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labml_nn/__init__.py
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@ -31,6 +31,7 @@ implementations.
* [Masked Language Model](transformers/mlm/index.html)
* [MLP-Mixer: An all-MLP Architecture for Vision](transformers/mlp_mixer/index.html)
* [Pay Attention to MLPs (gMLP)](transformers/gmlp/index.html)
* [Vision Transformer (ViT)](transformers/vit/index.html)
#### ✨ [Recurrent Highway Networks](recurrent_highway_networks/index.html)

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labml_nn/transformers/__init__.py
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@ -82,6 +82,11 @@ This is an implementation of the paper
This is an implementation of the paper
[Pay Attention to MLPs](https://papers.labml.ai/paper/2105.08050).
## [Vision Transformer (ViT)](vit/index.html)
This is an implementation of the paper
[An Image Is Worth 16x16 Words: Transformers For Image Recognition At Scale](https://arxiv.org/abs/2010.11929).
"""
from .configs import TransformerConfigs

144
labml_nn/transformers/vit/__init__.py
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@ -1,3 +1,47 @@
"""
---
title: Vision Transformer (ViT)
summary: >
A PyTorch implementation/tutorial of the paper
"An Image Is Worth 16x16 Words: Transformers For Image Recognition At Scale"
---
# Vision Transformer (ViT)
This is a [PyTorch](https://pytorch.org) implementation of the paper
[An Image Is Worth 16x16 Words: Transformers For Image Recognition At Scale](https://arxiv.org/abs/2010.11929).
Vision transformer applies a pure transformer to images
without any convolution layers.
They split the image into patches and apply a transformer on patch embeddings.
[Patch embeddings](#PathEmbeddings) are generated by applying a simple linear transformation
to the flattened pixel values of the patch.
Then a standard transformer encoder is fed with the patch embeddings, along with a
classification token `[CLS]`.
The encoding on the `[CLS]` token is used to classify the image with an MLP.
When feeding the transformer with the patches, learned positional embeddings are
added to the patch embeddings, because the patch embeddings do not have any information
about where that patch is from.
The positional embeddings are a set of vectors for each patch location that get trained
with gradient descent along with other parameters.
ViTs perform well when they are pre-trained on large datasets.
The paper suggests pre-training them with an MLP classification head and
then using a single linear layer when fine-tuning.
The paper beats SOTA with a ViT pre-trained on a 300 million image dataset.
They also use higher resolution images during inference while keeping the
patch size the same.
The positional embeddings for new patch locations are calculated by interpolating
learning positional embeddings.
Here's [an experiment](experiment.html) that trains ViT on CIFAR-10.
This doesn't do very well because it's trained on a small dataset.
It's a simple experiment that anyone can run and play with ViTs.
[![View Run](https://img.shields.io/badge/labml-experiment-brightgreen)](https://app.labml.ai/run/8b531d9ce3dc11eb84fc87df6756eb8f)
"""
import torch
from torch import nn
@ -9,24 +53,41 @@ from labml_nn.utils import clone_module_list
class PatchEmbeddings(Module):
"""
<a id="PatchEmbeddings">
## Embed patches
## Get patch embeddings
</a>
The paper splits the image into patches of equal size and do a linear transformation
on the flattened pixels for each patch.
We implement the same thing through a convolution layer, because it's simpler to implement.
"""
def __init__(self, d_model: int, patch_size: int, in_channels: int):
"""
* `d_model` is the transformer embeddings size
* `patch_size` is the size of the patch
* `in_channels` is the number of channels in the input image (3 for rgb)
"""
super().__init__()
self.patch_size = patch_size
# We create a convolution layer with a kernel size and and stride length equal to patch size.
# This is equivalent to splitting the image into patches and doing a linear
# transformation on each patch.
self.conv = nn.Conv2d(in_channels, d_model, patch_size, stride=patch_size)
def __call__(self, x: torch.Tensor):
"""
x has shape `[batch_size, channels, height, width]`
* `x` is the input image of shape `[batch_size, channels, height, width]`
"""
# Apply convolution layer
x = self.conv(x)
# Get the shape.
bs, c, h, w = x.shape
# Rearrange to shape `[patches, batch_size, d_model]`
x = x.permute(2, 3, 0, 1)
x = x.view(h * w, bs, c)
# Return the patch embeddings
return x
@ -35,56 +96,121 @@ class LearnedPositionalEmbeddings(Module):
<a id="LearnedPositionalEmbeddings">
## Add parameterized positional encodings
</a>
This adds learned positional embeddings to patch embeddings.
"""
def __init__(self, d_model: int, max_len: int = 5_000):
"""
* `d_model` is the transformer embeddings size
* `max_len` is the maximum number of patches
"""
super().__init__()
# Positional embeddings for each location
self.positional_encodings = nn.Parameter(torch.zeros(max_len, 1, d_model), requires_grad=True)
def __call__(self, x: torch.Tensor):
"""
* `x` is the patch embeddings of shape `[patches, batch_size, d_model]`
"""
# Get the positional embeddings for the given patches
pe = self.positional_encodings[x.shape[0]]
# Add to patch embeddings and return
return x + pe
class ClassificationHead(Module):
"""
<a id="ClassificationHead">
## MLP Classification Head
</a>
This is the two layer MLP head to classify the image based on `[CLS]` token embedding.
"""
def __init__(self, d_model: int, n_hidden: int, n_classes: int):
"""
* `d_model` is the transformer embedding size
* `n_hidden` is the size of the hidden layer
* `n_classes` is the number of classes in the classification task
"""
super().__init__()
self.ln = nn.LayerNorm([d_model])
# First layer
self.linear1 = nn.Linear(d_model, n_hidden)
# Activation
self.act = nn.ReLU()
# Second layer
self.linear2 = nn.Linear(n_hidden, n_classes)
def __call__(self, x: torch.Tensor):
x = self.ln(x)
"""
* `x` is the transformer encoding for `[CLS]` token
"""
# First layer and activation
x = self.act(self.linear1(x))
# Second layer
x = self.linear2(x)
#
return x
class VisionTransformer(Module):
"""
## Vision Transformer
This combines the [patch embeddings](#PatchEmbeddings),
[positional embeddings](#LearnedPositionalEmbeddings),
transformer and the [classification head](#ClassificationHead).
"""
def __init__(self, transformer_layer: TransformerLayer, n_layers: int,
patch_emb: PatchEmbeddings, pos_emb: LearnedPositionalEmbeddings,
classification: ClassificationHead):
"""
* `transformer_layer` is a copy of a single [transformer layer](../models.html#TransformerLayer).
We make copies of it to make the transformer with `n_layers`.
* `n_layers` is the number of [transformer layers]((../models.html#TransformerLayer).
* `patch_emb` is the [patch embeddings layer](#PatchEmbeddings).
* `pos_emb` is the [positional embeddings layer](#LearnedPositionalEmbeddings).
* `classification` is the [classification head](#ClassificationHead).
"""
super().__init__()
# Make copies of the transformer layer
self.classification = classification
self.pos_emb = pos_emb
# Patch embeddings
self.patch_emb = patch_emb
self.pos_emb = pos_emb
# Classification head
self.classification = classification
# Make copies of the transformer layer
self.transformer_layers = clone_module_list(transformer_layer, n_layers)
# `[CLS]` token embedding
self.cls_token_emb = nn.Parameter(torch.randn(1, 1, transformer_layer.size), requires_grad=True)
# Final normalization layer
self.ln = nn.LayerNorm([transformer_layer.size])
def __call__(self, x):
def __call__(self, x: torch.Tensor):
"""
* `x` is the input image of shape `[batch_size, channels, height, width]`
"""
# Get patch embeddings. This gives a tensor of shape `[patches, batch_size, d_model]`
x = self.patch_emb(x)
# Add positional embeddings
x = self.pos_emb(x)
# Concatenate the `[CLS]` token embeddings before feeding the transformer
cls_token_emb = self.cls_token_emb.expand(-1, x.shape[1], -1)
x = torch.cat([cls_token_emb, x])
# Pass through transformer layers with no attention masking
for layer in self.transformer_layers:
x = layer(x=x, mask=None)
# Get the transformer output of the `[CLS]` token (which is the first in the sequence).
x = x[0]
# Layer normalization
x = self.ln(x)
# Classification head, to get logits
x = self.classification(x)
#
return x

36
labml_nn/transformers/vit/experiment.py
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@ -1,11 +1,13 @@
"""
---
title: Train a ViT on CIFAR 10
title: Train a Vision Transformer (ViT) on CIFAR 10
summary: >
Train a ViT on CIFAR 10
Train a Vision Transformer (ViT) on CIFAR 10
---
# Train a ViT on CIFAR 10
# Train a [Vision Transformer (ViT)](index.html) on CIFAR 10
[![View Run](https://img.shields.io/badge/labml-experiment-brightgreen)](https://app.labml.ai/run/8b531d9ce3dc11eb84fc87df6756eb8f)
"""
from labml import experiment
@ -18,19 +20,27 @@ class Configs(CIFAR10Configs):
"""
## Configurations
We use [`CIFAR10Configs`](../experiments/cifar10.html) which defines all the
We use [`CIFAR10Configs`](../../experiments/cifar10.html) which defines all the
dataset related configurations, optimizer, and a training loop.
"""
# [Transformer configurations](../configs.html#TransformerConfigs)
# to get [transformer layer](../models.html#TransformerLayer)
transformer: TransformerConfigs
# Size of a patch
patch_size: int = 4
n_hidden: int = 2048
# Size of the hidden layer in classification head
n_hidden_classification: int = 2048
# Number of classes in the task
n_classes: int = 10
@option(Configs.transformer)
def _transformer(c: Configs):
def _transformer():
"""
Create transformer configs
"""
return TransformerConfigs()
@ -42,11 +52,13 @@ def _vit(c: Configs):
from labml_nn.transformers.vit import VisionTransformer, LearnedPositionalEmbeddings, ClassificationHead, \
PatchEmbeddings
# Transformer size from [Transformer configurations](../configs.html#TransformerConfigs)
d_model = c.transformer.d_model
# Create a vision transformer
return VisionTransformer(c.transformer.encoder_layer, c.transformer.n_layers,
PatchEmbeddings(d_model, c.patch_size, 3),
LearnedPositionalEmbeddings(d_model),
ClassificationHead(d_model, c.n_hidden, c.n_classes)).to(c.device)
ClassificationHead(d_model, c.n_hidden_classification, c.n_classes)).to(c.device)
def main():
@ -56,20 +68,20 @@ def main():
conf = Configs()
# Load configurations
experiment.configs(conf, {
'device.cuda_device': 0,
# 'optimizer.optimizer': 'Noam',
# 'optimizer.learning_rate': 1.,
# Optimizer
'optimizer.optimizer': 'Adam',
'optimizer.learning_rate': 2.5e-4,
'optimizer.d_model': 512,
# Transformer embedding size
'transformer.d_model': 512,
# Training epochs and batch size
'epochs': 1000,
'train_batch_size': 64,
# Augment CIFAR 10 images for training
'train_dataset': 'cifar10_train_augmented',
# Do not augment CIFAR 10 images for validation
'valid_dataset': 'cifar10_valid_no_augment',
})
# Set model for saving/loading

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labml_nn/transformers/vit/readme.md Normal file
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@ -0,0 +1,32 @@
# [Vision Transformer (ViT)](https://nn.labml.ai/transformer/vit/index.html)
This is a [PyTorch](https://pytorch.org) implementation of the paper
[An Image Is Worth 16x16 Words: Transformers For Image Recognition At Scale](https://arxiv.org/abs/2010.11929).
Vision transformer applies a pure transformer to images
without any convolution layers.
They split the image into patches and apply a transformer on patch embeddings.
[Patch embeddings](https://nn.labml.ai/transformer/vit/index.html#PathEmbeddings) are generated by applying a simple linear transformation
to the flattened pixel values of the patch.
Then a standard transformer encoder is fed with the patch embeddings, along with a
classification token `[CLS]`.
The encoding on the `[CLS]` token is used to classify the image with an MLP.
When feeding the transformer with the patches, learned positional embeddings are
added to the patch embeddings, because the patch embeddings do not have any information
about where that patch is from.
The positional embeddings are a set of vectors for each patch location that get trained
with gradient descent along with other parameters.
ViTs perform well when they are pre-trained on large datasets.
The paper suggests pre-training them with an MLP classification head and
then using a single linear layer when fine-tuning.
The paper beats SOTA with a ViT pre-trained on a 300 million image dataset.
They also use higher resolution images during inference while keeping the
patch size the same.
The positional embeddings for new patch locations are calculated by interpolating
learning positional embeddings.
Here's [an experiment](https://nn.labml.ai/transformer/vit/experiment.html) that trains ViT on CIFAR-10.
This doesn't do very well because it's trained on a small dataset.
It's a simple experiment that anyone can run and play with ViTs.