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<img src="./images/vit.gif" width="500px"></img>
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<img src="./vit.png" width="500px"></img>
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## Vision Transformer - Pytorch
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Implementation of <a href="https://openreview.net/pdf?id=YicbFdNTTy">Vision Transformer</a>, a simple way to achieve SOTA in vision classification with only a single transformer encoder, in Pytorch. Significance is further explained in <a href="https://www.youtube.com/watch?v=TrdevFK_am4">Yannic Kilcher's</a> video. There's really not much to code here, but may as well lay it out for everyone so we expedite the attention revolution.
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For a Pytorch implementation with pretrained models, please see Ross Wightman's repository <a href="https://github.com/rwightman/pytorch-image-models">here</a>.
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The official Jax repository is <a href="https://github.com/google-research/vision_transformer">here</a>.
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## Install
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```bash
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@@ -21,63 +17,6 @@ import torch
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from vit_pytorch import ViT
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v = ViT(
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image_size = 256,
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patch_size = 32,
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num_classes = 1000,
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dim = 1024,
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depth = 6,
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heads = 16,
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mlp_dim = 2048,
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dropout = 0.1,
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emb_dropout = 0.1
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)
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img = torch.randn(1, 3, 256, 256)
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preds = v(img) # (1, 1000)
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```
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## Parameters
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- `image_size`: int.
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Image size. If you have rectangular images, make sure your image size is the maximum of the width and height
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- `patch_size`: int.
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Number of patches. `image_size` must be divisible by `patch_size`.
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The number of patches is: ` n = (image_size // patch_size) ** 2` and `n` **must be greater than 16**.
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- `num_classes`: int.
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Number of classes to classify.
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- `dim`: int.
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Last dimension of output tensor after linear transformation `nn.Linear(..., dim)`.
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- `depth`: int.
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Number of Transformer blocks.
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- `heads`: int.
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Number of heads in Multi-head Attention layer.
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- `mlp_dim`: int.
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Dimension of the MLP (FeedForward) layer.
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- `channels`: int, default `3`.
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Number of image's channels.
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- `dropout`: float between `[0, 1]`, default `0.`.
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Dropout rate.
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- `emb_dropout`: float between `[0, 1]`, default `0`.
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Embedding dropout rate.
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- `pool`: string, either `cls` token pooling or `mean` pooling
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## Distillation
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<img src="./images/distill.png" width="300px"></img>
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A recent <a href="https://arxiv.org/abs/2012.12877">paper</a> has shown that use of a distillation token for distilling knowledge from convolutional nets to vision transformer can yield small and efficient vision transformers. This repository offers the means to do distillation easily.
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ex. distilling from Resnet50 (or any teacher) to a vision transformer
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```python
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import torch
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from torchvision.models import resnet50
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from vit_pytorch.distill import DistillableViT, DistillWrapper
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teacher = resnet50(pretrained = True)
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v = DistillableViT(
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image_size = 256,
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patch_size = 32,
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num_classes = 1000,
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@@ -89,336 +28,10 @@ v = DistillableViT(
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emb_dropout = 0.1
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)
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distiller = DistillWrapper(
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student = v,
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teacher = teacher,
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temperature = 3, # temperature of distillation
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alpha = 0.5, # trade between main loss and distillation loss
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hard = False # whether to use soft or hard distillation
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)
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img = torch.randn(2, 3, 256, 256)
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labels = torch.randint(0, 1000, (2,))
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loss = distiller(img, labels)
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loss.backward()
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# after lots of training above ...
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pred = v(img) # (2, 1000)
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```
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The `DistillableViT` class is identical to `ViT` except for how the forward pass is handled, so you should be able to load the parameters back to `ViT` after you have completed distillation training.
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You can also use the handy `.to_vit` method on the `DistillableViT` instance to get back a `ViT` instance.
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```python
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v = v.to_vit()
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type(v) # <class 'vit_pytorch.vit_pytorch.ViT'>
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```
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## Deep ViT
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This <a href="https://arxiv.org/abs/2103.11886">paper</a> notes that ViT struggles to attend at greater depths (past 12 layers), and suggests mixing the attention of each head post-softmax as a solution, dubbed Re-attention. The results line up with the <a href="https://github.com/lucidrains/x-transformers#talking-heads-attention">Talking Heads</a> paper from NLP.
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You can use it as follows
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```python
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import torch
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from vit_pytorch.deepvit import DeepViT
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v = DeepViT(
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image_size = 256,
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patch_size = 32,
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num_classes = 1000,
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dim = 1024,
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depth = 6,
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heads = 16,
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mlp_dim = 2048,
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dropout = 0.1,
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emb_dropout = 0.1
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)
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img = torch.randn(1, 3, 256, 256)
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mask = torch.ones(1, 8, 8).bool() # optional mask, designating which patch to attend to
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preds = v(img) # (1, 1000)
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```
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## CaiT
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<a href="https://arxiv.org/abs/2103.17239">This paper</a> also notes difficulty in training vision transformers at greater depths and proposes two solutions. First it proposes to do per-channel multiplication of the output of the residual block. Second, it proposes to have the patches attend to one another, and only allow the CLS token to attend to the patches in the last few layers.
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They also add <a href="https://github.com/lucidrains/x-transformers#talking-heads-attention">Talking Heads</a>, noting improvements
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You can use this scheme as follows
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```python
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import torch
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from vit_pytorch.cait import CaiT
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v = CaiT(
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image_size = 256,
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patch_size = 32,
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num_classes = 1000,
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dim = 1024,
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depth = 12, # depth of transformer for patch to patch attention only
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cls_depth = 2, # depth of cross attention of CLS tokens to patch
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heads = 16,
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mlp_dim = 2048,
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dropout = 0.1,
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emb_dropout = 0.1,
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layer_dropout = 0.05 # randomly dropout 5% of the layers
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)
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img = torch.randn(1, 3, 256, 256)
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preds = v(img) # (1, 1000)
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```
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## Token-to-Token ViT
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<img src="./images/t2t.png" width="400px"></img>
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<a href="https://arxiv.org/abs/2101.11986">This paper</a> proposes that the first couple layers should downsample the image sequence by unfolding, leading to overlapping image data in each token as shown in the figure above. You can use this variant of the `ViT` as follows.
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```python
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import torch
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from vit_pytorch.t2t import T2TViT
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v = T2TViT(
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dim = 512,
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image_size = 224,
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depth = 5,
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heads = 8,
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mlp_dim = 512,
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num_classes = 1000,
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t2t_layers = ((7, 4), (3, 2), (3, 2)) # tuples of the kernel size and stride of each consecutive layers of the initial token to token module
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)
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img = torch.randn(1, 3, 224, 224)
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preds = v(img) # (1, 1000)
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```
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## Cross ViT
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<img src="./images/cross_vit.png" width="400px"></img>
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<a href="https://arxiv.org/abs/2103.14899">This paper</a> proposes to have two vision transformers processing the image at different scales, cross attending to one every so often. They show improvements on top of the base vision transformer.
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```python
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import torch
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from vit_pytorch.cross_vit import CrossViT
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v = CrossViT(
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image_size = 256,
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num_classes = 1000,
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depth = 4, # number of multi-scale encoding blocks
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sm_dim = 192, # high res dimension
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sm_patch_size = 16, # high res patch size (should be smaller than lg_patch_size)
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sm_enc_depth = 2, # high res depth
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sm_enc_heads = 8, # high res heads
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sm_enc_mlp_dim = 2048, # high res feedforward dimension
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lg_dim = 384, # low res dimension
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lg_patch_size = 64, # low res patch size
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lg_enc_depth = 3, # low res depth
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lg_enc_heads = 8, # low res heads
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lg_enc_mlp_dim = 2048, # low res feedforward dimensions
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cross_attn_depth = 2, # cross attention rounds
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cross_attn_heads = 8, # cross attention heads
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dropout = 0.1,
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emb_dropout = 0.1
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)
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img = torch.randn(1, 3, 256, 256)
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pred = v(img) # (1, 1000)
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```
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## PiT
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<img src="./images/pit.png" width="400px"></img>
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<a href="https://arxiv.org/abs/2103.16302">This paper</a> proposes to downsample the tokens through a pooling procedure using depth-wise convolutions.
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```python
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import torch
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from vit_pytorch.pit import PiT
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v = PiT(
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image_size = 224,
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patch_size = 14,
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dim = 256,
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num_classes = 1000,
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depth = (3, 3, 3), # list of depths, indicating the number of rounds of each stage before a downsample
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heads = 16,
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mlp_dim = 2048,
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dropout = 0.1,
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emb_dropout = 0.1
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)
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# forward pass now returns predictions and the attention maps
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img = torch.randn(1, 3, 224, 224)
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preds = v(img) # (1, 1000)
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```
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## LeViT
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<img src="./images/levit.png" width="300px"></img>
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<a href="https://arxiv.org/abs/2104.01136">This paper</a> proposes a number of changes, including (1) convolutional embedding instead of patch-wise projection (2) downsampling in stages (3) extra non-linearity in attention (4) 2d relative positional biases instead of initial absolute positional bias (5) batchnorm in place of layernorm.
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```python
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import torch
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from vit_pytorch.levit import LeViT
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levit = LeViT(
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image_size = 224,
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num_classes = 1000,
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stages = 3, # number of stages
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dim = (256, 384, 512), # dimensions at each stage
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depth = 4, # transformer of depth 4 at each stage
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heads = (4, 6, 8), # heads at each stage
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mlp_mult = 2,
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dropout = 0.1
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)
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img = torch.randn(1, 3, 224, 224)
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levit(img) # (1, 1000)
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```
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## CvT
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<img src="./images/cvt.png" width="400px"></img>
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<a href="https://arxiv.org/abs/2103.15808">This paper</a> proposes mixing convolutions and attention. Specifically, convolutions are used to embed and downsample the image / feature map in three stages. Depthwise-convoltion is also used to project the queries, keys, and values for attention.
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```python
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import torch
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from vit_pytorch.cvt import CvT
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v = CvT(
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num_classes = 1000,
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s1_emb_dim = 64, # stage 1 - dimension
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s1_emb_kernel = 7, # stage 1 - conv kernel
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s1_emb_stride = 4, # stage 1 - conv stride
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s1_proj_kernel = 3, # stage 1 - attention ds-conv kernel size
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s1_kv_proj_stride = 2, # stage 1 - attention key / value projection stride
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s1_heads = 1, # stage 1 - heads
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s1_depth = 1, # stage 1 - depth
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s1_mlp_mult = 4, # stage 1 - feedforward expansion factor
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s2_emb_dim = 192, # stage 2 - (same as above)
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s2_emb_kernel = 3,
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s2_emb_stride = 2,
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s2_proj_kernel = 3,
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s2_kv_proj_stride = 2,
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s2_heads = 3,
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s2_depth = 2,
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s2_mlp_mult = 4,
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s3_emb_dim = 384, # stage 3 - (same as above)
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s3_emb_kernel = 3,
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s3_emb_stride = 2,
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s3_proj_kernel = 3,
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s3_kv_proj_stride = 2,
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s3_heads = 4,
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s3_depth = 10,
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s3_mlp_mult = 4,
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dropout = 0.
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)
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img = torch.randn(1, 3, 224, 224)
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pred = v(img) # (1, 1000)
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```
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## Masked Patch Prediction
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Thanks to <a href="https://github.com/zankner">Zach</a>, you can train using the original masked patch prediction task presented in the paper, with the following code.
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```python
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import torch
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from vit_pytorch import ViT
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from vit_pytorch.mpp import MPP
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model = ViT(
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image_size=256,
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patch_size=32,
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num_classes=1000,
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dim=1024,
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depth=6,
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heads=8,
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mlp_dim=2048,
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dropout=0.1,
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emb_dropout=0.1
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)
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mpp_trainer = MPP(
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transformer=model,
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patch_size=32,
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dim=1024,
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mask_prob=0.15, # probability of using token in masked prediction task
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random_patch_prob=0.30, # probability of randomly replacing a token being used for mpp
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replace_prob=0.50, # probability of replacing a token being used for mpp with the mask token
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)
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opt = torch.optim.Adam(mpp_trainer.parameters(), lr=3e-4)
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def sample_unlabelled_images():
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return torch.randn(20, 3, 256, 256)
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for _ in range(100):
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images = sample_unlabelled_images()
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loss = mpp_trainer(images)
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opt.zero_grad()
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loss.backward()
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opt.step()
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# save your improved network
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torch.save(model.state_dict(), './pretrained-net.pt')
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```
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## Accessing Attention
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||||
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||||
If you would like to visualize the attention weights (post-softmax) for your research, just follow the procedure below
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|
||||
```python
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import torch
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from vit_pytorch.vit import ViT
|
||||
|
||||
v = ViT(
|
||||
image_size = 256,
|
||||
patch_size = 32,
|
||||
num_classes = 1000,
|
||||
dim = 1024,
|
||||
depth = 6,
|
||||
heads = 16,
|
||||
mlp_dim = 2048,
|
||||
dropout = 0.1,
|
||||
emb_dropout = 0.1
|
||||
)
|
||||
|
||||
# import Recorder and wrap the ViT
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||||
|
||||
from vit_pytorch.recorder import Recorder
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v = Recorder(v)
|
||||
|
||||
# forward pass now returns predictions and the attention maps
|
||||
|
||||
img = torch.randn(1, 3, 256, 256)
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preds, attns = v(img)
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||||
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||||
# there is one extra patch due to the CLS token
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||||
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attns # (1, 6, 16, 65, 65) - (batch x layers x heads x patch x patch)
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```
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||||
to cleanup the class and the hooks once you have collected enough data
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||||
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||||
```python
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v = v.eject() # wrapper is discarded and original ViT instance is returned
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preds = v(img, mask = mask) # (1, 1000)
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```
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## Research Ideas
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||||
@@ -451,7 +64,7 @@ model = ViT(
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learner = BYOL(
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model,
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image_size = 256,
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hidden_layer = 'to_latent'
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hidden_layer = 'to_cls_token'
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)
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opt = torch.optim.Adam(learner.parameters(), lr=3e-4)
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@@ -477,22 +90,23 @@ A pytorch-lightning script is ready for you to use at the repository link above.
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||||
There may be some coming from computer vision who think attention still suffers from quadratic costs. Fortunately, we have a lot of new techniques that may help. This repository offers a way for you to plugin your own sparse attention transformer.
|
||||
|
||||
An example with <a href="https://arxiv.org/abs/2102.03902">Nystromformer</a>
|
||||
An example with <a href="https://arxiv.org/abs/2006.04768">Linformer</a>
|
||||
|
||||
```bash
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||||
$ pip install nystrom-attention
|
||||
$ pip install linformer
|
||||
```
|
||||
|
||||
```python
|
||||
import torch
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||||
from vit_pytorch.efficient import ViT
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||||
from nystrom_attention import Nystromformer
|
||||
from linformer import Linformer
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||||
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||||
efficient_transformer = Nystromformer(
|
||||
efficient_transformer = Linformer(
|
||||
dim = 512,
|
||||
seq_len = 4096 + 1, # 64 x 64 patches + 1 cls token
|
||||
depth = 12,
|
||||
heads = 8,
|
||||
num_landmarks = 256
|
||||
k = 256
|
||||
)
|
||||
|
||||
v = ViT(
|
||||
@@ -509,171 +123,16 @@ v(img) # (1, 1000)
|
||||
|
||||
Other sparse attention frameworks I would highly recommend is <a href="https://github.com/lucidrains/routing-transformer">Routing Transformer</a> or <a href="https://github.com/lucidrains/sinkhorn-transformer">Sinkhorn Transformer</a>
|
||||
|
||||
### Combining with other Transformer improvements
|
||||
|
||||
This paper purposely used the most vanilla of attention networks to make a statement. If you would like to use some of the latest improvements for attention nets, please use the `Encoder` from <a href="https://github.com/lucidrains/x-transformers">this repository</a>.
|
||||
|
||||
ex.
|
||||
|
||||
```bash
|
||||
$ pip install x-transformers
|
||||
```
|
||||
|
||||
```python
|
||||
import torch
|
||||
from vit_pytorch.efficient import ViT
|
||||
from x_transformers import Encoder
|
||||
|
||||
v = ViT(
|
||||
dim = 512,
|
||||
image_size = 224,
|
||||
patch_size = 16,
|
||||
num_classes = 1000,
|
||||
transformer = Encoder(
|
||||
dim = 512, # set to be the same as the wrapper
|
||||
depth = 12,
|
||||
heads = 8,
|
||||
ff_glu = True, # ex. feed forward GLU variant https://arxiv.org/abs/2002.05202
|
||||
residual_attn = True # ex. residual attention https://arxiv.org/abs/2012.11747
|
||||
)
|
||||
)
|
||||
|
||||
img = torch.randn(1, 3, 224, 224)
|
||||
v(img) # (1, 1000)
|
||||
```
|
||||
|
||||
## Resources
|
||||
|
||||
Coming from computer vision and new to transformers? Here are some resources that greatly accelerated my learning.
|
||||
|
||||
1. <a href="http://jalammar.github.io/illustrated-transformer/">Illustrated Transformer</a> - Jay Alammar
|
||||
|
||||
2. <a href="http://peterbloem.nl/blog/transformers">Transformers from Scratch</a> - Peter Bloem
|
||||
|
||||
3. <a href="https://nlp.seas.harvard.edu/2018/04/03/attention.html">The Annotated Transformer</a> - Harvard NLP
|
||||
|
||||
|
||||
## Citations
|
||||
|
||||
```bibtex
|
||||
@misc{dosovitskiy2020image,
|
||||
title = {An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale},
|
||||
author = {Alexey Dosovitskiy and Lucas Beyer and Alexander Kolesnikov and Dirk Weissenborn and Xiaohua Zhai and Thomas Unterthiner and Mostafa Dehghani and Matthias Minderer and Georg Heigold and Sylvain Gelly and Jakob Uszkoreit and Neil Houlsby},
|
||||
year = {2020},
|
||||
eprint = {2010.11929},
|
||||
archivePrefix = {arXiv},
|
||||
primaryClass = {cs.CV}
|
||||
@inproceedings{
|
||||
anonymous2021an,
|
||||
title={An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale},
|
||||
author={Anonymous},
|
||||
booktitle={Submitted to International Conference on Learning Representations},
|
||||
year={2021},
|
||||
url={https://openreview.net/forum?id=YicbFdNTTy},
|
||||
note={under review}
|
||||
}
|
||||
```
|
||||
|
||||
```bibtex
|
||||
@misc{touvron2020training,
|
||||
title = {Training data-efficient image transformers & distillation through attention},
|
||||
author = {Hugo Touvron and Matthieu Cord and Matthijs Douze and Francisco Massa and Alexandre Sablayrolles and Hervé Jégou},
|
||||
year = {2020},
|
||||
eprint = {2012.12877},
|
||||
archivePrefix = {arXiv},
|
||||
primaryClass = {cs.CV}
|
||||
}
|
||||
```
|
||||
|
||||
```bibtex
|
||||
@misc{yuan2021tokenstotoken,
|
||||
title = {Tokens-to-Token ViT: Training Vision Transformers from Scratch on ImageNet},
|
||||
author = {Li Yuan and Yunpeng Chen and Tao Wang and Weihao Yu and Yujun Shi and Francis EH Tay and Jiashi Feng and Shuicheng Yan},
|
||||
year = {2021},
|
||||
eprint = {2101.11986},
|
||||
archivePrefix = {arXiv},
|
||||
primaryClass = {cs.CV}
|
||||
}
|
||||
```
|
||||
|
||||
```bibtex
|
||||
@misc{zhou2021deepvit,
|
||||
title = {DeepViT: Towards Deeper Vision Transformer},
|
||||
author = {Daquan Zhou and Bingyi Kang and Xiaojie Jin and Linjie Yang and Xiaochen Lian and Qibin Hou and Jiashi Feng},
|
||||
year = {2021},
|
||||
eprint = {2103.11886},
|
||||
archivePrefix = {arXiv},
|
||||
primaryClass = {cs.CV}
|
||||
}
|
||||
```
|
||||
|
||||
```bibtex
|
||||
@misc{touvron2021going,
|
||||
title = {Going deeper with Image Transformers},
|
||||
author = {Hugo Touvron and Matthieu Cord and Alexandre Sablayrolles and Gabriel Synnaeve and Hervé Jégou},
|
||||
year = {2021},
|
||||
eprint = {2103.17239},
|
||||
archivePrefix = {arXiv},
|
||||
primaryClass = {cs.CV}
|
||||
}
|
||||
```
|
||||
|
||||
```bibtex
|
||||
@misc{chen2021crossvit,
|
||||
title = {CrossViT: Cross-Attention Multi-Scale Vision Transformer for Image Classification},
|
||||
author = {Chun-Fu Chen and Quanfu Fan and Rameswar Panda},
|
||||
year = {2021},
|
||||
eprint = {2103.14899},
|
||||
archivePrefix = {arXiv},
|
||||
primaryClass = {cs.CV}
|
||||
}
|
||||
```
|
||||
|
||||
```bibtex
|
||||
@misc{wu2021cvt,
|
||||
title = {CvT: Introducing Convolutions to Vision Transformers},
|
||||
author = {Haiping Wu and Bin Xiao and Noel Codella and Mengchen Liu and Xiyang Dai and Lu Yuan and Lei Zhang},
|
||||
year = {2021},
|
||||
eprint = {2103.15808},
|
||||
archivePrefix = {arXiv},
|
||||
primaryClass = {cs.CV}
|
||||
}
|
||||
```
|
||||
|
||||
```bibtex
|
||||
@misc{heo2021rethinking,
|
||||
title = {Rethinking Spatial Dimensions of Vision Transformers},
|
||||
author = {Byeongho Heo and Sangdoo Yun and Dongyoon Han and Sanghyuk Chun and Junsuk Choe and Seong Joon Oh},
|
||||
year = {2021},
|
||||
eprint = {2103.16302},
|
||||
archivePrefix = {arXiv},
|
||||
primaryClass = {cs.CV}
|
||||
}
|
||||
```
|
||||
|
||||
```bibtex
|
||||
@misc{graham2021levit,
|
||||
title = {LeViT: a Vision Transformer in ConvNet's Clothing for Faster Inference},
|
||||
author = {Ben Graham and Alaaeldin El-Nouby and Hugo Touvron and Pierre Stock and Armand Joulin and Hervé Jégou and Matthijs Douze},
|
||||
year = {2021},
|
||||
eprint = {2104.01136},
|
||||
archivePrefix = {arXiv},
|
||||
primaryClass = {cs.CV}
|
||||
}
|
||||
```
|
||||
|
||||
```bibtex
|
||||
@misc{li2021localvit,
|
||||
title = {LocalViT: Bringing Locality to Vision Transformers},
|
||||
author = {Yawei Li and Kai Zhang and Jiezhang Cao and Radu Timofte and Luc Van Gool},
|
||||
year = {2021},
|
||||
eprint = {2104.05707},
|
||||
archivePrefix = {arXiv},
|
||||
primaryClass = {cs.CV}
|
||||
}
|
||||
```
|
||||
|
||||
```bibtex
|
||||
@misc{vaswani2017attention,
|
||||
title = {Attention Is All You Need},
|
||||
author = {Ashish Vaswani and Noam Shazeer and Niki Parmar and Jakob Uszkoreit and Llion Jones and Aidan N. Gomez and Lukasz Kaiser and Illia Polosukhin},
|
||||
year = {2017},
|
||||
eprint = {1706.03762},
|
||||
archivePrefix = {arXiv},
|
||||
primaryClass = {cs.CL}
|
||||
}
|
||||
```
|
||||
|
||||
*I visualise a time when we will be to robots what dogs are to humans, and I’m rooting for the machines.* — Claude Shannon
|
||||
|
||||
BIN
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2
setup.py
2
setup.py
@@ -3,7 +3,7 @@ from setuptools import setup, find_packages
|
||||
setup(
|
||||
name = 'vit-pytorch',
|
||||
packages = find_packages(exclude=['examples']),
|
||||
version = '0.16.0',
|
||||
version = '0.2.2',
|
||||
license='MIT',
|
||||
description = 'Vision Transformer (ViT) - Pytorch',
|
||||
author = 'Phil Wang',
|
||||
|
||||
@@ -1 +1 @@
|
||||
from vit_pytorch.vit import ViT
|
||||
from vit_pytorch.vit_pytorch import ViT
|
||||
|
||||
@@ -1,177 +0,0 @@
|
||||
from random import randrange
|
||||
import torch
|
||||
from torch import nn, einsum
|
||||
import torch.nn.functional as F
|
||||
|
||||
from einops import rearrange, repeat
|
||||
from einops.layers.torch import Rearrange
|
||||
|
||||
# helpers
|
||||
|
||||
def exists(val):
|
||||
return val is not None
|
||||
|
||||
def dropout_layers(layers, dropout):
|
||||
if dropout == 0:
|
||||
return layers
|
||||
|
||||
num_layers = len(layers)
|
||||
to_drop = torch.zeros(num_layers).uniform_(0., 1.) < dropout
|
||||
|
||||
# make sure at least one layer makes it
|
||||
if all(to_drop):
|
||||
rand_index = randrange(num_layers)
|
||||
to_drop[rand_index] = False
|
||||
|
||||
layers = [layer for (layer, drop) in zip(layers, to_drop) if not drop]
|
||||
return layers
|
||||
|
||||
# classes
|
||||
|
||||
class LayerScale(nn.Module):
|
||||
def __init__(self, dim, fn, depth):
|
||||
super().__init__()
|
||||
if depth <= 18: # epsilon detailed in section 2 of paper
|
||||
init_eps = 0.1
|
||||
elif depth > 18 and depth <= 24:
|
||||
init_eps = 1e-5
|
||||
else:
|
||||
init_eps = 1e-6
|
||||
|
||||
scale = torch.zeros(1, 1, dim).fill_(init_eps)
|
||||
self.scale = nn.Parameter(scale)
|
||||
self.fn = fn
|
||||
def forward(self, x, **kwargs):
|
||||
return self.fn(x, **kwargs) * self.scale
|
||||
|
||||
class PreNorm(nn.Module):
|
||||
def __init__(self, dim, fn):
|
||||
super().__init__()
|
||||
self.norm = nn.LayerNorm(dim)
|
||||
self.fn = fn
|
||||
def forward(self, x, **kwargs):
|
||||
return self.fn(self.norm(x), **kwargs)
|
||||
|
||||
class FeedForward(nn.Module):
|
||||
def __init__(self, dim, hidden_dim, dropout = 0.):
|
||||
super().__init__()
|
||||
self.net = nn.Sequential(
|
||||
nn.Linear(dim, hidden_dim),
|
||||
nn.GELU(),
|
||||
nn.Dropout(dropout),
|
||||
nn.Linear(hidden_dim, dim),
|
||||
nn.Dropout(dropout)
|
||||
)
|
||||
def forward(self, x):
|
||||
return self.net(x)
|
||||
|
||||
class Attention(nn.Module):
|
||||
def __init__(self, dim, heads = 8, dim_head = 64, dropout = 0.):
|
||||
super().__init__()
|
||||
inner_dim = dim_head * heads
|
||||
self.heads = heads
|
||||
self.scale = dim_head ** -0.5
|
||||
|
||||
self.to_q = nn.Linear(dim, inner_dim, bias = False)
|
||||
self.to_kv = nn.Linear(dim, inner_dim * 2, bias = False)
|
||||
|
||||
self.attend = nn.Softmax(dim = -1)
|
||||
|
||||
self.mix_heads_pre_attn = nn.Parameter(torch.randn(heads, heads))
|
||||
self.mix_heads_post_attn = nn.Parameter(torch.randn(heads, heads))
|
||||
|
||||
self.to_out = nn.Sequential(
|
||||
nn.Linear(inner_dim, dim),
|
||||
nn.Dropout(dropout)
|
||||
)
|
||||
|
||||
def forward(self, x, context = None):
|
||||
b, n, _, h = *x.shape, self.heads
|
||||
|
||||
context = x if not exists(context) else torch.cat((x, context), dim = 1)
|
||||
|
||||
qkv = (self.to_q(x), *self.to_kv(context).chunk(2, dim = -1))
|
||||
q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> b h n d', h = h), qkv)
|
||||
|
||||
dots = einsum('b h i d, b h j d -> b h i j', q, k) * self.scale
|
||||
|
||||
dots = einsum('b h i j, h g -> b g i j', dots, self.mix_heads_pre_attn) # talking heads, pre-softmax
|
||||
attn = self.attend(dots)
|
||||
attn = einsum('b h i j, h g -> b g i j', attn, self.mix_heads_post_attn) # talking heads, post-softmax
|
||||
|
||||
out = einsum('b h i j, b h j d -> b h i d', attn, v)
|
||||
out = rearrange(out, 'b h n d -> b n (h d)')
|
||||
return self.to_out(out)
|
||||
|
||||
class Transformer(nn.Module):
|
||||
def __init__(self, dim, depth, heads, dim_head, mlp_dim, dropout = 0., layer_dropout = 0.):
|
||||
super().__init__()
|
||||
self.layers = nn.ModuleList([])
|
||||
self.layer_dropout = layer_dropout
|
||||
|
||||
for ind in range(depth):
|
||||
self.layers.append(nn.ModuleList([
|
||||
LayerScale(dim, PreNorm(dim, Attention(dim, heads = heads, dim_head = dim_head, dropout = dropout)), depth = ind + 1),
|
||||
LayerScale(dim, PreNorm(dim, FeedForward(dim, mlp_dim, dropout = dropout)), depth = ind + 1)
|
||||
]))
|
||||
def forward(self, x, context = None):
|
||||
layers = dropout_layers(self.layers, dropout = self.layer_dropout)
|
||||
|
||||
for attn, ff in layers:
|
||||
x = attn(x, context = context) + x
|
||||
x = ff(x) + x
|
||||
return x
|
||||
|
||||
class CaiT(nn.Module):
|
||||
def __init__(
|
||||
self,
|
||||
*,
|
||||
image_size,
|
||||
patch_size,
|
||||
num_classes,
|
||||
dim,
|
||||
depth,
|
||||
cls_depth,
|
||||
heads,
|
||||
mlp_dim,
|
||||
dim_head = 64,
|
||||
dropout = 0.,
|
||||
emb_dropout = 0.,
|
||||
layer_dropout = 0.
|
||||
):
|
||||
super().__init__()
|
||||
assert image_size % patch_size == 0, 'Image dimensions must be divisible by the patch size.'
|
||||
num_patches = (image_size // patch_size) ** 2
|
||||
patch_dim = 3 * patch_size ** 2
|
||||
|
||||
self.to_patch_embedding = nn.Sequential(
|
||||
Rearrange('b c (h p1) (w p2) -> b (h w) (p1 p2 c)', p1 = patch_size, p2 = patch_size),
|
||||
nn.Linear(patch_dim, dim),
|
||||
)
|
||||
|
||||
self.pos_embedding = nn.Parameter(torch.randn(1, num_patches, dim))
|
||||
self.cls_token = nn.Parameter(torch.randn(1, 1, dim))
|
||||
|
||||
self.dropout = nn.Dropout(emb_dropout)
|
||||
|
||||
self.patch_transformer = Transformer(dim, depth, heads, dim_head, mlp_dim, dropout, layer_dropout)
|
||||
self.cls_transformer = Transformer(dim, cls_depth, heads, dim_head, mlp_dim, dropout, layer_dropout)
|
||||
|
||||
self.mlp_head = nn.Sequential(
|
||||
nn.LayerNorm(dim),
|
||||
nn.Linear(dim, num_classes)
|
||||
)
|
||||
|
||||
def forward(self, img):
|
||||
x = self.to_patch_embedding(img)
|
||||
b, n, _ = x.shape
|
||||
|
||||
x += self.pos_embedding[:, :n]
|
||||
x = self.dropout(x)
|
||||
|
||||
x = self.patch_transformer(x)
|
||||
|
||||
cls_tokens = repeat(self.cls_token, '() n d -> b n d', b = b)
|
||||
x = self.cls_transformer(cls_tokens, context = x)
|
||||
|
||||
return self.mlp_head(x[:, 0])
|
||||
@@ -1,270 +0,0 @@
|
||||
import torch
|
||||
from torch import nn, einsum
|
||||
import torch.nn.functional as F
|
||||
|
||||
from einops import rearrange, repeat
|
||||
from einops.layers.torch import Rearrange
|
||||
|
||||
# helpers
|
||||
|
||||
def exists(val):
|
||||
return val is not None
|
||||
|
||||
def default(val, d):
|
||||
return val if exists(val) else d
|
||||
|
||||
# pre-layernorm
|
||||
|
||||
class PreNorm(nn.Module):
|
||||
def __init__(self, dim, fn):
|
||||
super().__init__()
|
||||
self.norm = nn.LayerNorm(dim)
|
||||
self.fn = fn
|
||||
def forward(self, x, **kwargs):
|
||||
return self.fn(self.norm(x), **kwargs)
|
||||
|
||||
# feedforward
|
||||
|
||||
class FeedForward(nn.Module):
|
||||
def __init__(self, dim, hidden_dim, dropout = 0.):
|
||||
super().__init__()
|
||||
self.net = nn.Sequential(
|
||||
nn.Linear(dim, hidden_dim),
|
||||
nn.GELU(),
|
||||
nn.Dropout(dropout),
|
||||
nn.Linear(hidden_dim, dim),
|
||||
nn.Dropout(dropout)
|
||||
)
|
||||
def forward(self, x):
|
||||
return self.net(x)
|
||||
|
||||
# attention
|
||||
|
||||
class Attention(nn.Module):
|
||||
def __init__(self, dim, heads = 8, dim_head = 64, dropout = 0.):
|
||||
super().__init__()
|
||||
inner_dim = dim_head * heads
|
||||
self.heads = heads
|
||||
self.scale = dim_head ** -0.5
|
||||
|
||||
self.attend = nn.Softmax(dim = -1)
|
||||
self.to_q = nn.Linear(dim, inner_dim, bias = False)
|
||||
self.to_kv = nn.Linear(dim, inner_dim * 2, bias = False)
|
||||
|
||||
self.to_out = nn.Sequential(
|
||||
nn.Linear(inner_dim, dim),
|
||||
nn.Dropout(dropout)
|
||||
)
|
||||
|
||||
def forward(self, x, context = None, kv_include_self = False):
|
||||
b, n, _, h = *x.shape, self.heads
|
||||
context = default(context, x)
|
||||
|
||||
if kv_include_self:
|
||||
context = torch.cat((x, context), dim = 1) # cross attention requires CLS token includes itself as key / value
|
||||
|
||||
qkv = (self.to_q(x), *self.to_kv(context).chunk(2, dim = -1))
|
||||
q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> b h n d', h = h), qkv)
|
||||
|
||||
dots = einsum('b h i d, b h j d -> b h i j', q, k) * self.scale
|
||||
|
||||
attn = self.attend(dots)
|
||||
|
||||
out = einsum('b h i j, b h j d -> b h i d', attn, v)
|
||||
out = rearrange(out, 'b h n d -> b n (h d)')
|
||||
return self.to_out(out)
|
||||
|
||||
# transformer encoder, for small and large patches
|
||||
|
||||
class Transformer(nn.Module):
|
||||
def __init__(self, dim, depth, heads, dim_head, mlp_dim, dropout = 0.):
|
||||
super().__init__()
|
||||
self.layers = nn.ModuleList([])
|
||||
self.norm = nn.LayerNorm(dim)
|
||||
for _ in range(depth):
|
||||
self.layers.append(nn.ModuleList([
|
||||
PreNorm(dim, Attention(dim, heads = heads, dim_head = dim_head, dropout = dropout)),
|
||||
PreNorm(dim, FeedForward(dim, mlp_dim, dropout = dropout))
|
||||
]))
|
||||
|
||||
def forward(self, x):
|
||||
for attn, ff in self.layers:
|
||||
x = attn(x) + x
|
||||
x = ff(x) + x
|
||||
return self.norm(x)
|
||||
|
||||
# projecting CLS tokens, in the case that small and large patch tokens have different dimensions
|
||||
|
||||
class ProjectInOut(nn.Module):
|
||||
def __init__(self, dim_in, dim_out, fn):
|
||||
super().__init__()
|
||||
self.fn = fn
|
||||
|
||||
need_projection = dim_in != dim_out
|
||||
self.project_in = nn.Linear(dim_in, dim_out) if need_projection else nn.Identity()
|
||||
self.project_out = nn.Linear(dim_out, dim_in) if need_projection else nn.Identity()
|
||||
|
||||
def forward(self, x, *args, **kwargs):
|
||||
x = self.project_in(x)
|
||||
x = self.fn(x, *args, **kwargs)
|
||||
x = self.project_out(x)
|
||||
return x
|
||||
|
||||
# cross attention transformer
|
||||
|
||||
class CrossTransformer(nn.Module):
|
||||
def __init__(self, sm_dim, lg_dim, depth, heads, dim_head, dropout):
|
||||
super().__init__()
|
||||
self.layers = nn.ModuleList([])
|
||||
for _ in range(depth):
|
||||
self.layers.append(nn.ModuleList([
|
||||
ProjectInOut(sm_dim, lg_dim, PreNorm(lg_dim, Attention(lg_dim, heads = heads, dim_head = dim_head, dropout = dropout))),
|
||||
ProjectInOut(lg_dim, sm_dim, PreNorm(sm_dim, Attention(sm_dim, heads = heads, dim_head = dim_head, dropout = dropout)))
|
||||
]))
|
||||
|
||||
def forward(self, sm_tokens, lg_tokens):
|
||||
(sm_cls, sm_patch_tokens), (lg_cls, lg_patch_tokens) = map(lambda t: (t[:, :1], t[:, 1:]), (sm_tokens, lg_tokens))
|
||||
|
||||
for sm_attend_lg, lg_attend_sm in self.layers:
|
||||
sm_cls = sm_attend_lg(sm_cls, context = lg_patch_tokens, kv_include_self = True) + sm_cls
|
||||
lg_cls = lg_attend_sm(lg_cls, context = sm_patch_tokens, kv_include_self = True) + lg_cls
|
||||
|
||||
sm_tokens = torch.cat((sm_cls, sm_patch_tokens), dim = 1)
|
||||
lg_tokens = torch.cat((lg_cls, lg_patch_tokens), dim = 1)
|
||||
return sm_tokens, lg_tokens
|
||||
|
||||
# multi-scale encoder
|
||||
|
||||
class MultiScaleEncoder(nn.Module):
|
||||
def __init__(
|
||||
self,
|
||||
*,
|
||||
depth,
|
||||
sm_dim,
|
||||
lg_dim,
|
||||
sm_enc_params,
|
||||
lg_enc_params,
|
||||
cross_attn_heads,
|
||||
cross_attn_depth,
|
||||
cross_attn_dim_head = 64,
|
||||
dropout = 0.
|
||||
):
|
||||
super().__init__()
|
||||
self.layers = nn.ModuleList([])
|
||||
for _ in range(depth):
|
||||
self.layers.append(nn.ModuleList([
|
||||
Transformer(dim = sm_dim, dropout = dropout, **sm_enc_params),
|
||||
Transformer(dim = lg_dim, dropout = dropout, **lg_enc_params),
|
||||
CrossTransformer(sm_dim = sm_dim, lg_dim = lg_dim, depth = cross_attn_depth, heads = cross_attn_heads, dim_head = cross_attn_dim_head, dropout = dropout)
|
||||
]))
|
||||
|
||||
def forward(self, sm_tokens, lg_tokens):
|
||||
for sm_enc, lg_enc, cross_attend in self.layers:
|
||||
sm_tokens, lg_tokens = sm_enc(sm_tokens), lg_enc(lg_tokens)
|
||||
sm_tokens, lg_tokens = cross_attend(sm_tokens, lg_tokens)
|
||||
|
||||
return sm_tokens, lg_tokens
|
||||
|
||||
# patch-based image to token embedder
|
||||
|
||||
class ImageEmbedder(nn.Module):
|
||||
def __init__(
|
||||
self,
|
||||
*,
|
||||
dim,
|
||||
image_size,
|
||||
patch_size,
|
||||
dropout = 0.
|
||||
):
|
||||
super().__init__()
|
||||
assert image_size % patch_size == 0, 'Image dimensions must be divisible by the patch size.'
|
||||
num_patches = (image_size // patch_size) ** 2
|
||||
patch_dim = 3 * patch_size ** 2
|
||||
|
||||
self.to_patch_embedding = nn.Sequential(
|
||||
Rearrange('b c (h p1) (w p2) -> b (h w) (p1 p2 c)', p1 = patch_size, p2 = patch_size),
|
||||
nn.Linear(patch_dim, dim),
|
||||
)
|
||||
|
||||
self.pos_embedding = nn.Parameter(torch.randn(1, num_patches + 1, dim))
|
||||
self.cls_token = nn.Parameter(torch.randn(1, 1, dim))
|
||||
self.dropout = nn.Dropout(dropout)
|
||||
|
||||
def forward(self, img):
|
||||
x = self.to_patch_embedding(img)
|
||||
b, n, _ = x.shape
|
||||
|
||||
cls_tokens = repeat(self.cls_token, '() n d -> b n d', b = b)
|
||||
x = torch.cat((cls_tokens, x), dim=1)
|
||||
x += self.pos_embedding[:, :(n + 1)]
|
||||
|
||||
return self.dropout(x)
|
||||
|
||||
# cross ViT class
|
||||
|
||||
class CrossViT(nn.Module):
|
||||
def __init__(
|
||||
self,
|
||||
*,
|
||||
image_size,
|
||||
num_classes,
|
||||
sm_dim,
|
||||
lg_dim,
|
||||
sm_patch_size = 12,
|
||||
sm_enc_depth = 1,
|
||||
sm_enc_heads = 8,
|
||||
sm_enc_mlp_dim = 2048,
|
||||
sm_enc_dim_head = 64,
|
||||
lg_patch_size = 16,
|
||||
lg_enc_depth = 4,
|
||||
lg_enc_heads = 8,
|
||||
lg_enc_mlp_dim = 2048,
|
||||
lg_enc_dim_head = 64,
|
||||
cross_attn_depth = 2,
|
||||
cross_attn_heads = 8,
|
||||
cross_attn_dim_head = 64,
|
||||
depth = 3,
|
||||
dropout = 0.1,
|
||||
emb_dropout = 0.1
|
||||
):
|
||||
super().__init__()
|
||||
self.sm_image_embedder = ImageEmbedder(dim = sm_dim, image_size = image_size, patch_size = sm_patch_size, dropout = emb_dropout)
|
||||
self.lg_image_embedder = ImageEmbedder(dim = lg_dim, image_size = image_size, patch_size = lg_patch_size, dropout = emb_dropout)
|
||||
|
||||
self.multi_scale_encoder = MultiScaleEncoder(
|
||||
depth = depth,
|
||||
sm_dim = sm_dim,
|
||||
lg_dim = lg_dim,
|
||||
cross_attn_heads = cross_attn_heads,
|
||||
cross_attn_dim_head = cross_attn_dim_head,
|
||||
cross_attn_depth = cross_attn_depth,
|
||||
sm_enc_params = dict(
|
||||
depth = sm_enc_depth,
|
||||
heads = sm_enc_heads,
|
||||
mlp_dim = sm_enc_mlp_dim,
|
||||
dim_head = sm_enc_dim_head
|
||||
),
|
||||
lg_enc_params = dict(
|
||||
depth = lg_enc_depth,
|
||||
heads = lg_enc_heads,
|
||||
mlp_dim = lg_enc_mlp_dim,
|
||||
dim_head = lg_enc_dim_head
|
||||
),
|
||||
dropout = dropout
|
||||
)
|
||||
|
||||
self.sm_mlp_head = nn.Sequential(nn.LayerNorm(sm_dim), nn.Linear(sm_dim, num_classes))
|
||||
self.lg_mlp_head = nn.Sequential(nn.LayerNorm(lg_dim), nn.Linear(lg_dim, num_classes))
|
||||
|
||||
def forward(self, img):
|
||||
sm_tokens = self.sm_image_embedder(img)
|
||||
lg_tokens = self.lg_image_embedder(img)
|
||||
|
||||
sm_tokens, lg_tokens = self.multi_scale_encoder(sm_tokens, lg_tokens)
|
||||
|
||||
sm_cls, lg_cls = map(lambda t: t[:, 0], (sm_tokens, lg_tokens))
|
||||
|
||||
sm_logits = self.sm_mlp_head(sm_cls)
|
||||
lg_logits = self.lg_mlp_head(lg_cls)
|
||||
|
||||
return sm_logits + lg_logits
|
||||
@@ -1,162 +0,0 @@
|
||||
import torch
|
||||
from torch import nn, einsum
|
||||
import torch.nn.functional as F
|
||||
|
||||
from einops import rearrange, repeat
|
||||
from einops.layers.torch import Rearrange
|
||||
|
||||
# helper methods
|
||||
|
||||
def group_dict_by_key(cond, d):
|
||||
return_val = [dict(), dict()]
|
||||
for key in d.keys():
|
||||
match = bool(cond(key))
|
||||
ind = int(not match)
|
||||
return_val[ind][key] = d[key]
|
||||
return (*return_val,)
|
||||
|
||||
def group_by_key_prefix_and_remove_prefix(prefix, d):
|
||||
kwargs_with_prefix, kwargs = group_dict_by_key(lambda x: x.startswith(prefix), d)
|
||||
kwargs_without_prefix = dict(map(lambda x: (x[0][len(prefix):], x[1]), tuple(kwargs_with_prefix.items())))
|
||||
return kwargs_without_prefix, kwargs
|
||||
|
||||
# classes
|
||||
|
||||
class PreNorm(nn.Module):
|
||||
def __init__(self, dim, fn):
|
||||
super().__init__()
|
||||
self.norm = nn.LayerNorm(dim)
|
||||
self.fn = fn
|
||||
def forward(self, x, **kwargs):
|
||||
x = rearrange(x, 'b c h w -> b h w c')
|
||||
x = self.norm(x)
|
||||
x = rearrange(x, 'b h w c -> b c h w')
|
||||
return self.fn(x, **kwargs)
|
||||
|
||||
class FeedForward(nn.Module):
|
||||
def __init__(self, dim, mult = 4, dropout = 0.):
|
||||
super().__init__()
|
||||
self.net = nn.Sequential(
|
||||
nn.Conv2d(dim, dim * mult, 1),
|
||||
nn.GELU(),
|
||||
nn.Dropout(dropout),
|
||||
nn.Conv2d(dim * mult, dim, 1),
|
||||
nn.Dropout(dropout)
|
||||
)
|
||||
def forward(self, x):
|
||||
return self.net(x)
|
||||
|
||||
class DepthWiseConv2d(nn.Module):
|
||||
def __init__(self, dim_in, dim_out, kernel_size, padding, stride, bias = True):
|
||||
super().__init__()
|
||||
self.net = nn.Sequential(
|
||||
nn.Conv2d(dim_in, dim_in, kernel_size = kernel_size, padding = padding, groups = dim_in, stride = stride, bias = bias),
|
||||
nn.BatchNorm2d(dim_in),
|
||||
nn.Conv2d(dim_in, dim_out, kernel_size = 1, bias = bias)
|
||||
)
|
||||
def forward(self, x):
|
||||
return self.net(x)
|
||||
|
||||
class Attention(nn.Module):
|
||||
def __init__(self, dim, proj_kernel, kv_proj_stride, heads = 8, dim_head = 64, dropout = 0.):
|
||||
super().__init__()
|
||||
inner_dim = dim_head * heads
|
||||
padding = proj_kernel // 2
|
||||
self.heads = heads
|
||||
self.scale = dim_head ** -0.5
|
||||
|
||||
self.attend = nn.Softmax(dim = -1)
|
||||
|
||||
self.to_q = DepthWiseConv2d(dim, inner_dim, 3, padding = padding, stride = 1, bias = False)
|
||||
self.to_kv = DepthWiseConv2d(dim, inner_dim * 2, 3, padding = padding, stride = kv_proj_stride, bias = False)
|
||||
|
||||
self.to_out = nn.Sequential(
|
||||
nn.Conv2d(inner_dim, dim, 1),
|
||||
nn.Dropout(dropout)
|
||||
)
|
||||
|
||||
def forward(self, x):
|
||||
shape = x.shape
|
||||
b, n, _, y, h = *shape, self.heads
|
||||
q, k, v = (self.to_q(x), *self.to_kv(x).chunk(2, dim = 1))
|
||||
q, k, v = map(lambda t: rearrange(t, 'b (h d) x y -> (b h) (x y) d', h = h), (q, k, v))
|
||||
|
||||
dots = einsum('b i d, b j d -> b i j', q, k) * self.scale
|
||||
|
||||
attn = self.attend(dots)
|
||||
|
||||
out = einsum('b i j, b j d -> b i d', attn, v)
|
||||
out = rearrange(out, '(b h) (x y) d -> b (h d) x y', h = h, y = y)
|
||||
return self.to_out(out)
|
||||
|
||||
class Transformer(nn.Module):
|
||||
def __init__(self, dim, proj_kernel, kv_proj_stride, depth, heads, dim_head = 64, mlp_mult = 4, dropout = 0.):
|
||||
super().__init__()
|
||||
self.layers = nn.ModuleList([])
|
||||
for _ in range(depth):
|
||||
self.layers.append(nn.ModuleList([
|
||||
PreNorm(dim, Attention(dim, proj_kernel = proj_kernel, kv_proj_stride = kv_proj_stride, heads = heads, dim_head = dim_head, dropout = dropout)),
|
||||
PreNorm(dim, FeedForward(dim, mlp_mult, dropout = dropout))
|
||||
]))
|
||||
def forward(self, x):
|
||||
for attn, ff in self.layers:
|
||||
x = attn(x) + x
|
||||
x = ff(x) + x
|
||||
return x
|
||||
|
||||
class CvT(nn.Module):
|
||||
def __init__(
|
||||
self,
|
||||
*,
|
||||
num_classes,
|
||||
s1_emb_dim = 64,
|
||||
s1_emb_kernel = 7,
|
||||
s1_emb_stride = 4,
|
||||
s1_proj_kernel = 3,
|
||||
s1_kv_proj_stride = 2,
|
||||
s1_heads = 1,
|
||||
s1_depth = 1,
|
||||
s1_mlp_mult = 4,
|
||||
s2_emb_dim = 192,
|
||||
s2_emb_kernel = 3,
|
||||
s2_emb_stride = 2,
|
||||
s2_proj_kernel = 3,
|
||||
s2_kv_proj_stride = 2,
|
||||
s2_heads = 3,
|
||||
s2_depth = 2,
|
||||
s2_mlp_mult = 4,
|
||||
s3_emb_dim = 384,
|
||||
s3_emb_kernel = 3,
|
||||
s3_emb_stride = 2,
|
||||
s3_proj_kernel = 3,
|
||||
s3_kv_proj_stride = 2,
|
||||
s3_heads = 4,
|
||||
s3_depth = 10,
|
||||
s3_mlp_mult = 4,
|
||||
dropout = 0.
|
||||
):
|
||||
super().__init__()
|
||||
kwargs = dict(locals())
|
||||
|
||||
dim = 3
|
||||
layers = []
|
||||
|
||||
for prefix in ('s1', 's2', 's3'):
|
||||
config, kwargs = group_by_key_prefix_and_remove_prefix(f'{prefix}_', kwargs)
|
||||
|
||||
layers.append(nn.Sequential(
|
||||
nn.Conv2d(dim, config['emb_dim'], kernel_size = config['emb_kernel'], padding = (config['emb_kernel'] // 2), stride = config['emb_stride']),
|
||||
Transformer(dim = config['emb_dim'], proj_kernel = config['proj_kernel'], kv_proj_stride = config['kv_proj_stride'], depth = config['depth'], heads = config['heads'], mlp_mult = config['mlp_mult'], dropout = dropout)
|
||||
))
|
||||
|
||||
dim = config['emb_dim']
|
||||
|
||||
self.layers = nn.Sequential(
|
||||
*layers,
|
||||
nn.AdaptiveAvgPool2d(1),
|
||||
Rearrange('... () () -> ...'),
|
||||
nn.Linear(dim, num_classes)
|
||||
)
|
||||
|
||||
def forward(self, x):
|
||||
return self.layers(x)
|
||||
@@ -1,136 +0,0 @@
|
||||
import torch
|
||||
from torch import nn, einsum
|
||||
import torch.nn.functional as F
|
||||
|
||||
from einops import rearrange, repeat
|
||||
from einops.layers.torch import Rearrange
|
||||
|
||||
class Residual(nn.Module):
|
||||
def __init__(self, fn):
|
||||
super().__init__()
|
||||
self.fn = fn
|
||||
def forward(self, x, **kwargs):
|
||||
return self.fn(x, **kwargs) + x
|
||||
|
||||
class PreNorm(nn.Module):
|
||||
def __init__(self, dim, fn):
|
||||
super().__init__()
|
||||
self.norm = nn.LayerNorm(dim)
|
||||
self.fn = fn
|
||||
def forward(self, x, **kwargs):
|
||||
return self.fn(self.norm(x), **kwargs)
|
||||
|
||||
class FeedForward(nn.Module):
|
||||
def __init__(self, dim, hidden_dim, dropout = 0.):
|
||||
super().__init__()
|
||||
self.net = nn.Sequential(
|
||||
nn.Linear(dim, hidden_dim),
|
||||
nn.GELU(),
|
||||
nn.Dropout(dropout),
|
||||
nn.Linear(hidden_dim, dim),
|
||||
nn.Dropout(dropout)
|
||||
)
|
||||
def forward(self, x):
|
||||
return self.net(x)
|
||||
|
||||
class Attention(nn.Module):
|
||||
def __init__(self, dim, heads = 8, dim_head = 64, dropout = 0.):
|
||||
super().__init__()
|
||||
inner_dim = dim_head * heads
|
||||
self.heads = heads
|
||||
self.scale = dim_head ** -0.5
|
||||
|
||||
self.to_qkv = nn.Linear(dim, inner_dim * 3, bias = False)
|
||||
|
||||
self.reattn_weights = nn.Parameter(torch.randn(heads, heads))
|
||||
|
||||
self.reattn_norm = nn.Sequential(
|
||||
Rearrange('b h i j -> b i j h'),
|
||||
nn.LayerNorm(heads),
|
||||
Rearrange('b i j h -> b h i j')
|
||||
)
|
||||
|
||||
self.to_out = nn.Sequential(
|
||||
nn.Linear(inner_dim, dim),
|
||||
nn.Dropout(dropout)
|
||||
)
|
||||
|
||||
def forward(self, x):
|
||||
b, n, _, h = *x.shape, self.heads
|
||||
qkv = self.to_qkv(x).chunk(3, dim = -1)
|
||||
q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> b h n d', h = h), qkv)
|
||||
|
||||
# attention
|
||||
|
||||
dots = einsum('b h i d, b h j d -> b h i j', q, k) * self.scale
|
||||
attn = dots.softmax(dim=-1)
|
||||
|
||||
# re-attention
|
||||
|
||||
attn = einsum('b h i j, h g -> b g i j', attn, self.reattn_weights)
|
||||
attn = self.reattn_norm(attn)
|
||||
|
||||
# aggregate and out
|
||||
|
||||
out = einsum('b h i j, b h j d -> b h i d', attn, v)
|
||||
out = rearrange(out, 'b h n d -> b n (h d)')
|
||||
out = self.to_out(out)
|
||||
return out
|
||||
|
||||
class Transformer(nn.Module):
|
||||
def __init__(self, dim, depth, heads, dim_head, mlp_dim, dropout = 0.):
|
||||
super().__init__()
|
||||
self.layers = nn.ModuleList([])
|
||||
for _ in range(depth):
|
||||
self.layers.append(nn.ModuleList([
|
||||
Residual(PreNorm(dim, Attention(dim, heads = heads, dim_head = dim_head, dropout = dropout))),
|
||||
Residual(PreNorm(dim, FeedForward(dim, mlp_dim, dropout = dropout)))
|
||||
]))
|
||||
def forward(self, x):
|
||||
for attn, ff in self.layers:
|
||||
x = attn(x)
|
||||
x = ff(x)
|
||||
return x
|
||||
|
||||
class DeepViT(nn.Module):
|
||||
def __init__(self, *, image_size, patch_size, num_classes, dim, depth, heads, mlp_dim, pool = 'cls', channels = 3, dim_head = 64, dropout = 0., emb_dropout = 0.):
|
||||
super().__init__()
|
||||
assert image_size % patch_size == 0, 'Image dimensions must be divisible by the patch size.'
|
||||
num_patches = (image_size // patch_size) ** 2
|
||||
patch_dim = channels * patch_size ** 2
|
||||
assert pool in {'cls', 'mean'}, 'pool type must be either cls (cls token) or mean (mean pooling)'
|
||||
|
||||
self.to_patch_embedding = nn.Sequential(
|
||||
Rearrange('b c (h p1) (w p2) -> b (h w) (p1 p2 c)', p1 = patch_size, p2 = patch_size),
|
||||
nn.Linear(patch_dim, dim),
|
||||
)
|
||||
|
||||
self.pos_embedding = nn.Parameter(torch.randn(1, num_patches + 1, dim))
|
||||
self.cls_token = nn.Parameter(torch.randn(1, 1, dim))
|
||||
self.dropout = nn.Dropout(emb_dropout)
|
||||
|
||||
self.transformer = Transformer(dim, depth, heads, dim_head, mlp_dim, dropout)
|
||||
|
||||
self.pool = pool
|
||||
self.to_latent = nn.Identity()
|
||||
|
||||
self.mlp_head = nn.Sequential(
|
||||
nn.LayerNorm(dim),
|
||||
nn.Linear(dim, num_classes)
|
||||
)
|
||||
|
||||
def forward(self, img):
|
||||
x = self.to_patch_embedding(img)
|
||||
b, n, _ = x.shape
|
||||
|
||||
cls_tokens = repeat(self.cls_token, '() n d -> b n d', b = b)
|
||||
x = torch.cat((cls_tokens, x), dim=1)
|
||||
x += self.pos_embedding[:, :(n + 1)]
|
||||
x = self.dropout(x)
|
||||
|
||||
x = self.transformer(x)
|
||||
|
||||
x = x.mean(dim = 1) if self.pool == 'mean' else x[:, 0]
|
||||
|
||||
x = self.to_latent(x)
|
||||
return self.mlp_head(x)
|
||||
@@ -1,153 +0,0 @@
|
||||
import torch
|
||||
import torch.nn.functional as F
|
||||
from torch import nn
|
||||
from vit_pytorch.vit import ViT
|
||||
from vit_pytorch.t2t import T2TViT
|
||||
from vit_pytorch.efficient import ViT as EfficientViT
|
||||
|
||||
from einops import rearrange, repeat
|
||||
|
||||
# helpers
|
||||
|
||||
def exists(val):
|
||||
return val is not None
|
||||
|
||||
# classes
|
||||
|
||||
class DistillMixin:
|
||||
def forward(self, img, distill_token = None):
|
||||
distilling = exists(distill_token)
|
||||
x = self.to_patch_embedding(img)
|
||||
b, n, _ = x.shape
|
||||
|
||||
cls_tokens = repeat(self.cls_token, '() n d -> b n d', b = b)
|
||||
x = torch.cat((cls_tokens, x), dim = 1)
|
||||
x += self.pos_embedding[:, :(n + 1)]
|
||||
|
||||
if distilling:
|
||||
distill_tokens = repeat(distill_token, '() n d -> b n d', b = b)
|
||||
x = torch.cat((x, distill_tokens), dim = 1)
|
||||
|
||||
x = self._attend(x)
|
||||
|
||||
if distilling:
|
||||
x, distill_tokens = x[:, :-1], x[:, -1]
|
||||
|
||||
x = x.mean(dim = 1) if self.pool == 'mean' else x[:, 0]
|
||||
|
||||
x = self.to_latent(x)
|
||||
out = self.mlp_head(x)
|
||||
|
||||
if distilling:
|
||||
return out, distill_tokens
|
||||
|
||||
return out
|
||||
|
||||
class DistillableViT(DistillMixin, ViT):
|
||||
def __init__(self, *args, **kwargs):
|
||||
super(DistillableViT, self).__init__(*args, **kwargs)
|
||||
self.args = args
|
||||
self.kwargs = kwargs
|
||||
self.dim = kwargs['dim']
|
||||
self.num_classes = kwargs['num_classes']
|
||||
|
||||
def to_vit(self):
|
||||
v = ViT(*self.args, **self.kwargs)
|
||||
v.load_state_dict(self.state_dict())
|
||||
return v
|
||||
|
||||
def _attend(self, x):
|
||||
x = self.dropout(x)
|
||||
x = self.transformer(x)
|
||||
return x
|
||||
|
||||
class DistillableT2TViT(DistillMixin, T2TViT):
|
||||
def __init__(self, *args, **kwargs):
|
||||
super(DistillableT2TViT, self).__init__(*args, **kwargs)
|
||||
self.args = args
|
||||
self.kwargs = kwargs
|
||||
self.dim = kwargs['dim']
|
||||
self.num_classes = kwargs['num_classes']
|
||||
|
||||
def to_vit(self):
|
||||
v = T2TViT(*self.args, **self.kwargs)
|
||||
v.load_state_dict(self.state_dict())
|
||||
return v
|
||||
|
||||
def _attend(self, x):
|
||||
x = self.dropout(x)
|
||||
x = self.transformer(x)
|
||||
return x
|
||||
|
||||
class DistillableEfficientViT(DistillMixin, EfficientViT):
|
||||
def __init__(self, *args, **kwargs):
|
||||
super(DistillableEfficientViT, self).__init__(*args, **kwargs)
|
||||
self.args = args
|
||||
self.kwargs = kwargs
|
||||
self.dim = kwargs['dim']
|
||||
self.num_classes = kwargs['num_classes']
|
||||
|
||||
def to_vit(self):
|
||||
v = EfficientViT(*self.args, **self.kwargs)
|
||||
v.load_state_dict(self.state_dict())
|
||||
return v
|
||||
|
||||
def _attend(self, x):
|
||||
return self.transformer(x)
|
||||
|
||||
# knowledge distillation wrapper
|
||||
|
||||
class DistillWrapper(nn.Module):
|
||||
def __init__(
|
||||
self,
|
||||
*,
|
||||
teacher,
|
||||
student,
|
||||
temperature = 1.,
|
||||
alpha = 0.5,
|
||||
hard = False
|
||||
):
|
||||
super().__init__()
|
||||
assert (isinstance(student, (DistillableViT, DistillableT2TViT, DistillableEfficientViT))) , 'student must be a vision transformer'
|
||||
|
||||
self.teacher = teacher
|
||||
self.student = student
|
||||
|
||||
dim = student.dim
|
||||
num_classes = student.num_classes
|
||||
self.temperature = temperature
|
||||
self.alpha = alpha
|
||||
self.hard = hard
|
||||
|
||||
self.distillation_token = nn.Parameter(torch.randn(1, 1, dim))
|
||||
|
||||
self.distill_mlp = nn.Sequential(
|
||||
nn.LayerNorm(dim),
|
||||
nn.Linear(dim, num_classes)
|
||||
)
|
||||
|
||||
def forward(self, img, labels, temperature = None, alpha = None, **kwargs):
|
||||
b, *_ = img.shape
|
||||
alpha = alpha if exists(alpha) else self.alpha
|
||||
T = temperature if exists(temperature) else self.temperature
|
||||
|
||||
with torch.no_grad():
|
||||
teacher_logits = self.teacher(img)
|
||||
|
||||
student_logits, distill_tokens = self.student(img, distill_token = self.distillation_token, **kwargs)
|
||||
distill_logits = self.distill_mlp(distill_tokens)
|
||||
|
||||
loss = F.cross_entropy(student_logits, labels)
|
||||
|
||||
if not self.hard:
|
||||
distill_loss = F.kl_div(
|
||||
F.log_softmax(distill_logits / T, dim = -1),
|
||||
F.softmax(teacher_logits / T, dim = -1).detach(),
|
||||
reduction = 'batchmean')
|
||||
distill_loss *= T ** 2
|
||||
|
||||
else:
|
||||
teacher_labels = teacher_logits.argmax(dim = -1)
|
||||
distill_loss = F.cross_entropy(student_logits, teacher_labels)
|
||||
|
||||
return loss * alpha + distill_loss * (1 - alpha)
|
||||
@@ -1,43 +1,40 @@
|
||||
import torch
|
||||
from einops import rearrange
|
||||
from torch import nn
|
||||
from einops import rearrange, repeat
|
||||
from einops.layers.torch import Rearrange
|
||||
|
||||
class ViT(nn.Module):
|
||||
def __init__(self, *, image_size, patch_size, num_classes, dim, transformer, pool = 'cls', channels = 3):
|
||||
def __init__(self, *, image_size, patch_size, num_classes, dim, transformer, channels = 3):
|
||||
super().__init__()
|
||||
assert image_size % patch_size == 0, 'image dimensions must be divisible by the patch size'
|
||||
assert pool in {'cls', 'mean'}, 'pool type must be either cls (cls token) or mean (mean pooling)'
|
||||
num_patches = (image_size // patch_size) ** 2
|
||||
patch_dim = channels * patch_size ** 2
|
||||
|
||||
self.to_patch_embedding = nn.Sequential(
|
||||
Rearrange('b c (h p1) (w p2) -> b (h w) (p1 p2 c)', p1 = patch_size, p2 = patch_size),
|
||||
nn.Linear(patch_dim, dim),
|
||||
)
|
||||
self.patch_size = patch_size
|
||||
|
||||
self.pos_embedding = nn.Parameter(torch.randn(1, num_patches + 1, dim))
|
||||
self.patch_to_embedding = nn.Linear(patch_dim, dim)
|
||||
self.cls_token = nn.Parameter(torch.randn(1, 1, dim))
|
||||
self.transformer = transformer
|
||||
|
||||
self.pool = pool
|
||||
self.to_latent = nn.Identity()
|
||||
self.to_cls_token = nn.Identity()
|
||||
|
||||
self.mlp_head = nn.Sequential(
|
||||
nn.LayerNorm(dim),
|
||||
nn.Linear(dim, num_classes)
|
||||
nn.Linear(dim, dim * 4),
|
||||
nn.GELU(),
|
||||
nn.Linear(dim * 4, num_classes)
|
||||
)
|
||||
|
||||
def forward(self, img):
|
||||
x = self.to_patch_embedding(img)
|
||||
b, n, _ = x.shape
|
||||
p = self.patch_size
|
||||
|
||||
cls_tokens = repeat(self.cls_token, '() n d -> b n d', b = b)
|
||||
x = rearrange(img, 'b c (h p1) (w p2) -> b (h w) (p1 p2 c)', p1 = p, p2 = p)
|
||||
x = self.patch_to_embedding(x)
|
||||
|
||||
cls_tokens = self.cls_token.expand(img.shape[0], -1, -1)
|
||||
x = torch.cat((cls_tokens, x), dim=1)
|
||||
x += self.pos_embedding[:, :(n + 1)]
|
||||
x += self.pos_embedding
|
||||
x = self.transformer(x)
|
||||
|
||||
x = x.mean(dim = 1) if self.pool == 'mean' else x[:, 0]
|
||||
|
||||
x = self.to_latent(x)
|
||||
x = self.to_cls_token(x[:, 0])
|
||||
return self.mlp_head(x)
|
||||
|
||||
@@ -1,190 +0,0 @@
|
||||
from math import ceil
|
||||
|
||||
import torch
|
||||
from torch import nn, einsum
|
||||
import torch.nn.functional as F
|
||||
|
||||
from einops import rearrange, repeat
|
||||
from einops.layers.torch import Rearrange
|
||||
|
||||
# helpers
|
||||
|
||||
def exists(val):
|
||||
return val is not None
|
||||
|
||||
def default(val, d):
|
||||
return val if exists(val) else d
|
||||
|
||||
def cast_tuple(val, l = 3):
|
||||
val = val if isinstance(val, tuple) else (val,)
|
||||
return (*val, *((val[-1],) * max(l - len(val), 0)))
|
||||
|
||||
def always(val):
|
||||
return lambda *args, **kwargs: val
|
||||
|
||||
# classes
|
||||
|
||||
class FeedForward(nn.Module):
|
||||
def __init__(self, dim, mult, dropout = 0.):
|
||||
super().__init__()
|
||||
self.net = nn.Sequential(
|
||||
nn.Conv2d(dim, dim * mult, 1),
|
||||
nn.GELU(),
|
||||
nn.Dropout(dropout),
|
||||
nn.Conv2d(dim * mult, dim, 1),
|
||||
nn.Dropout(dropout)
|
||||
)
|
||||
def forward(self, x):
|
||||
return self.net(x)
|
||||
|
||||
class Attention(nn.Module):
|
||||
def __init__(self, dim, fmap_size, heads = 8, dim_key = 32, dim_value = 64, dropout = 0., dim_out = None, downsample = False):
|
||||
super().__init__()
|
||||
inner_dim_key = dim_key * heads
|
||||
inner_dim_value = dim_value * heads
|
||||
dim_out = default(dim_out, dim)
|
||||
|
||||
self.heads = heads
|
||||
self.scale = dim_key ** -0.5
|
||||
|
||||
self.to_q = nn.Sequential(nn.Conv2d(dim, inner_dim_key, 1, stride = (2 if downsample else 1), bias = False), nn.BatchNorm2d(inner_dim_key))
|
||||
self.to_k = nn.Sequential(nn.Conv2d(dim, inner_dim_key, 1, bias = False), nn.BatchNorm2d(inner_dim_key))
|
||||
self.to_v = nn.Sequential(nn.Conv2d(dim, inner_dim_value, 1, bias = False), nn.BatchNorm2d(inner_dim_value))
|
||||
|
||||
self.attend = nn.Softmax(dim = -1)
|
||||
|
||||
self.to_out = nn.Sequential(
|
||||
nn.GELU(),
|
||||
nn.Conv2d(inner_dim_value, dim_out, 1),
|
||||
nn.BatchNorm2d(dim_out),
|
||||
nn.Dropout(dropout)
|
||||
)
|
||||
|
||||
# positional bias
|
||||
|
||||
self.pos_bias = nn.Embedding(fmap_size * fmap_size, heads)
|
||||
|
||||
q_range = torch.arange(0, fmap_size, step = (2 if downsample else 1))
|
||||
k_range = torch.arange(fmap_size)
|
||||
|
||||
q_pos = torch.stack(torch.meshgrid(q_range, q_range), dim = -1)
|
||||
k_pos = torch.stack(torch.meshgrid(k_range, k_range), dim = -1)
|
||||
|
||||
q_pos, k_pos = map(lambda t: rearrange(t, 'i j c -> (i j) c'), (q_pos, k_pos))
|
||||
rel_pos = (q_pos[:, None, ...] - k_pos[None, :, ...]).abs()
|
||||
|
||||
x_rel, y_rel = rel_pos.unbind(dim = -1)
|
||||
pos_indices = (x_rel * fmap_size) + y_rel
|
||||
|
||||
self.register_buffer('pos_indices', pos_indices)
|
||||
|
||||
def apply_pos_bias(self, fmap):
|
||||
bias = self.pos_bias(self.pos_indices)
|
||||
bias = rearrange(bias, 'i j h -> () h i j')
|
||||
return fmap + bias
|
||||
|
||||
def forward(self, x):
|
||||
b, n, *_, h = *x.shape, self.heads
|
||||
|
||||
q = self.to_q(x)
|
||||
y = q.shape[2]
|
||||
|
||||
qkv = (q, self.to_k(x), self.to_v(x))
|
||||
q, k, v = map(lambda t: rearrange(t, 'b (h d) ... -> b h (...) d', h = h), qkv)
|
||||
|
||||
dots = einsum('b h i d, b h j d -> b h i j', q, k) * self.scale
|
||||
|
||||
dots = self.apply_pos_bias(dots)
|
||||
|
||||
attn = self.attend(dots)
|
||||
|
||||
out = einsum('b h i j, b h j d -> b h i d', attn, v)
|
||||
out = rearrange(out, 'b h (x y) d -> b (h d) x y', h = h, y = y)
|
||||
return self.to_out(out)
|
||||
|
||||
class Transformer(nn.Module):
|
||||
def __init__(self, dim, fmap_size, depth, heads, dim_key, dim_value, mlp_mult = 2, dropout = 0., dim_out = None, downsample = False):
|
||||
super().__init__()
|
||||
dim_out = default(dim_out, dim)
|
||||
self.layers = nn.ModuleList([])
|
||||
self.attn_residual = (not downsample) and dim == dim_out
|
||||
|
||||
for _ in range(depth):
|
||||
self.layers.append(nn.ModuleList([
|
||||
Attention(dim, fmap_size = fmap_size, heads = heads, dim_key = dim_key, dim_value = dim_value, dropout = dropout, downsample = downsample, dim_out = dim_out),
|
||||
FeedForward(dim_out, mlp_mult, dropout = dropout)
|
||||
]))
|
||||
def forward(self, x):
|
||||
for attn, ff in self.layers:
|
||||
attn_res = (x if self.attn_residual else 0)
|
||||
x = attn(x) + attn_res
|
||||
x = ff(x) + x
|
||||
return x
|
||||
|
||||
class LeViT(nn.Module):
|
||||
def __init__(
|
||||
self,
|
||||
*,
|
||||
image_size,
|
||||
num_classes,
|
||||
dim,
|
||||
depth,
|
||||
heads,
|
||||
mlp_mult,
|
||||
stages = 3,
|
||||
dim_key = 32,
|
||||
dim_value = 64,
|
||||
dropout = 0.,
|
||||
num_distill_classes = None
|
||||
):
|
||||
super().__init__()
|
||||
|
||||
dims = cast_tuple(dim, stages)
|
||||
depths = cast_tuple(depth, stages)
|
||||
layer_heads = cast_tuple(heads, stages)
|
||||
|
||||
assert all(map(lambda t: len(t) == stages, (dims, depths, layer_heads))), 'dimensions, depths, and heads must be a tuple that is less than the designated number of stages'
|
||||
|
||||
self.conv_embedding = nn.Sequential(
|
||||
nn.Conv2d(3, 32, 3, stride = 2, padding = 1),
|
||||
nn.Conv2d(32, 64, 3, stride = 2, padding = 1),
|
||||
nn.Conv2d(64, 128, 3, stride = 2, padding = 1),
|
||||
nn.Conv2d(128, dims[0], 3, stride = 2, padding = 1)
|
||||
)
|
||||
|
||||
fmap_size = image_size // (2 ** 4)
|
||||
layers = []
|
||||
|
||||
for ind, dim, depth, heads in zip(range(stages), dims, depths, layer_heads):
|
||||
is_last = ind == (stages - 1)
|
||||
layers.append(Transformer(dim, fmap_size, depth, heads, dim_key, dim_value, mlp_mult, dropout))
|
||||
|
||||
if not is_last:
|
||||
next_dim = dims[ind + 1]
|
||||
layers.append(Transformer(dim, fmap_size, 1, heads * 2, dim_key, dim_value, dim_out = next_dim, downsample = True))
|
||||
fmap_size = ceil(fmap_size / 2)
|
||||
|
||||
self.backbone = nn.Sequential(*layers)
|
||||
|
||||
self.pool = nn.Sequential(
|
||||
nn.AdaptiveAvgPool2d(1),
|
||||
Rearrange('... () () -> ...')
|
||||
)
|
||||
|
||||
self.distill_head = nn.Linear(dim, num_distill_classes) if exists(num_distill_classes) else always(None)
|
||||
self.mlp_head = nn.Linear(dim, num_classes)
|
||||
|
||||
def forward(self, img):
|
||||
x = self.conv_embedding(img)
|
||||
|
||||
x = self.backbone(x)
|
||||
|
||||
x = self.pool(x)
|
||||
|
||||
out = self.mlp_head(x)
|
||||
distill = self.distill_head(x)
|
||||
|
||||
if exists(distill):
|
||||
return out, distill
|
||||
|
||||
return out
|
||||
@@ -1,152 +0,0 @@
|
||||
from math import sqrt
|
||||
import torch
|
||||
from torch import nn, einsum
|
||||
import torch.nn.functional as F
|
||||
|
||||
from einops import rearrange, repeat
|
||||
from einops.layers.torch import Rearrange
|
||||
|
||||
# classes
|
||||
|
||||
class Residual(nn.Module):
|
||||
def __init__(self, fn):
|
||||
super().__init__()
|
||||
self.fn = fn
|
||||
|
||||
def forward(self, x, **kwargs):
|
||||
return self.fn(x, **kwargs) + x
|
||||
|
||||
class ExcludeCLS(nn.Module):
|
||||
def __init__(self, fn):
|
||||
super().__init__()
|
||||
self.fn = fn
|
||||
|
||||
def forward(self, x, **kwargs):
|
||||
cls_token, x = x[:, :1], x[:, 1:]
|
||||
x = self.fn(x, **kwargs)
|
||||
return torch.cat((cls_token, x), dim = 1)
|
||||
|
||||
# prenorm
|
||||
|
||||
class PreNorm(nn.Module):
|
||||
def __init__(self, dim, fn):
|
||||
super().__init__()
|
||||
self.norm = nn.LayerNorm(dim)
|
||||
self.fn = fn
|
||||
def forward(self, x, **kwargs):
|
||||
return self.fn(self.norm(x), **kwargs)
|
||||
|
||||
# feed forward related classes
|
||||
|
||||
class DepthWiseConv2d(nn.Module):
|
||||
def __init__(self, dim_in, dim_out, kernel_size, padding, stride = 1, bias = True):
|
||||
super().__init__()
|
||||
self.net = nn.Sequential(
|
||||
nn.Conv2d(dim_in, dim_in, kernel_size = kernel_size, padding = padding, groups = dim_in, stride = stride, bias = bias),
|
||||
nn.Conv2d(dim_in, dim_out, kernel_size = 1, bias = bias)
|
||||
)
|
||||
def forward(self, x):
|
||||
return self.net(x)
|
||||
|
||||
class FeedForward(nn.Module):
|
||||
def __init__(self, dim, hidden_dim, dropout = 0.):
|
||||
super().__init__()
|
||||
self.net = nn.Sequential(
|
||||
nn.Conv2d(dim, hidden_dim, 1),
|
||||
nn.Hardswish(),
|
||||
DepthWiseConv2d(hidden_dim, hidden_dim, 3, padding = 1),
|
||||
nn.Hardswish(),
|
||||
nn.Dropout(dropout),
|
||||
nn.Conv2d(hidden_dim, dim, 1),
|
||||
nn.Dropout(dropout)
|
||||
)
|
||||
def forward(self, x):
|
||||
h = w = int(sqrt(x.shape[-2]))
|
||||
x = rearrange(x, 'b (h w) c -> b c h w', h = h, w = w)
|
||||
x = self.net(x)
|
||||
x = rearrange(x, 'b c h w -> b (h w) c')
|
||||
return x
|
||||
|
||||
# attention
|
||||
|
||||
class Attention(nn.Module):
|
||||
def __init__(self, dim, heads = 8, dim_head = 64, dropout = 0.):
|
||||
super().__init__()
|
||||
inner_dim = dim_head * heads
|
||||
|
||||
self.heads = heads
|
||||
self.scale = dim_head ** -0.5
|
||||
|
||||
self.attend = nn.Softmax(dim = -1)
|
||||
self.to_qkv = nn.Linear(dim, inner_dim * 3, bias = False)
|
||||
|
||||
self.to_out = nn.Sequential(
|
||||
nn.Linear(inner_dim, dim),
|
||||
nn.Dropout(dropout)
|
||||
)
|
||||
|
||||
def forward(self, x):
|
||||
b, n, _, h = *x.shape, self.heads
|
||||
qkv = self.to_qkv(x).chunk(3, dim = -1)
|
||||
q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> b h n d', h = h), qkv)
|
||||
|
||||
dots = einsum('b h i d, b h j d -> b h i j', q, k) * self.scale
|
||||
|
||||
attn = self.attend(dots)
|
||||
|
||||
out = einsum('b h i j, b h j d -> b h i d', attn, v)
|
||||
out = rearrange(out, 'b h n d -> b n (h d)')
|
||||
return self.to_out(out)
|
||||
|
||||
class Transformer(nn.Module):
|
||||
def __init__(self, dim, depth, heads, dim_head, mlp_dim, dropout = 0.):
|
||||
super().__init__()
|
||||
self.layers = nn.ModuleList([])
|
||||
for _ in range(depth):
|
||||
self.layers.append(nn.ModuleList([
|
||||
Residual(PreNorm(dim, Attention(dim, heads = heads, dim_head = dim_head, dropout = dropout))),
|
||||
ExcludeCLS(Residual(PreNorm(dim, FeedForward(dim, mlp_dim, dropout = dropout))))
|
||||
]))
|
||||
def forward(self, x):
|
||||
for attn, ff in self.layers:
|
||||
x = attn(x)
|
||||
x = ff(x)
|
||||
return x
|
||||
|
||||
# main class
|
||||
|
||||
class LocalViT(nn.Module):
|
||||
def __init__(self, *, image_size, patch_size, num_classes, dim, depth, heads, mlp_dim, channels = 3, dim_head = 64, dropout = 0., emb_dropout = 0.):
|
||||
super().__init__()
|
||||
assert image_size % patch_size == 0, 'Image dimensions must be divisible by the patch size.'
|
||||
num_patches = (image_size // patch_size) ** 2
|
||||
patch_dim = channels * patch_size ** 2
|
||||
|
||||
self.to_patch_embedding = nn.Sequential(
|
||||
Rearrange('b c (h p1) (w p2) -> b (h w) (p1 p2 c)', p1 = patch_size, p2 = patch_size),
|
||||
nn.Linear(patch_dim, dim),
|
||||
)
|
||||
|
||||
self.pos_embedding = nn.Parameter(torch.randn(1, num_patches + 1, dim))
|
||||
self.cls_token = nn.Parameter(torch.randn(1, 1, dim))
|
||||
self.dropout = nn.Dropout(emb_dropout)
|
||||
|
||||
self.transformer = Transformer(dim, depth, heads, dim_head, mlp_dim, dropout)
|
||||
|
||||
self.mlp_head = nn.Sequential(
|
||||
nn.LayerNorm(dim),
|
||||
nn.Linear(dim, num_classes)
|
||||
)
|
||||
|
||||
def forward(self, img):
|
||||
x = self.to_patch_embedding(img)
|
||||
b, n, _ = x.shape
|
||||
|
||||
cls_tokens = repeat(self.cls_token, '() n d -> b n d', b = b)
|
||||
x = torch.cat((cls_tokens, x), dim=1)
|
||||
x += self.pos_embedding[:, :(n + 1)]
|
||||
x = self.dropout(x)
|
||||
|
||||
x = self.transformer(x)
|
||||
|
||||
return self.mlp_head(x)
|
||||
@@ -1,166 +0,0 @@
|
||||
import math
|
||||
from functools import reduce
|
||||
|
||||
import torch
|
||||
from torch import nn
|
||||
import torch.nn.functional as F
|
||||
|
||||
from einops import rearrange, repeat
|
||||
|
||||
# helpers
|
||||
|
||||
|
||||
def prob_mask_like(t, prob):
|
||||
batch, seq_length, _ = t.shape
|
||||
return torch.zeros((batch, seq_length)).float().uniform_(0, 1) < prob
|
||||
|
||||
|
||||
def get_mask_subset_with_prob(patched_input, prob):
|
||||
batch, seq_len, _, device = *patched_input.shape, patched_input.device
|
||||
max_masked = math.ceil(prob * seq_len)
|
||||
|
||||
rand = torch.rand((batch, seq_len), device=device)
|
||||
_, sampled_indices = rand.topk(max_masked, dim=-1)
|
||||
|
||||
new_mask = torch.zeros((batch, seq_len), device=device)
|
||||
new_mask.scatter_(1, sampled_indices, 1)
|
||||
return new_mask.bool()
|
||||
|
||||
|
||||
# mpp loss
|
||||
|
||||
|
||||
class MPPLoss(nn.Module):
|
||||
def __init__(self, patch_size, channels, output_channel_bits,
|
||||
max_pixel_val):
|
||||
super(MPPLoss, self).__init__()
|
||||
self.patch_size = patch_size
|
||||
self.channels = channels
|
||||
self.output_channel_bits = output_channel_bits
|
||||
self.max_pixel_val = max_pixel_val
|
||||
|
||||
def forward(self, predicted_patches, target, mask):
|
||||
# reshape target to patches
|
||||
p = self.patch_size
|
||||
target = rearrange(target,
|
||||
"b c (h p1) (w p2) -> b (h w) c (p1 p2) ",
|
||||
p1=p,
|
||||
p2=p)
|
||||
|
||||
avg_target = target.mean(dim=3)
|
||||
|
||||
bin_size = self.max_pixel_val / self.output_channel_bits
|
||||
channel_bins = torch.arange(bin_size, self.max_pixel_val, bin_size)
|
||||
discretized_target = torch.bucketize(avg_target, channel_bins)
|
||||
discretized_target = F.one_hot(discretized_target,
|
||||
self.output_channel_bits)
|
||||
c, bi = self.channels, self.output_channel_bits
|
||||
discretized_target = rearrange(discretized_target,
|
||||
"b n c bi -> b n (c bi)",
|
||||
c=c,
|
||||
bi=bi)
|
||||
|
||||
bin_mask = 2**torch.arange(c * bi - 1, -1,
|
||||
-1).to(discretized_target.device,
|
||||
discretized_target.dtype)
|
||||
target_label = torch.sum(bin_mask * discretized_target, -1)
|
||||
|
||||
predicted_patches = predicted_patches[mask]
|
||||
target_label = target_label[mask]
|
||||
loss = F.cross_entropy(predicted_patches, target_label)
|
||||
return loss
|
||||
|
||||
|
||||
# main class
|
||||
|
||||
|
||||
class MPP(nn.Module):
|
||||
def __init__(self,
|
||||
transformer,
|
||||
patch_size,
|
||||
dim,
|
||||
output_channel_bits=3,
|
||||
channels=3,
|
||||
max_pixel_val=1.0,
|
||||
mask_prob=0.15,
|
||||
replace_prob=0.5,
|
||||
random_patch_prob=0.5):
|
||||
super().__init__()
|
||||
|
||||
self.transformer = transformer
|
||||
self.loss = MPPLoss(patch_size, channels, output_channel_bits,
|
||||
max_pixel_val)
|
||||
|
||||
# output transformation
|
||||
self.to_bits = nn.Linear(dim, 2**(output_channel_bits * channels))
|
||||
|
||||
# vit related dimensions
|
||||
self.patch_size = patch_size
|
||||
|
||||
# mpp related probabilities
|
||||
self.mask_prob = mask_prob
|
||||
self.replace_prob = replace_prob
|
||||
self.random_patch_prob = random_patch_prob
|
||||
|
||||
# token ids
|
||||
self.mask_token = nn.Parameter(torch.randn(1, 1, dim * channels))
|
||||
|
||||
def forward(self, input, **kwargs):
|
||||
transformer = self.transformer
|
||||
# clone original image for loss
|
||||
img = input.clone().detach()
|
||||
|
||||
# reshape raw image to patches
|
||||
p = self.patch_size
|
||||
input = rearrange(input,
|
||||
'b c (h p1) (w p2) -> b (h w) (p1 p2 c)',
|
||||
p1=p,
|
||||
p2=p)
|
||||
|
||||
mask = get_mask_subset_with_prob(input, self.mask_prob)
|
||||
|
||||
# mask input with mask patches with probability of `replace_prob` (keep patches the same with probability 1 - replace_prob)
|
||||
masked_input = input.clone().detach()
|
||||
|
||||
# if random token probability > 0 for mpp
|
||||
if self.random_patch_prob > 0:
|
||||
random_patch_sampling_prob = self.random_patch_prob / (
|
||||
1 - self.replace_prob)
|
||||
random_patch_prob = prob_mask_like(input,
|
||||
random_patch_sampling_prob)
|
||||
bool_random_patch_prob = mask * random_patch_prob == True
|
||||
random_patches = torch.randint(0,
|
||||
input.shape[1],
|
||||
(input.shape[0], input.shape[1]),
|
||||
device=input.device)
|
||||
randomized_input = masked_input[
|
||||
torch.arange(masked_input.shape[0]).unsqueeze(-1),
|
||||
random_patches]
|
||||
masked_input[bool_random_patch_prob] = randomized_input[
|
||||
bool_random_patch_prob]
|
||||
|
||||
# [mask] input
|
||||
replace_prob = prob_mask_like(input, self.replace_prob)
|
||||
bool_mask_replace = (mask * replace_prob) == True
|
||||
masked_input[bool_mask_replace] = self.mask_token
|
||||
|
||||
# linear embedding of patches
|
||||
masked_input = transformer.to_patch_embedding[-1](masked_input)
|
||||
|
||||
# add cls token to input sequence
|
||||
b, n, _ = masked_input.shape
|
||||
cls_tokens = repeat(transformer.cls_token, '() n d -> b n d', b=b)
|
||||
masked_input = torch.cat((cls_tokens, masked_input), dim=1)
|
||||
|
||||
# add positional embeddings to input
|
||||
masked_input += transformer.pos_embedding[:, :(n + 1)]
|
||||
masked_input = transformer.dropout(masked_input)
|
||||
|
||||
# get generator output and get mpp loss
|
||||
masked_input = transformer.transformer(masked_input, **kwargs)
|
||||
cls_logits = self.to_bits(masked_input)
|
||||
logits = cls_logits[:, 1:, :]
|
||||
|
||||
mpp_loss = self.loss(logits, img, mask)
|
||||
|
||||
return mpp_loss
|
||||
@@ -1,180 +0,0 @@
|
||||
from math import sqrt
|
||||
|
||||
import torch
|
||||
from torch import nn, einsum
|
||||
import torch.nn.functional as F
|
||||
|
||||
from einops import rearrange, repeat
|
||||
from einops.layers.torch import Rearrange
|
||||
|
||||
# helpers
|
||||
|
||||
def cast_tuple(val, num):
|
||||
return val if isinstance(val, tuple) else (val,) * num
|
||||
|
||||
def conv_output_size(image_size, kernel_size, stride, padding = 0):
|
||||
return int(((image_size - kernel_size + (2 * padding)) / stride) + 1)
|
||||
|
||||
# classes
|
||||
|
||||
class PreNorm(nn.Module):
|
||||
def __init__(self, dim, fn):
|
||||
super().__init__()
|
||||
self.norm = nn.LayerNorm(dim)
|
||||
self.fn = fn
|
||||
def forward(self, x, **kwargs):
|
||||
return self.fn(self.norm(x), **kwargs)
|
||||
|
||||
class FeedForward(nn.Module):
|
||||
def __init__(self, dim, hidden_dim, dropout = 0.):
|
||||
super().__init__()
|
||||
self.net = nn.Sequential(
|
||||
nn.Linear(dim, hidden_dim),
|
||||
nn.GELU(),
|
||||
nn.Dropout(dropout),
|
||||
nn.Linear(hidden_dim, dim),
|
||||
nn.Dropout(dropout)
|
||||
)
|
||||
def forward(self, x):
|
||||
return self.net(x)
|
||||
|
||||
class Attention(nn.Module):
|
||||
def __init__(self, dim, heads = 8, dim_head = 64, dropout = 0.):
|
||||
super().__init__()
|
||||
inner_dim = dim_head * heads
|
||||
project_out = not (heads == 1 and dim_head == dim)
|
||||
|
||||
self.heads = heads
|
||||
self.scale = dim_head ** -0.5
|
||||
|
||||
self.attend = nn.Softmax(dim = -1)
|
||||
self.to_qkv = nn.Linear(dim, inner_dim * 3, bias = False)
|
||||
|
||||
self.to_out = nn.Sequential(
|
||||
nn.Linear(inner_dim, dim),
|
||||
nn.Dropout(dropout)
|
||||
) if project_out else nn.Identity()
|
||||
|
||||
def forward(self, x):
|
||||
b, n, _, h = *x.shape, self.heads
|
||||
qkv = self.to_qkv(x).chunk(3, dim = -1)
|
||||
q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> b h n d', h = h), qkv)
|
||||
|
||||
dots = einsum('b h i d, b h j d -> b h i j', q, k) * self.scale
|
||||
|
||||
attn = self.attend(dots)
|
||||
|
||||
out = einsum('b h i j, b h j d -> b h i d', attn, v)
|
||||
out = rearrange(out, 'b h n d -> b n (h d)')
|
||||
return self.to_out(out)
|
||||
|
||||
class Transformer(nn.Module):
|
||||
def __init__(self, dim, depth, heads, dim_head, mlp_dim, dropout = 0.):
|
||||
super().__init__()
|
||||
self.layers = nn.ModuleList([])
|
||||
for _ in range(depth):
|
||||
self.layers.append(nn.ModuleList([
|
||||
PreNorm(dim, Attention(dim, heads = heads, dim_head = dim_head, dropout = dropout)),
|
||||
PreNorm(dim, FeedForward(dim, mlp_dim, dropout = dropout))
|
||||
]))
|
||||
def forward(self, x):
|
||||
for attn, ff in self.layers:
|
||||
x = attn(x) + x
|
||||
x = ff(x) + x
|
||||
return x
|
||||
|
||||
# depthwise convolution, for pooling
|
||||
|
||||
class DepthWiseConv2d(nn.Module):
|
||||
def __init__(self, dim_in, dim_out, kernel_size, padding, stride, bias = True):
|
||||
super().__init__()
|
||||
self.net = nn.Sequential(
|
||||
nn.Conv2d(dim_in, dim_in, kernel_size = kernel_size, padding = padding, groups = dim_in, stride = stride, bias = bias),
|
||||
nn.Conv2d(dim_in, dim_out, kernel_size = 1, bias = bias)
|
||||
)
|
||||
def forward(self, x):
|
||||
return self.net(x)
|
||||
|
||||
# pooling layer
|
||||
|
||||
class Pool(nn.Module):
|
||||
def __init__(self, dim):
|
||||
super().__init__()
|
||||
self.downsample = DepthWiseConv2d(dim, dim * 2, kernel_size = 3, stride = 2, padding = 1)
|
||||
self.cls_ff = nn.Linear(dim, dim * 2)
|
||||
|
||||
def forward(self, x):
|
||||
cls_token, tokens = x[:, :1], x[:, 1:]
|
||||
|
||||
cls_token = self.cls_ff(cls_token)
|
||||
|
||||
tokens = rearrange(tokens, 'b (h w) c -> b c h w', h = int(sqrt(tokens.shape[1])))
|
||||
tokens = self.downsample(tokens)
|
||||
tokens = rearrange(tokens, 'b c h w -> b (h w) c')
|
||||
|
||||
return torch.cat((cls_token, tokens), dim = 1)
|
||||
|
||||
# main class
|
||||
|
||||
class PiT(nn.Module):
|
||||
def __init__(
|
||||
self,
|
||||
*,
|
||||
image_size,
|
||||
patch_size,
|
||||
num_classes,
|
||||
dim,
|
||||
depth,
|
||||
heads,
|
||||
mlp_dim,
|
||||
dim_head = 64,
|
||||
dropout = 0.,
|
||||
emb_dropout = 0.
|
||||
):
|
||||
super().__init__()
|
||||
assert image_size % patch_size == 0, 'Image dimensions must be divisible by the patch size.'
|
||||
assert isinstance(depth, tuple), 'depth must be a tuple of integers, specifying the number of blocks before each downsizing'
|
||||
heads = cast_tuple(heads, len(depth))
|
||||
|
||||
patch_dim = 3 * patch_size ** 2
|
||||
|
||||
self.to_patch_embedding = nn.Sequential(
|
||||
nn.Unfold(kernel_size = patch_size, stride = patch_size // 2),
|
||||
Rearrange('b c n -> b n c'),
|
||||
nn.Linear(patch_dim, dim)
|
||||
)
|
||||
|
||||
output_size = conv_output_size(image_size, patch_size, patch_size // 2)
|
||||
num_patches = output_size ** 2
|
||||
|
||||
self.pos_embedding = nn.Parameter(torch.randn(1, num_patches + 1, dim))
|
||||
self.cls_token = nn.Parameter(torch.randn(1, 1, dim))
|
||||
self.dropout = nn.Dropout(emb_dropout)
|
||||
|
||||
layers = []
|
||||
|
||||
for ind, (layer_depth, layer_heads) in enumerate(zip(depth, heads)):
|
||||
not_last = ind < (len(depth) - 1)
|
||||
|
||||
layers.append(Transformer(dim, layer_depth, layer_heads, dim_head, mlp_dim, dropout))
|
||||
|
||||
if not_last:
|
||||
layers.append(Pool(dim))
|
||||
dim *= 2
|
||||
|
||||
self.layers = nn.Sequential(
|
||||
*layers,
|
||||
nn.LayerNorm(dim),
|
||||
nn.Linear(dim, num_classes)
|
||||
)
|
||||
|
||||
def forward(self, img):
|
||||
x = self.to_patch_embedding(img)
|
||||
b, n, _ = x.shape
|
||||
|
||||
cls_tokens = repeat(self.cls_token, '() n d -> b n d', b = b)
|
||||
x = torch.cat((cls_tokens, x), dim=1)
|
||||
x += self.pos_embedding
|
||||
x = self.dropout(x)
|
||||
|
||||
return self.layers(x)
|
||||
@@ -1,54 +0,0 @@
|
||||
from functools import wraps
|
||||
import torch
|
||||
from torch import nn
|
||||
|
||||
from vit_pytorch.vit import Attention
|
||||
|
||||
def find_modules(nn_module, type):
|
||||
return [module for module in nn_module.modules() if isinstance(module, type)]
|
||||
|
||||
class Recorder(nn.Module):
|
||||
def __init__(self, vit):
|
||||
super().__init__()
|
||||
self.vit = vit
|
||||
|
||||
self.data = None
|
||||
self.recordings = []
|
||||
self.hooks = []
|
||||
self.hook_registered = False
|
||||
self.ejected = False
|
||||
|
||||
def _hook(self, _, input, output):
|
||||
self.recordings.append(output.clone().detach())
|
||||
|
||||
def _register_hook(self):
|
||||
modules = find_modules(self.vit.transformer, Attention)
|
||||
for module in modules:
|
||||
handle = module.attend.register_forward_hook(self._hook)
|
||||
self.hooks.append(handle)
|
||||
self.hook_registered = True
|
||||
|
||||
def eject(self):
|
||||
self.ejected = True
|
||||
for hook in self.hooks:
|
||||
hook.remove()
|
||||
self.hooks.clear()
|
||||
return self.vit
|
||||
|
||||
def clear(self):
|
||||
self.recordings.clear()
|
||||
|
||||
def record(self, attn):
|
||||
recording = attn.clone().detach()
|
||||
self.recordings.append(recording)
|
||||
|
||||
def forward(self, img):
|
||||
assert not self.ejected, 'recorder has been ejected, cannot be used anymore'
|
||||
self.clear()
|
||||
|
||||
if not self.hook_registered:
|
||||
self._register_hook()
|
||||
|
||||
pred = self.vit(img)
|
||||
attns = torch.stack(self.recordings, dim = 1)
|
||||
return pred, attns
|
||||
@@ -1,163 +0,0 @@
|
||||
from math import sqrt, pi, log
|
||||
|
||||
import torch
|
||||
from torch import nn, einsum
|
||||
import torch.nn.functional as F
|
||||
|
||||
from einops import rearrange, repeat
|
||||
from einops.layers.torch import Rearrange
|
||||
|
||||
# rotary embeddings
|
||||
|
||||
def rotate_every_two(x):
|
||||
x = rearrange(x, '... (d j) -> ... d j', j = 2)
|
||||
x1, x2 = x.unbind(dim = -1)
|
||||
x = torch.stack((-x2, x1), dim = -1)
|
||||
return rearrange(x, '... d j -> ... (d j)')
|
||||
|
||||
class AxialRotaryEmbedding(nn.Module):
|
||||
def __init__(self, dim, max_freq = 10):
|
||||
super().__init__()
|
||||
self.dim = dim
|
||||
scales = torch.logspace(1., log(max_freq / 2) / log(2), self.dim // 4, base = 2)
|
||||
self.register_buffer('scales', scales)
|
||||
|
||||
def forward(self, x):
|
||||
device, dtype, n = x.device, x.dtype, int(sqrt(x.shape[-2]))
|
||||
|
||||
seq = torch.linspace(-1., 1., steps = n, device = device)
|
||||
seq = seq.unsqueeze(-1)
|
||||
|
||||
scales = self.scales[(*((None,) * (len(seq.shape) - 1)), Ellipsis)]
|
||||
scales = scales.to(x)
|
||||
|
||||
seq = seq * scales * pi
|
||||
|
||||
x_sinu = repeat(seq, 'i d -> i j d', j = n)
|
||||
y_sinu = repeat(seq, 'j d -> i j d', i = n)
|
||||
|
||||
sin = torch.cat((x_sinu.sin(), y_sinu.sin()), dim = -1)
|
||||
cos = torch.cat((x_sinu.cos(), y_sinu.cos()), dim = -1)
|
||||
|
||||
sin, cos = map(lambda t: rearrange(t, 'i j d -> (i j) d'), (sin, cos))
|
||||
sin, cos = map(lambda t: repeat(t, 'n d -> () n (d j)', j = 2), (sin, cos))
|
||||
return sin, cos
|
||||
|
||||
# helper classes
|
||||
|
||||
class PreNorm(nn.Module):
|
||||
def __init__(self, dim, fn):
|
||||
super().__init__()
|
||||
self.norm = nn.LayerNorm(dim)
|
||||
self.fn = fn
|
||||
def forward(self, x, **kwargs):
|
||||
return self.fn(self.norm(x), **kwargs)
|
||||
|
||||
class GEGLU(nn.Module):
|
||||
def forward(self, x):
|
||||
x, gates = x.chunk(2, dim = -1)
|
||||
return F.gelu(gates) * x
|
||||
|
||||
class FeedForward(nn.Module):
|
||||
def __init__(self, dim, hidden_dim, dropout = 0.):
|
||||
super().__init__()
|
||||
self.net = nn.Sequential(
|
||||
nn.Linear(dim, hidden_dim * 2),
|
||||
GEGLU(),
|
||||
nn.Dropout(dropout),
|
||||
nn.Linear(hidden_dim, dim),
|
||||
nn.Dropout(dropout)
|
||||
)
|
||||
def forward(self, x):
|
||||
return self.net(x)
|
||||
|
||||
class Attention(nn.Module):
|
||||
def __init__(self, dim, heads = 8, dim_head = 64, dropout = 0.):
|
||||
super().__init__()
|
||||
inner_dim = dim_head * heads
|
||||
|
||||
self.heads = heads
|
||||
self.scale = dim_head ** -0.5
|
||||
|
||||
self.attend = nn.Softmax(dim = -1)
|
||||
self.to_qkv = nn.Linear(dim, inner_dim * 3, bias = False)
|
||||
|
||||
self.to_out = nn.Sequential(
|
||||
nn.Linear(inner_dim, dim),
|
||||
nn.Dropout(dropout)
|
||||
)
|
||||
|
||||
def forward(self, x, pos_emb):
|
||||
b, n, _, h = *x.shape, self.heads
|
||||
qkv = self.to_qkv(x).chunk(3, dim = -1)
|
||||
q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> (b h) n d', h = h), qkv)
|
||||
|
||||
# apply 2d rotary embeddings to queries and keys, excluding CLS tokens
|
||||
|
||||
sin, cos = pos_emb
|
||||
(q_cls, q), (k_cls, k) = map(lambda t: (t[:, :1], t[:, 1:]), (q, k))
|
||||
q, k = map(lambda t: (t * cos) + (rotate_every_two(t) * sin), (q, k))
|
||||
|
||||
# concat back the CLS tokens
|
||||
|
||||
q = torch.cat((q_cls, q), dim = 1)
|
||||
k = torch.cat((k_cls, k), dim = 1)
|
||||
|
||||
dots = einsum('b i d, b j d -> b i j', q, k) * self.scale
|
||||
|
||||
attn = self.attend(dots)
|
||||
|
||||
out = einsum('b i j, b j d -> b i d', attn, v)
|
||||
out = rearrange(out, '(b h) n d -> b n (h d)', h = h)
|
||||
return self.to_out(out)
|
||||
|
||||
class Transformer(nn.Module):
|
||||
def __init__(self, dim, depth, heads, dim_head, mlp_dim, dropout = 0.):
|
||||
super().__init__()
|
||||
self.layers = nn.ModuleList([])
|
||||
self.pos_emb = AxialRotaryEmbedding(dim_head)
|
||||
for _ in range(depth):
|
||||
self.layers.append(nn.ModuleList([
|
||||
PreNorm(dim, Attention(dim, heads = heads, dim_head = dim_head, dropout = dropout)),
|
||||
PreNorm(dim, FeedForward(dim, mlp_dim, dropout = dropout))
|
||||
]))
|
||||
def forward(self, x):
|
||||
pos_emb = self.pos_emb(x[:, 1:])
|
||||
|
||||
for attn, ff in self.layers:
|
||||
x = attn(x, pos_emb = pos_emb) + x
|
||||
x = ff(x) + x
|
||||
return x
|
||||
|
||||
# Rotary Vision Transformer
|
||||
|
||||
class RvT(nn.Module):
|
||||
def __init__(self, *, image_size, patch_size, num_classes, dim, depth, heads, mlp_dim, channels = 3, dim_head = 64, dropout = 0., emb_dropout = 0.):
|
||||
super().__init__()
|
||||
assert image_size % patch_size == 0, 'Image dimensions must be divisible by the patch size.'
|
||||
num_patches = (image_size // patch_size) ** 2
|
||||
patch_dim = channels * patch_size ** 2
|
||||
|
||||
self.to_patch_embedding = nn.Sequential(
|
||||
Rearrange('b c (h p1) (w p2) -> b (h w) (p1 p2 c)', p1 = patch_size, p2 = patch_size),
|
||||
nn.Linear(patch_dim, dim),
|
||||
)
|
||||
|
||||
self.cls_token = nn.Parameter(torch.randn(1, 1, dim))
|
||||
self.transformer = Transformer(dim, depth, heads, dim_head, mlp_dim, dropout)
|
||||
|
||||
self.mlp_head = nn.Sequential(
|
||||
nn.LayerNorm(dim),
|
||||
nn.Linear(dim, num_classes)
|
||||
)
|
||||
|
||||
def forward(self, img):
|
||||
x = self.to_patch_embedding(img)
|
||||
b, n, _ = x.shape
|
||||
|
||||
cls_tokens = repeat(self.cls_token, '() n d -> b n d', b = b)
|
||||
x = torch.cat((cls_tokens, x), dim=1)
|
||||
|
||||
x = self.transformer(x)
|
||||
|
||||
return self.mlp_head(x)
|
||||
@@ -1,82 +0,0 @@
|
||||
import math
|
||||
import torch
|
||||
from torch import nn
|
||||
|
||||
from vit_pytorch.vit import Transformer
|
||||
|
||||
from einops import rearrange, repeat
|
||||
from einops.layers.torch import Rearrange
|
||||
|
||||
# helpers
|
||||
|
||||
def exists(val):
|
||||
return val is not None
|
||||
|
||||
def conv_output_size(image_size, kernel_size, stride, padding):
|
||||
return int(((image_size - kernel_size + (2 * padding)) / stride) + 1)
|
||||
|
||||
# classes
|
||||
|
||||
class RearrangeImage(nn.Module):
|
||||
def forward(self, x):
|
||||
return rearrange(x, 'b (h w) c -> b c h w', h = int(math.sqrt(x.shape[1])))
|
||||
|
||||
# main class
|
||||
|
||||
class T2TViT(nn.Module):
|
||||
def __init__(self, *, image_size, num_classes, dim, depth = None, heads = None, mlp_dim = None, pool = 'cls', channels = 3, dim_head = 64, dropout = 0., emb_dropout = 0., transformer = None, t2t_layers = ((7, 4), (3, 2), (3, 2))):
|
||||
super().__init__()
|
||||
assert pool in {'cls', 'mean'}, 'pool type must be either cls (cls token) or mean (mean pooling)'
|
||||
|
||||
layers = []
|
||||
layer_dim = channels
|
||||
output_image_size = image_size
|
||||
|
||||
for i, (kernel_size, stride) in enumerate(t2t_layers):
|
||||
layer_dim *= kernel_size ** 2
|
||||
is_first = i == 0
|
||||
output_image_size = conv_output_size(output_image_size, kernel_size, stride, stride // 2)
|
||||
|
||||
layers.extend([
|
||||
RearrangeImage() if not is_first else nn.Identity(),
|
||||
nn.Unfold(kernel_size = kernel_size, stride = stride, padding = stride // 2),
|
||||
Rearrange('b c n -> b n c'),
|
||||
Transformer(dim = layer_dim, heads = 1, depth = 1, dim_head = layer_dim, mlp_dim = layer_dim, dropout = dropout),
|
||||
])
|
||||
|
||||
layers.append(nn.Linear(layer_dim, dim))
|
||||
self.to_patch_embedding = nn.Sequential(*layers)
|
||||
|
||||
self.pos_embedding = nn.Parameter(torch.randn(1, output_image_size ** 2 + 1, dim))
|
||||
self.cls_token = nn.Parameter(torch.randn(1, 1, dim))
|
||||
self.dropout = nn.Dropout(emb_dropout)
|
||||
|
||||
if not exists(transformer):
|
||||
assert all([exists(depth), exists(heads), exists(mlp_dim)]), 'depth, heads, and mlp_dim must be supplied'
|
||||
self.transformer = Transformer(dim, depth, heads, dim_head, mlp_dim, dropout)
|
||||
else:
|
||||
self.transformer = transformer
|
||||
|
||||
self.pool = pool
|
||||
self.to_latent = nn.Identity()
|
||||
|
||||
self.mlp_head = nn.Sequential(
|
||||
nn.LayerNorm(dim),
|
||||
nn.Linear(dim, num_classes)
|
||||
)
|
||||
|
||||
def forward(self, img):
|
||||
x = self.to_patch_embedding(img)
|
||||
b, n, _ = x.shape
|
||||
|
||||
cls_tokens = repeat(self.cls_token, '() n d -> b n d', b = b)
|
||||
x = torch.cat((cls_tokens, x), dim=1)
|
||||
x += self.pos_embedding
|
||||
x = self.dropout(x)
|
||||
|
||||
x = self.transformer(x)
|
||||
|
||||
x = x.mean(dim = 1) if self.pool == 'mean' else x[:, 0]
|
||||
|
||||
x = self.to_latent(x)
|
||||
return self.mlp_head(x)
|
||||
@@ -1,115 +0,0 @@
|
||||
import torch
|
||||
from torch import nn, einsum
|
||||
import torch.nn.functional as F
|
||||
|
||||
from einops import rearrange, repeat
|
||||
from einops.layers.torch import Rearrange
|
||||
|
||||
class PreNorm(nn.Module):
|
||||
def __init__(self, dim, fn):
|
||||
super().__init__()
|
||||
self.norm = nn.LayerNorm(dim)
|
||||
self.fn = fn
|
||||
def forward(self, x, **kwargs):
|
||||
return self.fn(self.norm(x), **kwargs)
|
||||
|
||||
class FeedForward(nn.Module):
|
||||
def __init__(self, dim, hidden_dim, dropout = 0.):
|
||||
super().__init__()
|
||||
self.net = nn.Sequential(
|
||||
nn.Linear(dim, hidden_dim),
|
||||
nn.GELU(),
|
||||
nn.Dropout(dropout),
|
||||
nn.Linear(hidden_dim, dim),
|
||||
nn.Dropout(dropout)
|
||||
)
|
||||
def forward(self, x):
|
||||
return self.net(x)
|
||||
|
||||
class Attention(nn.Module):
|
||||
def __init__(self, dim, heads = 8, dim_head = 64, dropout = 0.):
|
||||
super().__init__()
|
||||
inner_dim = dim_head * heads
|
||||
project_out = not (heads == 1 and dim_head == dim)
|
||||
|
||||
self.heads = heads
|
||||
self.scale = dim_head ** -0.5
|
||||
|
||||
self.attend = nn.Softmax(dim = -1)
|
||||
self.to_qkv = nn.Linear(dim, inner_dim * 3, bias = False)
|
||||
|
||||
self.to_out = nn.Sequential(
|
||||
nn.Linear(inner_dim, dim),
|
||||
nn.Dropout(dropout)
|
||||
) if project_out else nn.Identity()
|
||||
|
||||
def forward(self, x):
|
||||
b, n, _, h = *x.shape, self.heads
|
||||
qkv = self.to_qkv(x).chunk(3, dim = -1)
|
||||
q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> b h n d', h = h), qkv)
|
||||
|
||||
dots = einsum('b h i d, b h j d -> b h i j', q, k) * self.scale
|
||||
|
||||
attn = self.attend(dots)
|
||||
|
||||
out = einsum('b h i j, b h j d -> b h i d', attn, v)
|
||||
out = rearrange(out, 'b h n d -> b n (h d)')
|
||||
return self.to_out(out)
|
||||
|
||||
class Transformer(nn.Module):
|
||||
def __init__(self, dim, depth, heads, dim_head, mlp_dim, dropout = 0.):
|
||||
super().__init__()
|
||||
self.layers = nn.ModuleList([])
|
||||
for _ in range(depth):
|
||||
self.layers.append(nn.ModuleList([
|
||||
PreNorm(dim, Attention(dim, heads = heads, dim_head = dim_head, dropout = dropout)),
|
||||
PreNorm(dim, FeedForward(dim, mlp_dim, dropout = dropout))
|
||||
]))
|
||||
def forward(self, x):
|
||||
for attn, ff in self.layers:
|
||||
x = attn(x) + x
|
||||
x = ff(x) + x
|
||||
return x
|
||||
|
||||
class ViT(nn.Module):
|
||||
def __init__(self, *, image_size, patch_size, num_classes, dim, depth, heads, mlp_dim, pool = 'cls', channels = 3, dim_head = 64, dropout = 0., emb_dropout = 0.):
|
||||
super().__init__()
|
||||
assert image_size % patch_size == 0, 'Image dimensions must be divisible by the patch size.'
|
||||
num_patches = (image_size // patch_size) ** 2
|
||||
patch_dim = channels * patch_size ** 2
|
||||
assert pool in {'cls', 'mean'}, 'pool type must be either cls (cls token) or mean (mean pooling)'
|
||||
|
||||
self.to_patch_embedding = nn.Sequential(
|
||||
Rearrange('b c (h p1) (w p2) -> b (h w) (p1 p2 c)', p1 = patch_size, p2 = patch_size),
|
||||
nn.Linear(patch_dim, dim),
|
||||
)
|
||||
|
||||
self.pos_embedding = nn.Parameter(torch.randn(1, num_patches + 1, dim))
|
||||
self.cls_token = nn.Parameter(torch.randn(1, 1, dim))
|
||||
self.dropout = nn.Dropout(emb_dropout)
|
||||
|
||||
self.transformer = Transformer(dim, depth, heads, dim_head, mlp_dim, dropout)
|
||||
|
||||
self.pool = pool
|
||||
self.to_latent = nn.Identity()
|
||||
|
||||
self.mlp_head = nn.Sequential(
|
||||
nn.LayerNorm(dim),
|
||||
nn.Linear(dim, num_classes)
|
||||
)
|
||||
|
||||
def forward(self, img):
|
||||
x = self.to_patch_embedding(img)
|
||||
b, n, _ = x.shape
|
||||
|
||||
cls_tokens = repeat(self.cls_token, '() n d -> b n d', b = b)
|
||||
x = torch.cat((cls_tokens, x), dim=1)
|
||||
x += self.pos_embedding[:, :(n + 1)]
|
||||
x = self.dropout(x)
|
||||
|
||||
x = self.transformer(x)
|
||||
|
||||
x = x.mean(dim = 1) if self.pool == 'mean' else x[:, 0]
|
||||
|
||||
x = self.to_latent(x)
|
||||
return self.mlp_head(x)
|
||||
123
vit_pytorch/vit_pytorch.py
Normal file
123
vit_pytorch/vit_pytorch.py
Normal file
@@ -0,0 +1,123 @@
|
||||
import torch
|
||||
import torch.nn.functional as F
|
||||
from einops import rearrange
|
||||
from torch import nn
|
||||
|
||||
class Residual(nn.Module):
|
||||
def __init__(self, fn):
|
||||
super().__init__()
|
||||
self.fn = fn
|
||||
def forward(self, x, **kwargs):
|
||||
return self.fn(x, **kwargs) + x
|
||||
|
||||
class PreNorm(nn.Module):
|
||||
def __init__(self, dim, fn):
|
||||
super().__init__()
|
||||
self.norm = nn.LayerNorm(dim)
|
||||
self.fn = fn
|
||||
def forward(self, x, **kwargs):
|
||||
return self.fn(self.norm(x), **kwargs)
|
||||
|
||||
class FeedForward(nn.Module):
|
||||
def __init__(self, dim, hidden_dim, dropout = 0.):
|
||||
super().__init__()
|
||||
self.net = nn.Sequential(
|
||||
nn.Linear(dim, hidden_dim),
|
||||
nn.GELU(),
|
||||
nn.Dropout(dropout),
|
||||
nn.Linear(hidden_dim, dim),
|
||||
nn.Dropout(dropout)
|
||||
)
|
||||
def forward(self, x):
|
||||
return self.net(x)
|
||||
|
||||
class Attention(nn.Module):
|
||||
def __init__(self, dim, heads = 8, dropout = 0.):
|
||||
super().__init__()
|
||||
self.heads = heads
|
||||
self.scale = dim ** -0.5
|
||||
|
||||
self.to_qkv = nn.Linear(dim, dim * 3, bias = False)
|
||||
self.to_out = nn.Sequential(
|
||||
nn.Linear(dim, dim),
|
||||
nn.Dropout(dropout)
|
||||
)
|
||||
|
||||
def forward(self, x, mask = None):
|
||||
b, n, _, h = *x.shape, self.heads
|
||||
qkv = self.to_qkv(x)
|
||||
q, k, v = rearrange(qkv, 'b n (qkv h d) -> qkv b h n d', qkv = 3, h = h)
|
||||
|
||||
dots = torch.einsum('bhid,bhjd->bhij', q, k) * self.scale
|
||||
|
||||
if mask is not None:
|
||||
mask = F.pad(mask.flatten(1), (1, 0), value = True)
|
||||
assert mask.shape[-1] == dots.shape[-1], 'mask has incorrect dimensions'
|
||||
mask = mask[:, None, :] * mask[:, :, None]
|
||||
dots.masked_fill_(~mask, float('-inf'))
|
||||
del mask
|
||||
|
||||
attn = dots.softmax(dim=-1)
|
||||
|
||||
out = torch.einsum('bhij,bhjd->bhid', attn, v)
|
||||
out = rearrange(out, 'b h n d -> b n (h d)')
|
||||
out = self.to_out(out)
|
||||
return out
|
||||
|
||||
class Transformer(nn.Module):
|
||||
def __init__(self, dim, depth, heads, mlp_dim, dropout):
|
||||
super().__init__()
|
||||
self.layers = nn.ModuleList([])
|
||||
for _ in range(depth):
|
||||
self.layers.append(nn.ModuleList([
|
||||
Residual(PreNorm(dim, Attention(dim, heads = heads, dropout = dropout))),
|
||||
Residual(PreNorm(dim, FeedForward(dim, mlp_dim, dropout = dropout)))
|
||||
]))
|
||||
def forward(self, x, mask = None):
|
||||
for attn, ff in self.layers:
|
||||
x = attn(x, mask = mask)
|
||||
x = ff(x)
|
||||
return x
|
||||
|
||||
class ViT(nn.Module):
|
||||
def __init__(self, *, image_size, patch_size, num_classes, dim, depth, heads, mlp_dim, channels = 3, dropout = 0., emb_dropout = 0.):
|
||||
super().__init__()
|
||||
assert image_size % patch_size == 0, 'image dimensions must be divisible by the patch size'
|
||||
num_patches = (image_size // patch_size) ** 2
|
||||
patch_dim = channels * patch_size ** 2
|
||||
|
||||
self.patch_size = patch_size
|
||||
|
||||
self.pos_embedding = nn.Parameter(torch.randn(1, num_patches + 1, dim))
|
||||
self.patch_to_embedding = nn.Linear(patch_dim, dim)
|
||||
self.cls_token = nn.Parameter(torch.randn(1, 1, dim))
|
||||
self.dropout = nn.Dropout(emb_dropout)
|
||||
|
||||
self.transformer = Transformer(dim, depth, heads, mlp_dim, dropout)
|
||||
|
||||
self.to_cls_token = nn.Identity()
|
||||
|
||||
self.mlp_head = nn.Sequential(
|
||||
nn.LayerNorm(dim),
|
||||
nn.Linear(dim, mlp_dim),
|
||||
nn.GELU(),
|
||||
nn.Dropout(dropout),
|
||||
nn.Linear(mlp_dim, num_classes),
|
||||
nn.Dropout(dropout)
|
||||
)
|
||||
|
||||
def forward(self, img, mask = None):
|
||||
p = self.patch_size
|
||||
|
||||
x = rearrange(img, 'b c (h p1) (w p2) -> b (h w) (p1 p2 c)', p1 = p, p2 = p)
|
||||
x = self.patch_to_embedding(x)
|
||||
|
||||
cls_tokens = self.cls_token.expand(img.shape[0], -1, -1)
|
||||
x = torch.cat((cls_tokens, x), dim=1)
|
||||
x += self.pos_embedding
|
||||
x = self.dropout(x)
|
||||
|
||||
x = self.transformer(x, mask)
|
||||
|
||||
x = self.to_cls_token(x[:, 0])
|
||||
return self.mlp_head(x)
|
||||
Reference in New Issue
Block a user