57bffc3`

### Building TorchVision

change to a tmp dir 

`git clone https://github.com/pytorch/vision.git`

`cd vision/`

`git checkout 2f40a483d73018ae6e1488a484c5927f2b309969`

`conda install -c conda-forge ffmpeg`

`python setup.py install`


### Install CLIP

`python setup.py install`

Run the test
`python clip_test.py`

## 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

![CLIP](CLIP.png)

## 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(np.float)) * 100.
print(f"Accuracy = {accuracy:.3f}")
```

Note that the `C` value should be determined via a hyperparameter sweep using a validation split.