TensorFlow-Image-Models
Introduction
TensorfFlow-Image-Models (tfimm) is a collection of image models with pretrained weights, obtained by porting architectures from timm to TensorFlow. The hope is that the number of available architectures will grow over time. For now, it contains vision transformers (ViT, DeiT and Swin Transformers) and ResNets.
This work would not have been possible wihout Ross Wightman's timm library and the work on PyTorch/TensorFlow interoperability in HuggingFace's transformer repository. I tried to make sure all source material is acknowledged. Please let me know if I have missed something.
Usage
Installation
The package can be installed via pip,
pip install tfimm
To load pretrained weights, timm needs to be installed. This is an optional dependency and can be installed via
pip install tfimm[timm]
Creating models
To load pretrained models use
import tfimm
model = tfimm.create_model("vit_tiny_patch16_224", pretrained="timm")
We can list available models with pretrained weights via
import tfimm
print(tfimm.list_models(pretrained="timm"))
Most models are pretrained on ImageNet or ImageNet-21k. If we want to use them for other tasks we need to change the number of classes in the classifier or remove the classifier altogether. We can do this by setting the nb_classes parameter in create_model. If nb_classes=0, the model will have no classification layer. If nb_classes is set to a value different from the default model config, the classification layer will be randomly initialized, while all other weights will be copied from the pretrained model.
The preprocessing function for each model can be created via
import tensorflow as tf
import tfimm
preprocess = tfimm.create_preprocessing("vit_tiny_patch16_224", dtype="float32")
img = tf.ones((1, 224, 224, 3), dtype="uint8")
img_preprocessed = preprocess(img)
Saving and loading models
All models are subclassed from tf.keras.Model (they are not functional models). They can still be saved and loaded using the SavedModel format.
>>> import tesnorflow as tf
>>> import tfimm
>>> model = tfimm.create_model("vit_tiny_patch16_224")
>>> type(model)
>>> model.save("/tmp/my_model")
>>> loaded_model = tf.keras.models.load_model("/tmp/my_model")
>>> type(loaded_model)
For this to work, the tfimm library needs to be imported before the model is loaded, since during the import process, tfimm is registering custom models with Keras. Otherwise, we obtain the following output
>>> import tensorflow as tf
>>> loaded_model = tf.keras.models.load_model("/tmp/my_model")
>>> type(loaded_model)
ViT'>
Models
The following architectures are currently available:
- DeiT (vision transformer) [github]
- Training data-efficient image transformers & distillation through attention. [arXiv:2012.12877]
- ViT (vision transformer) [github]
- An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale. [arXiv:2010.11929]
- How to train your ViT? Data, Augmentation, and Regularization in Vision Transformers. [arXiv:2106.10270]
- Includes models trained with the SAM optimizer: Sharpness-Aware Minimization for Efficiently Improving Generalization. [arXiv:2010.01412]
- Includes models from: ImageNet-21K Pretraining for the Masses [arXiv:2104.10972] [github]
- Swin Transformer [github]
- Swin Transformer: Hierarchical Vision Transformer using Shifted Windows. [arXiv:2103.14030]
- Tensorflow code adapted from Swin-Transformer-TF
- ConvMixer [github]
- Patches Are All You Need? [ICLR 2022 submission]
- ResNet (work in progress, most available weights are from
timm)- Deep Residual Learning for Image Recognition. [arXiv:1512.03385]
Profiling
To understand how big each of the models is, I have done some profiling to measure
- maximum batch size that fits in GPU memory and
- throughput in images/second for both inference and backpropagation on K80 and V100 GPUs. For V100, measurements were done for both
float32and mixed precision.
The results can be found in the results/profiling_{k80, v100}.csv files.
For backpropagation, we use as loss the mean of model outputs
def backprop():
with tf.GradientTape() as tape:
output = model(x, training=True)
loss = tf.reduce_mean(output)
grads = tape.gradient(loss, model.trainable_variables)
optimizer.apply_gradients(zip(grads, model.trainable_variables))
License
This repository is released under the Apache 2.0 license as found in the LICENSE file.
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