Getting started

Setting up your environment

Working locally.

First, create a new conda environment with Python version 3.8, 3.9, or 3.10 e.g. by using conda:

$ conda create -n thingsvision python=3.9
$ conda activate thingsvision

Then, activate the environment and simply install thingsvision via running the following pip command in your terminal.

$ pip install --upgrade thingsvision
$ pip install git+https://github.com/openai/CLIP.git
$ pip install git+https://github.com/serre-lab/Harmonization.git

Google Colab.

Alternatively, you can use Google Colab to play around with thingsvision by uploading your image data to Google Drive (via directory mounting). You can find the jupyter notebook using PyTorch here and the TensorFlow example here.

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Basic usage

Command Line Interface (CLI)

thingsvision was designed to simplify feature extraction. If you have some folder of images (e.g., ./images) and want to extract features for each of these images without opening a Jupyter Notebook instance or writing a Python script, it’s probably easiest to use our CLI. The interface includes two options,

thingsvision show-model
thingsvision extract-features

Example calls might look as follows:

thingsvision show-model --model-name "alexnet" --source "torchvision"
thingsvision extract_features --image-root "./data" --model-name "alexnet" --module-name "features.10" --batch-size 32 --device "cuda" --source "torchvision" --file-format "npy" --out-path "./features"

See thingsvision show-model -h and thingsvision extract-features -h for a list of all possible arguments. Note that the CLI provides just the basic extraction functionalities but is probably enough for most users that don’t want to dive too deep into various models and modules. If you need more fine-grained control over the extraction itself, we recommend to use the python package directly and write your own Python script.

To do this start by importing all the necessary components and instantiating a thingsvision extractor. Here we’re using AlexNet from the torchvision library as the model to extract features from and also load the model to GPU for faster inference,

import torch
from thingsvision import get_extractor
from thingsvision.utils.storing import save_features
from thingsvision.utils.data import ImageDataset, DataLoader

model_name = 'alexnet'
source = 'torchvision'
device = 'cuda' if torch.cuda.is_available() else 'cpu'

extractor = get_extractor(
  model_name=model_name,
  source=source,
  device=device,
  pretrained=True
)

As a next step, create both dataset and dataloader for your images. We assume that all of your images are in a single root directory which can contain subfolders (e.g., for individual classes). Therefore, we leverage the ImageDataset class.

root='path/to/root/img/directory' # (e.g., './images/)
batch_size = 32

dataset = ImageDataset(
  root=root,
  out_path='path/to/features',
  backend=extractor.get_backend(),
  transforms=extractor.get_transformations()
)

batches = DataLoader(
  dataset=dataset,
  batch_size=batch_size, 
  backend=extractor.get_backend()
)

Now all that is left is to extract the image features and store them to disk! We’re extracting features from the last convolutional layer of AlexNet (features.10).

module_name = 'features.10'

features = extractor.extract_features(
  batches=batches,
  module_name=module_name,
  flatten_acts=True  # flatten 2D feature maps from convolutional layer
)

save_features(features, out_path='path/to/features', file_format='npy')

Showing available modules

If you don’t know which modules exist in your model, you can use the show_model method to print a summary of the model architecture. For example, if you want to see which modules exist in AlexNet (using the extractor from above), you can run the following:

extractor.show_model()

# Output:
AlexNet(
  (features): Sequential(
    (0): Conv2d(3, 64, kernel_size=(11, 11), stride=(4, 4), padding=(2, 2))
    (1): ReLU(inplace=True)
    (2): MaxPool2d(kernel_size=3, stride=2, padding=0, dilation=1, ceil_mode=False)
    (3): Conv2d(64, 192, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2))
    (4): ReLU(inplace=True)
    (5): MaxPool2d(kernel_size=3, stride=2, padding=0, dilation=1, ceil_mode=False)
    (6): Conv2d(192, 384, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (7): ReLU(inplace=True)
    (8): Conv2d(384, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (9): ReLU(inplace=True)
    (10): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (11): ReLU(inplace=True)
    (12): MaxPool2d(kernel_size=3, stride=2, padding=0, dilation=1, ceil_mode=False)
  )
  (avgpool): AdaptiveAvgPool2d(output_size=(6, 6))
  (classifier): Sequential(
    (0): Dropout(p=0.5, inplace=False)
    (1): Linear(in_features=9216, out_features=4096, bias=True)
    (2): ReLU(inplace=True)
    (3): Dropout(p=0.5, inplace=False)
    (4): Linear(in_features=4096, out_features=4096, bias=True)
    (5): ReLU(inplace=True)
    (6): Linear(in_features=4096, out_features=1000, bias=True)
  )
)

The module names you have to use in the extractor depend on the model you’re using. For example, the first convolutional layer in AlexNet is called features.0 and the last convolutional layer is called features.10. The last fully connected layer is called classifier.6.

In comparison, the .show_model() output for ResNet50 looks like this:

extractor.show_model()

# Output:
ResNet(
  (conv1): Conv2d(3, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False)
  (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
  (relu): ReLU(inplace=True)
  (maxpool): MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False)
  (layer1): Sequential(
    (0): Bottleneck(
      (conv1): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (relu): ReLU(inplace=True)
      (downsample): Sequential(
        (0): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      )
    )
    (1): Bottleneck(
      (conv1): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv2): Conv2d(64, 64, kernel_size [...]

so the first convolutional layer is called conv1 and the last convolutional layer is called layer4.2.conv3. The last fully connected layer would be called fc.