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tfgcvit

Keras (TensorFlow v2) reimplementation of Global Context Vision Transformer models.

• Based on Official Pytorch implementation.
• Supports variable-shape inference for downstream tasks.
• Contains pretrained weights converted from official ones.

Installation

pip install tfgcvit

Examples

Default usage (without preprocessing):

from tfgcvit import GCViTTiny  # + 4 other variants and input preprocessing

model = GCViTTiny()  # by default will download imagenet-pretrained weights
model.compile(...)
model.fit(...)

Custom classification (with preprocessing):

from keras import layers, models
from tfgcvit import GCViTTiny, preprocess_input

inputs = layers.Input(shape=(224, 224, 3), dtype='uint8')
outputs = layers.Lambda(preprocess_input)(inputs)
outputs = GCViTTiny(include_top=False)(outputs)
outputs = layers.Dense(100, activation='softmax')(outputs)

model = models.Model(inputs=inputs, outputs=outputs)
model.compile(...)
model.fit(...)

Differences

Code simplification:

• All input shapes automatically evaluated (not passed through a constructor like in PyTorch)
• Downsampling have been moved out from GCViTLayer layer to simplify feature extraction in downstream tasks.

Performance improvements:

• Layer normalization epsilon fixed at 1.001e-5 and inputs are casted to float32 to use fused op implementation.
• Some layers have been refactored to use faster TF operations.
• A lot of reshapes/transposes have been removed. Most of the time internal representation is 4D-tensor.
• Relative index estimations moved to GCViTLayer layer level.

Variable shapes

When using GCViT models with input shapes different from pretraining one, try to make height and width to be multiple of 32 * window_size. Otherwise, a lot of tensors will be padded, resulting in speed degradation.

Evaluation

For correctness, Tiny and Small models (original and ported) tested with ImageNet-v2 test set.

import tensorflow as tf
import tensorflow_datasets as tfds
from tfgcvit import GCViTTiny, preprocess_input


def _prepare(example, input_size=224, crop_pct=0.875):
    scale_size = tf.math.floor(input_size / crop_pct)

    image = example['image']

    shape = tf.shape(image)[:2]
    shape = tf.cast(shape, 'float32')
    shape *= scale_size / tf.reduce_min(shape)
    shape = tf.round(shape)
    shape = tf.cast(shape, 'int32')

    image = tf.image.resize(image, shape, method=tf.image.ResizeMethod.BICUBIC)
    image = tf.round(image)
    image = tf.clip_by_value(image, 0., 255.)
    image = tf.cast(image, 'uint8')

    pad_h, pad_w = tf.unstack((shape - input_size) // 2)
    image = image[pad_h:pad_h + input_size, pad_w:pad_w + input_size]

    image = preprocess_input(image)

    return image, example['label']


imagenet2 = tfds.load('imagenet_v2', split='test', shuffle_files=True)
imagenet2 = imagenet2.map(_prepare, num_parallel_calls=tf.data.AUTOTUNE)
imagenet2 = imagenet2.batch(8).prefetch(tf.data.AUTOTUNE)

model = GCViTTiny()
model.compile('sgd', 'sparse_categorical_crossentropy', ['accuracy', 'sparse_top_k_categorical_accuracy'])
history = model.evaluate(imagenet2)

print(history)

The most metric differences comes from input data preprocessing (decoding, interpolation). All layers outputs have been compared with original ones. Maximum absolute difference among all layers is 8e-4. Most of them have maximum absolute difference less then 1e-5.

Citation

@article{hatamizadeh2022global,
  title={Global Context Vision Transformers},
  author={Hatamizadeh, Ali and Yin, Hongxu and Kautz, Jan and Molchanov, Pavlo},
  journal={arXiv preprint arXiv:2206.09959},
  year={2022}
}