fix typo

update  mpp.py to work on GPU

README.md

@@ -38,6 +38,7 @@ preds = v(img) # (1, 1000)
 ```
 
 ## Parameters
+
 - `image_size`: int.  
 Image size. If you have rectangular images, make sure your image size is the maximum of the width and height
 - `patch_size`: int.  
@@ -270,6 +271,8 @@ preds = v(img) # (1, 1000)
 
 <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.
 
+<a href="https://github.com/facebookresearch/LeViT">Official repository</a>
+
 ```python
 import torch
 from vit_pytorch.levit import LeViT
@@ -334,6 +337,73 @@ img = torch.randn(1, 3, 224, 224)
 pred = v(img) # (1, 1000)
 ```
 
+## Twins SVT
+
+<img src="./images/twins_svt.png" width="400px"></img>
+
+This <a href="https://arxiv.org/abs/2104.13840">paper</a> proposes mixing local and global attention, along with position encoding generator (proposed in <a href="https://arxiv.org/abs/2102.10882">CPVT</a>) and global average pooling, to achieve the same results as <a href="https://arxiv.org/abs/2103.14030">Swin</a>, without the extra complexity of shifted windows, CLS tokens, nor positional embeddings.
+
+```python
+import torch
+from vit_pytorch.twins_svt import TwinsSVT
+
+model = TwinsSVT(
+    num_classes = 1000,       # number of output classes
+    s1_emb_dim = 64,          # stage 1 - patch embedding projected dimension
+    s1_patch_size = 4,        # stage 1 - patch size for patch embedding
+    s1_local_patch_size = 7,  # stage 1 - patch size for local attention
+    s1_global_k = 7,          # stage 1 - global attention key / value reduction factor, defaults to 7 as specified in paper
+    s1_depth = 1,             # stage 1 - number of transformer blocks (local attn -> ff -> global attn -> ff)
+    s2_emb_dim = 128,         # stage 2 (same as above)
+    s2_patch_size = 2,
+    s2_local_patch_size = 7,
+    s2_global_k = 7,
+    s2_depth = 1,
+    s3_emb_dim = 256,         # stage 3 (same as above)
+    s3_patch_size = 2,
+    s3_local_patch_size = 7,
+    s3_global_k = 7,
+    s3_depth = 5,
+    s4_emb_dim = 512,         # stage 4 (same as above)
+    s4_patch_size = 2,
+    s4_local_patch_size = 7,
+    s4_global_k = 7,
+    s4_depth = 4,
+    peg_kernel_size = 3,      # positional encoding generator kernel size
+    dropout = 0.              # dropout
+)
+
+img = torch.randn(1, 3, 224, 224)
+
+pred = model(img) # (1, 1000)
+```
+
+## NesT
+
+<img src="./images/nest.png" width="400px"></img>
+
+This <a href="https://arxiv.org/abs/2105.12723">paper</a> decided to process the image in hierarchical stages, with attention only within tokens of local blocks, which aggregate as it moves up the heirarchy. The aggregation is done in the image plane, and contains a convolution and subsequent maxpool to allow it to pass information across the boundary.
+
+You can use it with the following code (ex. NesT-T)
+
+```python
+import torch
+from vit_pytorch.nest import NesT
+
+nest = NesT(
+    image_size = 224,
+    patch_size = 4,
+    dim = 96,
+    heads = 3,
+    num_hierarchies = 3,        # number of hierarchies
+    block_repeats = (8, 4, 1),  # the number of transformer blocks at each heirarchy, starting from the bottom
+    num_classes = 1000
+)
+
+img = torch.randn(1, 3, 224, 224)
+pred = nest(img) # (1, 1000)
+```
+
 ## Masked Patch Prediction
 
 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.
@@ -380,6 +450,60 @@ for _ in range(100):
 torch.save(model.state_dict(), './pretrained-net.pt')
 ```
 
+## Dino
+
+<img src="./images/dino.png" width="350px"></img>
+
+You can train `ViT` with the recent SOTA self-supervised learning technique, <a href="https://arxiv.org/abs/2104.14294">Dino</a>, with the following code.
+
+<a href="https://www.youtube.com/watch?v=h3ij3F3cPIk">Yannic Kilcher</a> video
+
+```python
+import torch
+from vit_pytorch import ViT, Dino
+
+model = ViT(
+    image_size = 256,
+    patch_size = 32,
+    num_classes = 1000,
+    dim = 1024,
+    depth = 6,
+    heads = 8,
+    mlp_dim = 2048
+)
+
+learner = Dino(
+    model,
+    image_size = 256,
+    hidden_layer = 'to_latent',        # hidden layer name or index, from which to extract the embedding
+    projection_hidden_size = 256,      # projector network hidden dimension
+    projection_layers = 4,             # number of layers in projection network
+    num_classes_K = 65336,             # output logits dimensions (referenced as K in paper)
+    student_temp = 0.9,                # student temperature
+    teacher_temp = 0.04,               # teacher temperature, needs to be annealed from 0.04 to 0.07 over 30 epochs
+    local_upper_crop_scale = 0.4,      # upper bound for local crop - 0.4 was recommended in the paper 
+    global_lower_crop_scale = 0.5,     # lower bound for global crop - 0.5 was recommended in the paper
+    moving_average_decay = 0.9,        # moving average of encoder - paper showed anywhere from 0.9 to 0.999 was ok
+    center_moving_average_decay = 0.9, # moving average of teacher centers - paper showed anywhere from 0.9 to 0.999 was ok
+)
+
+opt = torch.optim.Adam(learner.parameters(), lr = 3e-4)
+
+def sample_unlabelled_images():
+    return torch.randn(20, 3, 256, 256)
+
+for _ in range(100):
+    images = sample_unlabelled_images()
+    loss = learner(images)
+    opt.zero_grad()
+    loss.backward()
+    opt.step()
+    learner.update_moving_average() # update moving average of teacher encoder and teacher centers
+
+# save your improved network
+torch.save(model.state_dict(), './pretrained-net.pt')
+```
+
 ## Accessing Attention
 
 If you would like to visualize the attention weights (post-softmax) for your research, just follow the procedure below
@@ -423,56 +547,6 @@ v = v.eject()  # wrapper is discarded and original ViT instance is returned
 
 ## Research Ideas
 
-### Self Supervised Training
-
-You can train this with a near SOTA self-supervised learning technique, <a href="https://github.com/lucidrains/byol-pytorch">BYOL</a>, with the following code.
-
-(1)
-```bash
-$ pip install byol-pytorch
-```
-
-(2)
-```python
-import torch
-from vit_pytorch import ViT
-from byol_pytorch import BYOL
-
-model = ViT(
-    image_size = 256,
-    patch_size = 32,
-    num_classes = 1000,
-    dim = 1024,
-    depth = 6,
-    heads = 8,
-    mlp_dim = 2048
-)
-
-learner = BYOL(
-    model,
-    image_size = 256,
-    hidden_layer = 'to_latent'
-)
-
-opt = torch.optim.Adam(learner.parameters(), lr=3e-4)
-
-def sample_unlabelled_images():
-    return torch.randn(20, 3, 256, 256)
-
-for _ in range(100):
-    images = sample_unlabelled_images()
-    loss = learner(images)
-    opt.zero_grad()
-    loss.backward()
-    opt.step()
-    learner.update_moving_average() # update moving average of target encoder
-
-# save your improved network
-torch.save(model.state_dict(), './pretrained-net.pt')
-```
-
-A pytorch-lightning script is ready for you to use at the repository link above.
-
 ### Efficient Attention
 
 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.
@@ -542,6 +616,58 @@ img = torch.randn(1, 3, 224, 224)
 v(img) # (1, 1000)
 ```
 
+## FAQ
+
+- How do I pass in non-square images?
+
+You can already pass in non-square images - you just have to make sure your height and width is less than or equal to the `image_size`, and both divisible by the `patch_size`
+
+ex.
+
+```python
+import torch
+from vit_pytorch 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
+)
+
+img = torch.randn(1, 3, 256, 128) # <-- not a square
+
+preds = v(img) # (1, 1000)
+```
+
+- How do I pass in non-square patches?
+
+```python
+import torch
+from vit_pytorch import ViT
+
+v = ViT(
+    num_classes = 1000,
+    image_size = (256, 128),  # image size is a tuple of (height, width)
+    patch_size = (32, 16),    # patch size is a tuple of (height, width)
+    dim = 1024,
+    depth = 6,
+    heads = 16,
+    mlp_dim = 2048,
+    dropout = 0.1,
+    emb_dropout = 0.1
+)
+
+img = torch.randn(1, 3, 256, 128)
+
+preds = v(img)
+```
+
 ## Resources
 
 Coming from computer vision and new to transformers? Here are some resources that greatly accelerated my learning.
@@ -665,6 +791,50 @@ Coming from computer vision and new to transformers? Here are some resources tha
 }
 ```
 
+```bibtex
+@misc{chu2021twins,
+    title   = {Twins: Revisiting Spatial Attention Design in Vision Transformers},
+    author  = {Xiangxiang Chu and Zhi Tian and Yuqing Wang and Bo Zhang and Haibing Ren and Xiaolin Wei and Huaxia Xia and Chunhua Shen},
+    year    = {2021},
+    eprint  = {2104.13840},
+    archivePrefix = {arXiv},
+    primaryClass = {cs.CV}
+}
+```
+
+```bibtex
+@misc{su2021roformer,
+    title   = {RoFormer: Enhanced Transformer with Rotary Position Embedding}, 
+    author  = {Jianlin Su and Yu Lu and Shengfeng Pan and Bo Wen and Yunfeng Liu},
+    year    = {2021},
+    eprint  = {2104.09864},
+    archivePrefix = {arXiv},
+    primaryClass = {cs.CL}
+}
+```
+
+```bibtex
+@misc{zhang2021aggregating,
+    title   = {Aggregating Nested Transformers},
+    author  = {Zizhao Zhang and Han Zhang and Long Zhao and Ting Chen and Tomas Pfister},
+    year    = {2021},
+    eprint  = {2105.12723},
+    archivePrefix = {arXiv},
+    primaryClass = {cs.CV}
+}
+```
+
+```bibtex
+@misc{caron2021emerging,
+    title   = {Emerging Properties in Self-Supervised Vision Transformers},
+    author  = {Mathilde Caron and Hugo Touvron and Ishan Misra and Hervé Jégou and Julien Mairal and Piotr Bojanowski and Armand Joulin},
+    year    = {2021},
+    eprint  = {2104.14294},
+    archivePrefix = {arXiv},
+    primaryClass = {cs.CV}
+}
+```
+
 ```bibtex
 @misc{vaswani2017attention,
     title   = {Attention Is All You Need},

setup.py

@@ -3,7 +3,7 @@ from setuptools import setup, find_packages
 setup(
   name = 'vit-pytorch',
   packages = find_packages(exclude=['examples']),
-  version = '0.16.4',
+  version = '0.19.3',
   license='MIT',
   description = 'Vision Transformer (ViT) - Pytorch',
   author = 'Phil Wang',
@@ -15,8 +15,9 @@ setup(
     'image recognition'
   ],
   install_requires=[
+    'einops>=0.3',
     'torch>=1.6',
-    'einops>=0.3'
+    'torchvision'
   ],
   classifiers=[
     'Development Status :: 4 - Beta',

vit_pytorch/__init__.py

@@ -1 +1,2 @@
 from vit_pytorch.vit import ViT
+from vit_pytorch.dino import Dino

vit_pytorch/cvt.py

@@ -22,15 +22,25 @@ def group_by_key_prefix_and_remove_prefix(prefix, d):
 
 # classes
 
+class LayerNorm(nn.Module): # layernorm, but done in the channel dimension #1
+    def __init__(self, dim, eps = 1e-5):
+        super().__init__()
+        self.eps = eps
+        self.g = nn.Parameter(torch.ones(1, dim, 1, 1))
+        self.b = nn.Parameter(torch.zeros(1, dim, 1, 1))
+
+    def forward(self, x):
+        std = torch.var(x, dim = 1, unbiased = False, keepdim = True).sqrt()
+        mean = torch.mean(x, dim = 1, keepdim = True)
+        return (x - mean) / (std + self.eps) * self.g + self.b
+
 class PreNorm(nn.Module):
     def __init__(self, dim, fn):
         super().__init__()
-        self.norm = nn.LayerNorm(dim)
+        self.norm = 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):
@@ -67,8 +77,8 @@ class Attention(nn.Module):
 
         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_q = DepthWiseConv2d(dim, inner_dim, proj_kernel, padding = padding, stride = 1, bias = False)
+        self.to_kv = DepthWiseConv2d(dim, inner_dim * 2, proj_kernel, padding = padding, stride = kv_proj_stride, bias = False)
 
         self.to_out = nn.Sequential(
             nn.Conv2d(inner_dim, dim, 1),
@@ -130,7 +140,7 @@ class CvT(nn.Module):
         s3_emb_stride = 2,
         s3_proj_kernel = 3,
         s3_kv_proj_stride = 2,
-        s3_heads = 4,
+        s3_heads = 6,
         s3_depth = 10,
         s3_mlp_mult = 4,
         dropout = 0.
@@ -146,6 +156,7 @@ class CvT(nn.Module):
 
             layers.append(nn.Sequential(
                 nn.Conv2d(dim, config['emb_dim'], kernel_size = config['emb_kernel'], padding = (config['emb_kernel'] // 2), stride = config['emb_stride']),
+                LayerNorm(config['emb_dim']),
                 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)
             ))
 

vit_pytorch/dino.py

@@ -0,0 +1,303 @@
+import copy
+import random
+from functools import wraps, partial
+
+import torch
+from torch import nn
+import torch.nn.functional as F
+
+from torchvision import transforms as T
+
+# helper functions
+
+def exists(val):
+    return val is not None
+
+def default(val, default):
+    return val if exists(val) else default
+
+def singleton(cache_key):
+    def inner_fn(fn):
+        @wraps(fn)
+        def wrapper(self, *args, **kwargs):
+            instance = getattr(self, cache_key)
+            if instance is not None:
+                return instance
+
+            instance = fn(self, *args, **kwargs)
+            setattr(self, cache_key, instance)
+            return instance
+        return wrapper
+    return inner_fn
+
+def get_module_device(module):
+    return next(module.parameters()).device
+
+def set_requires_grad(model, val):
+    for p in model.parameters():
+        p.requires_grad = val
+
+# loss function # (algorithm 1 in the paper)
+
+def loss_fn(
+    teacher_logits,
+    student_logits,
+    teacher_temp,
+    student_temp,
+    centers,
+    eps = 1e-20
+):
+    teacher_logits = teacher_logits.detach()
+    student_probs = (student_logits / student_temp).softmax(dim = -1)
+    teacher_probs = ((teacher_logits - centers) / teacher_temp).softmax(dim = -1)
+    return - (teacher_probs * torch.log(student_probs + eps)).sum(dim = -1).mean()
+
+# augmentation utils
+
+class RandomApply(nn.Module):
+    def __init__(self, fn, p):
+        super().__init__()
+        self.fn = fn
+        self.p = p
+
+    def forward(self, x):
+        if random.random() > self.p:
+            return x
+        return self.fn(x)
+
+# exponential moving average
+
+class EMA():
+    def __init__(self, beta):
+        super().__init__()
+        self.beta = beta
+
+    def update_average(self, old, new):
+        if old is None:
+            return new
+        return old * self.beta + (1 - self.beta) * new
+
+def update_moving_average(ema_updater, ma_model, current_model):
+    for current_params, ma_params in zip(current_model.parameters(), ma_model.parameters()):
+        old_weight, up_weight = ma_params.data, current_params.data
+        ma_params.data = ema_updater.update_average(old_weight, up_weight)
+
+# MLP class for projector and predictor
+
+class L2Norm(nn.Module):
+    def forward(self, x, eps = 1e-6):
+        norm = x.norm(dim = 1, keepdim = True).clamp(min = eps)
+        return x / norm
+
+class MLP(nn.Module):
+    def __init__(self, dim, dim_out, num_layers, hidden_size = 256):
+        super().__init__()
+
+        layers = []
+        dims = (dim, *((hidden_size,) * (num_layers - 1)))
+
+        for ind, (layer_dim_in, layer_dim_out) in enumerate(zip(dims[:-1], dims[1:])):
+            is_last = ind == (len(dims) - 1)
+
+            layers.extend([
+                nn.Linear(layer_dim_in, layer_dim_out),
+                nn.GELU() if not is_last else nn.Identity()
+            ])
+
+        self.net = nn.Sequential(
+            *layers,
+            L2Norm(),
+            nn.Linear(hidden_size, dim_out)
+        )
+
+    def forward(self, x):
+        return self.net(x)
+
+# a wrapper class for the base neural network
+# will manage the interception of the hidden layer output
+# and pipe it into the projecter and predictor nets
+
+class NetWrapper(nn.Module):
+    def __init__(self, net, output_dim, projection_hidden_size, projection_num_layers, layer = -2):
+        super().__init__()
+        self.net = net
+        self.layer = layer
+
+        self.projector = None
+        self.projection_hidden_size = projection_hidden_size
+        self.projection_num_layers = projection_num_layers
+        self.output_dim = output_dim
+
+        self.hidden = {}
+        self.hook_registered = False
+
+    def _find_layer(self):
+        if type(self.layer) == str:
+            modules = dict([*self.net.named_modules()])
+            return modules.get(self.layer, None)
+        elif type(self.layer) == int:
+            children = [*self.net.children()]
+            return children[self.layer]
+        return None
+
+    def _hook(self, _, input, output):
+        device = input[0].device
+        self.hidden[device] = output.flatten(1)
+
+    def _register_hook(self):
+        layer = self._find_layer()
+        assert layer is not None, f'hidden layer ({self.layer}) not found'
+        handle = layer.register_forward_hook(self._hook)
+        self.hook_registered = True
+
+    @singleton('projector')
+    def _get_projector(self, hidden):
+        _, dim = hidden.shape
+        projector = MLP(dim, self.output_dim, self.projection_num_layers, self.projection_hidden_size)
+        return projector.to(hidden)
+
+    def get_embedding(self, x):
+        if self.layer == -1:
+            return self.net(x)
+
+        if not self.hook_registered:
+            self._register_hook()
+
+        self.hidden.clear()
+        _ = self.net(x)
+        hidden = self.hidden[x.device]
+        self.hidden.clear()
+
+        assert hidden is not None, f'hidden layer {self.layer} never emitted an output'
+        return hidden
+
+    def forward(self, x, return_projection = True):
+        embed = self.get_embedding(x)
+        if not return_projection:
+            return embed
+
+        projector = self._get_projector(embed)
+        return projector(embed), embed
+
+# main class
+
+class Dino(nn.Module):
+    def __init__(
+        self,
+        net,
+        image_size,
+        hidden_layer = -2,
+        projection_hidden_size = 256,
+        num_classes_K = 65336,
+        projection_layers = 4,
+        student_temp = 0.9,
+        teacher_temp = 0.04,
+        local_upper_crop_scale = 0.4,
+        global_lower_crop_scale = 0.5,
+        moving_average_decay = 0.9,
+        center_moving_average_decay = 0.9,
+        augment_fn = None,
+        augment_fn2 = None
+    ):
+        super().__init__()
+        self.net = net
+
+        # default BYOL augmentation
+
+        DEFAULT_AUG = torch.nn.Sequential(
+            RandomApply(
+                T.ColorJitter(0.8, 0.8, 0.8, 0.2),
+                p = 0.3
+            ),
+            T.RandomGrayscale(p=0.2),
+            T.RandomHorizontalFlip(),
+            RandomApply(
+                T.GaussianBlur((3, 3), (1.0, 2.0)),
+                p = 0.2
+            ),
+            T.Normalize(
+                mean=torch.tensor([0.485, 0.456, 0.406]),
+                std=torch.tensor([0.229, 0.224, 0.225])),
+        )
+
+        self.augment1 = default(augment_fn, DEFAULT_AUG)
+        self.augment2 = default(augment_fn2, DEFAULT_AUG)
+
+        # local and global crops
+
+        self.local_crop = T.RandomResizedCrop((image_size, image_size), scale = (0.05, local_upper_crop_scale))
+        self.global_crop = T.RandomResizedCrop((image_size, image_size), scale = (global_lower_crop_scale, 1.))
+
+        self.student_encoder = NetWrapper(net, num_classes_K, projection_hidden_size, projection_layers, layer = hidden_layer)
+
+        self.teacher_encoder = None
+        self.teacher_ema_updater = EMA(moving_average_decay)
+
+        self.register_buffer('teacher_centers', torch.zeros(1, num_classes_K))
+        self.register_buffer('last_teacher_centers',  torch.zeros(1, num_classes_K))
+
+        self.teacher_centering_ema_updater = EMA(center_moving_average_decay)
+
+        self.student_temp = student_temp
+        self.teacher_temp = teacher_temp
+
+        # get device of network and make wrapper same device
+        device = get_module_device(net)
+        self.to(device)
+
+        # send a mock image tensor to instantiate singleton parameters
+        self.forward(torch.randn(2, 3, image_size, image_size, device=device))
+
+    @singleton('teacher_encoder')
+    def _get_teacher_encoder(self):
+        teacher_encoder = copy.deepcopy(self.student_encoder)
+        set_requires_grad(teacher_encoder, False)
+        return teacher_encoder
+
+    def reset_moving_average(self):
+        del self.teacher_encoder
+        self.teacher_encoder = None
+
+    def update_moving_average(self):
+        assert self.teacher_encoder is not None, 'target encoder has not been created yet'
+        update_moving_average(self.teacher_ema_updater, self.teacher_encoder, self.student_encoder)
+
+        new_teacher_centers = self.teacher_centering_ema_updater.update_average(self.teacher_centers, self.last_teacher_centers)
+        self.teacher_centers.copy_(new_teacher_centers)
+
+    def forward(
+        self,
+        x,
+        return_embedding = False,
+        return_projection = True,
+        student_temp = None,
+        teacher_temp = None
+    ):
+        if return_embedding:
+            return self.student_encoder(x, return_projection = return_projection)
+
+        image_one, image_two = self.augment1(x), self.augment2(x)
+
+        local_image_one, local_image_two   = self.local_crop(image_one),  self.local_crop(image_two)
+        global_image_one, global_image_two = self.global_crop(image_one), self.global_crop(image_two)
+
+        student_proj_one, _ = self.student_encoder(local_image_one)
+        student_proj_two, _ = self.student_encoder(local_image_two)
+
+        with torch.no_grad():
+            teacher_encoder = self._get_teacher_encoder()
+            teacher_proj_one, _ = teacher_encoder(global_image_one)
+            teacher_proj_two, _ = teacher_encoder(global_image_two)
+
+        loss_fn_ = partial(
+            loss_fn,
+            student_temp = default(student_temp, self.student_temp),
+            teacher_temp = default(teacher_temp, self.teacher_temp),
+            centers = self.teacher_centers
+        )
+
+        teacher_logits_avg = torch.cat((teacher_proj_one, teacher_proj_two)).mean(dim = 0)
+        self.last_teacher_centers.copy_(teacher_logits_avg)
+
+        loss = (loss_fn_(teacher_proj_one, student_proj_two) + loss_fn_(teacher_proj_two, student_proj_one)) / 2
+        return loss

vit_pytorch/levit.py

@@ -53,10 +53,13 @@ class Attention(nn.Module):
 
         self.attend = nn.Softmax(dim = -1)
 
+        out_batch_norm = nn.BatchNorm2d(dim_out)
+        nn.init.zeros_(out_batch_norm.weight)
+
         self.to_out = nn.Sequential(
             nn.GELU(),
             nn.Conv2d(inner_dim_value, dim_out, 1),
-            nn.BatchNorm2d(dim_out),
+            out_batch_norm,
             nn.Dropout(dropout)
         )
 
@@ -81,7 +84,7 @@ class Attention(nn.Module):
     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
+        return fmap + (bias / self.scale)
 
     def forward(self, x):
         b, n, *_, h = *x.shape, self.heads

vit_pytorch/local_vit.py

@@ -149,4 +149,4 @@ class LocalViT(nn.Module):
 
         x = self.transformer(x)
 
-        return self.mlp_head(x)
+        return self.mlp_head(x[:, 0])

vit_pytorch/mpp.py

@@ -50,7 +50,7 @@ class MPPLoss(nn.Module):
         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)
+        channel_bins = torch.arange(bin_size, self.max_pixel_val, bin_size).to(avg_target.device)
         discretized_target = torch.bucketize(avg_target, channel_bins)
         discretized_target = F.one_hot(discretized_target,
                                        self.output_channel_bits)
@@ -86,7 +86,6 @@ class MPP(nn.Module):
                  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)
@@ -127,8 +126,9 @@ class MPP(nn.Module):
             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_patch_sampling_prob).to(mask.device)
+
+            bool_random_patch_prob = mask * (random_patch_prob == True)
             random_patches = torch.randint(0,
                                            input.shape[1],
                                            (input.shape[0], input.shape[1]),
@@ -140,7 +140,7 @@ class MPP(nn.Module):
                 bool_random_patch_prob]
 
         # [mask] input
-        replace_prob = prob_mask_like(input, self.replace_prob)
+        replace_prob = prob_mask_like(input, self.replace_prob).to(mask.device)
         bool_mask_replace = (mask * replace_prob) == True
         masked_input[bool_mask_replace] = self.mask_token
 

vit_pytorch/nest.py

@@ -0,0 +1,178 @@
+import torch
+from torch import nn, einsum
+
+from einops import rearrange
+from einops.layers.torch import Rearrange, Reduce
+
+# helpers
+
+def cast_tuple(val, depth):
+    return val if isinstance(val, tuple) else ((val,) * depth)
+
+# classes
+
+class ChanNorm(nn.Module):
+    def __init__(self, dim, eps = 1e-5):
+        super().__init__()
+        self.eps = eps
+        self.g = nn.Parameter(torch.ones(1, dim, 1, 1))
+        self.b = nn.Parameter(torch.zeros(1, dim, 1, 1))
+
+    def forward(self, x):
+        std = torch.var(x, dim = 1, unbiased = False, keepdim = True).sqrt()
+        mean = torch.mean(x, dim = 1, keepdim = True)
+        return (x - mean) / (std + self.eps) * self.g + self.b
+
+class PreNorm(nn.Module):
+    def __init__(self, dim, fn):
+        super().__init__()
+        self.norm = ChanNorm(dim)
+        self.fn = fn
+
+    def forward(self, x, **kwargs):
+        return self.fn(self.norm(x), **kwargs)
+
+class FeedForward(nn.Module):
+    def __init__(self, dim, mlp_mult = 4, dropout = 0.):
+        super().__init__()
+        self.net = nn.Sequential(
+            nn.Conv2d(dim, dim * mlp_mult, 1),
+            nn.GELU(),
+            nn.Dropout(dropout),
+            nn.Conv2d(dim * mlp_mult, dim, 1),
+            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__()
+        dim_head = dim // heads
+        inner_dim = dim_head * heads
+        self.heads = heads
+        self.scale = dim_head ** -0.5
+
+        self.attend = nn.Softmax(dim = -1)
+        self.to_qkv = nn.Conv2d(dim, inner_dim * 3, 1, bias = False)
+
+        self.to_out = nn.Sequential(
+            nn.Conv2d(inner_dim, dim, 1),
+            nn.Dropout(dropout)
+        )
+
+    def forward(self, x):
+        b, c, h, w, heads = *x.shape, self.heads
+
+        qkv = self.to_qkv(x).chunk(3, dim = 1)
+        q, k, v = map(lambda t: rearrange(t, 'b (h d) x y -> b h (x y) d', h = heads), 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 (x y) d -> b (h d) x y', x = h, y = w)
+        return self.to_out(out)
+
+def Aggregate(dim, dim_out):
+    return nn.Sequential(
+        nn.Conv2d(dim, dim_out, 3, padding = 1),
+        ChanNorm(dim_out),
+        nn.MaxPool2d(3, stride = 2, padding = 1)
+    )
+
+class Transformer(nn.Module):
+    def __init__(self, dim, seq_len, depth, heads, mlp_mult, dropout = 0.):
+        super().__init__()
+        self.layers = nn.ModuleList([])
+        self.pos_emb = nn.Parameter(torch.randn(seq_len))
+
+        for _ in range(depth):
+            self.layers.append(nn.ModuleList([
+                PreNorm(dim, Attention(dim, heads = heads, dropout = dropout)),
+                PreNorm(dim, FeedForward(dim, mlp_mult, dropout = dropout))
+            ]))
+    def forward(self, x):
+        *_, h, w = x.shape
+
+        pos_emb = self.pos_emb[:(h * w)]
+        pos_emb = rearrange(pos_emb, '(h w) -> () () h w', h = h, w = w)
+        x = x + pos_emb
+
+        for attn, ff in self.layers:
+            x = attn(x) + x
+            x = ff(x) + x
+        return x
+
+class NesT(nn.Module):
+    def __init__(
+        self,
+        *,
+        image_size,
+        patch_size,
+        num_classes,
+        dim,
+        heads,
+        num_hierarchies,
+        block_repeats,
+        mlp_mult = 4,
+        channels = 3,
+        dim_head = 64,
+        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
+        fmap_size = image_size // patch_size
+        blocks = 2 ** (num_hierarchies - 1)
+
+        seq_len = (fmap_size // blocks) ** 2   # sequence length is held constant across heirarchy
+        hierarchies = list(reversed(range(num_hierarchies)))
+        mults = [2 ** i for i in hierarchies]
+
+        layer_heads = list(map(lambda t: t * heads, mults))
+        layer_dims = list(map(lambda t: t * dim, mults))
+
+        layer_dims = [*layer_dims, layer_dims[-1]]
+        dim_pairs = zip(layer_dims[:-1], layer_dims[1:])
+
+        self.to_patch_embedding = nn.Sequential(
+            Rearrange('b c (h p1) (w p2) -> b (p1 p2 c) h w', p1 = patch_size, p2 = patch_size),
+            nn.Conv2d(patch_dim, layer_dims[0], 1),
+        )
+
+        block_repeats = cast_tuple(block_repeats, num_hierarchies)
+
+        self.layers = nn.ModuleList([])
+
+        for level, heads, (dim_in, dim_out), block_repeat in zip(hierarchies, layer_heads, dim_pairs, block_repeats):
+            is_last = level == 0
+            depth = block_repeat
+
+            self.layers.append(nn.ModuleList([
+                Transformer(dim_in, seq_len, depth, heads, mlp_mult, dropout),
+                Aggregate(dim_in, dim_out) if not is_last else nn.Identity()
+            ]))
+
+        self.mlp_head = nn.Sequential(
+            ChanNorm(dim),
+            Reduce('b c h w -> b c', 'mean'),
+            nn.Linear(dim, num_classes)
+        )
+
+    def forward(self, img):
+        x = self.to_patch_embedding(img)
+        b, c, h, w = x.shape
+
+        num_hierarchies = len(self.layers)
+
+        for level, (transformer, aggregate) in zip(reversed(range(num_hierarchies)), self.layers):
+            block_size = 2 ** level
+            x = rearrange(x, 'b c (b1 h) (b2 w) -> (b b1 b2) c h w', b1 = block_size, b2 = block_size)
+            x = transformer(x)
+            x = rearrange(x, '(b b1 b2) c h w -> b c (b1 h) (b2 w)', b1 = block_size, b2 = block_size)
+            x = aggregate(x)
+
+        return self.mlp_head(x)

vit_pytorch/pit.py

@@ -89,8 +89,8 @@ 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)
+            nn.Conv2d(dim_in, dim_out, kernel_size = kernel_size, padding = padding, groups = dim_in, stride = stride, bias = bias),
+            nn.Conv2d(dim_out, dim_out, kernel_size = 1, bias = bias)
         )
     def forward(self, x):
         return self.net(x)
@@ -162,8 +162,9 @@ class PiT(nn.Module):
                 layers.append(Pool(dim))
                 dim *= 2
 
-        self.layers = nn.Sequential(
-            *layers,
+        self.layers = nn.Sequential(*layers)
+
+        self.mlp_head = nn.Sequential(
             nn.LayerNorm(dim),
             nn.Linear(dim, num_classes)
         )
@@ -177,4 +178,6 @@ class PiT(nn.Module):
         x += self.pos_embedding
         x = self.dropout(x)
 
-        return self.layers(x)
+        x = self.layers(x)
+
+        return self.mlp_head(x[:, 0])

vit_pytorch/rvt.py

@@ -67,12 +67,14 @@ class SpatialConv(nn.Module):
     def __init__(self, dim_in, dim_out, kernel, bias = False):
         super().__init__()
         self.conv = DepthWiseConv2d(dim_in, dim_out, kernel, padding = kernel // 2, bias = False)
+        self.cls_proj = nn.Linear(dim_in, dim_out) if dim_in != dim_out else nn.Identity()
 
     def forward(self, x, fmap_dims):
         cls_token, x = x[:, :1], x[:, 1:]
         x = rearrange(x, 'b (h w) d -> b d h w', **fmap_dims)
         x = self.conv(x)
         x = rearrange(x, 'b d h w -> b (h w) d')
+        cls_token = self.cls_proj(cls_token)
         return torch.cat((cls_token, x), dim = 1)
 
 class GEGLU(nn.Module):
@@ -81,11 +83,11 @@ class GEGLU(nn.Module):
         return F.gelu(gates) * x
 
 class FeedForward(nn.Module):
-    def __init__(self, dim, hidden_dim, dropout = 0.):
+    def __init__(self, dim, hidden_dim, dropout = 0., use_glu = True):
         super().__init__()
         self.net = nn.Sequential(
-            nn.Linear(dim, hidden_dim * 2),
-            GEGLU(),
+            nn.Linear(dim, hidden_dim * 2 if use_glu else hidden_dim),
+            GEGLU() if use_glu else nn.GELU(),
             nn.Dropout(dropout),
             nn.Linear(hidden_dim, dim),
             nn.Dropout(dropout)
@@ -94,16 +96,18 @@ class FeedForward(nn.Module):
         return self.net(x)
 
 class Attention(nn.Module):
-    def __init__(self, dim, heads = 8, dim_head = 64, dropout = 0., conv_query_kernel = 5):
+    def __init__(self, dim, heads = 8, dim_head = 64, dropout = 0., use_rotary = True, use_ds_conv = True, conv_query_kernel = 5):
         super().__init__()
         inner_dim = dim_head *  heads
-
+        self.use_rotary = use_rotary
         self.heads = heads
         self.scale = dim_head ** -0.5
 
         self.attend = nn.Softmax(dim = -1)
 
-        self.to_q = SpatialConv(dim, inner_dim, conv_query_kernel, bias = False)
+        self.use_ds_conv = use_ds_conv
+
+        self.to_q = SpatialConv(dim, inner_dim, conv_query_kernel, bias = False) if use_ds_conv else nn.Linear(dim, inner_dim, bias = False)
 
         self.to_kv = nn.Linear(dim, inner_dim * 2, bias = False)
 
@@ -115,21 +119,31 @@ class Attention(nn.Module):
     def forward(self, x, pos_emb, fmap_dims):
         b, n, _, h = *x.shape, self.heads
 
-        q = self.to_q(x, fmap_dims = fmap_dims)
+        to_q_kwargs = {'fmap_dims': fmap_dims} if self.use_ds_conv else {}
+        q = self.to_q(x, **to_q_kwargs)
+
         qkv = (q, *self.to_kv(x).chunk(2, 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
+        if self.use_rotary:
+            # 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))
+            sin, cos = pos_emb
+            dim_rotary = sin.shape[-1]
 
-        # concat back the CLS tokens
+            (q_cls, q), (k_cls, k) = map(lambda t: (t[:, :1], t[:, 1:]), (q, k))
 
-        q = torch.cat((q_cls, q), dim = 1)
-        k = torch.cat((k_cls, k), dim = 1)
+            # handle the case where rotary dimension < head dimension
+
+            (q, q_pass), (k, k_pass) = map(lambda t: (t[..., :dim_rotary], t[..., dim_rotary:]), (q, k))
+            q, k = map(lambda t: (t * cos) + (rotate_every_two(t) * sin), (q, k))
+            q, k = map(lambda t: torch.cat(t, dim = -1), ((q, q_pass), (k, k_pass)))
+
+            # 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
 
@@ -140,14 +154,14 @@ class Attention(nn.Module):
         return self.to_out(out)
 
 class Transformer(nn.Module):
-    def __init__(self, dim, depth, heads, dim_head, mlp_dim, dropout = 0.):
+    def __init__(self, dim, depth, heads, dim_head, mlp_dim, dropout = 0., use_rotary = True, use_ds_conv = True, use_glu = True):
         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))
+                PreNorm(dim, Attention(dim, heads = heads, dim_head = dim_head, dropout = dropout, use_rotary = use_rotary, use_ds_conv = use_ds_conv)),
+                PreNorm(dim, FeedForward(dim, mlp_dim, dropout = dropout, use_glu = use_glu))
             ]))
     def forward(self, x, fmap_dims):
         pos_emb = self.pos_emb(x[:, 1:])
@@ -160,7 +174,7 @@ class Transformer(nn.Module):
 # 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.):
+    def __init__(self, *, image_size, patch_size, num_classes, dim, depth, heads, mlp_dim, channels = 3, dim_head = 64, dropout = 0., emb_dropout = 0., use_rotary = True, use_ds_conv = True, use_glu = True):
         super().__init__()
         assert image_size % patch_size == 0, 'Image dimensions must be divisible by the patch size.'
         num_patches = (image_size // patch_size) ** 2
@@ -173,7 +187,7 @@ class RvT(nn.Module):
         )
 
         self.cls_token = nn.Parameter(torch.randn(1, 1, dim))
-        self.transformer = Transformer(dim, depth, heads, dim_head, mlp_dim, dropout)
+        self.transformer = Transformer(dim, depth, heads, dim_head, mlp_dim, dropout, use_rotary, use_ds_conv, use_glu)
 
         self.mlp_head = nn.Sequential(
             nn.LayerNorm(dim),
@@ -192,4 +206,4 @@ class RvT(nn.Module):
         fmap_dims = {'h': h // p, 'w': w // p}
         x = self.transformer(x, fmap_dims = fmap_dims)
 
-        return self.mlp_head(x)
+        return self.mlp_head(x[:, 0])

vit_pytorch/twins_svt.py

@@ -0,0 +1,229 @@
+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 Residual(nn.Module):
+    def __init__(self, fn):
+        super().__init__()
+        self.fn = fn
+
+    def forward(self, x, **kwargs):
+        return self.fn(x, **kwargs) + x
+
+class LayerNorm(nn.Module):
+    def __init__(self, dim, eps = 1e-5):
+        super().__init__()
+        self.eps = eps
+        self.g = nn.Parameter(torch.ones(1, dim, 1, 1))
+        self.b = nn.Parameter(torch.zeros(1, dim, 1, 1))
+
+    def forward(self, x):
+        std = torch.var(x, dim = 1, unbiased = False, keepdim = True).sqrt()
+        mean = torch.mean(x, dim = 1, keepdim = True)
+        return (x - mean) / (std + self.eps) * self.g + self.b
+
+class PreNorm(nn.Module):
+    def __init__(self, dim, fn):
+        super().__init__()
+        self.norm = LayerNorm(dim)
+        self.fn = fn
+
+    def forward(self, x, **kwargs):
+        x = self.norm(x)
+        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 PatchEmbedding(nn.Module):
+    def __init__(self, *, dim, dim_out, patch_size):
+        super().__init__()
+        self.dim = dim
+        self.dim_out = dim_out
+        self.patch_size = patch_size
+        self.proj = nn.Conv2d(patch_size ** 2 * dim, dim_out, 1)
+
+    def forward(self, fmap):
+        p = self.patch_size
+        fmap = rearrange(fmap, 'b c (h p1) (w p2) -> b (c p1 p2) h w', p1 = p, p2 = p)
+        return self.proj(fmap)
+
+class PEG(nn.Module):
+    def __init__(self, dim, kernel_size = 3):
+        super().__init__()
+        self.proj = Residual(nn.Conv2d(dim, dim, kernel_size = kernel_size, padding = kernel_size // 2, groups = dim, stride = 1))
+
+    def forward(self, x):
+        return self.proj(x)
+
+class LocalAttention(nn.Module):
+    def __init__(self, dim, heads = 8, dim_head = 64, dropout = 0., patch_size = 7):
+        super().__init__()
+        inner_dim = dim_head *  heads
+        self.patch_size = patch_size
+        self.heads = heads
+        self.scale = dim_head ** -0.5
+
+        self.to_q = nn.Conv2d(dim, inner_dim, 1, bias = False)
+        self.to_kv = nn.Conv2d(dim, inner_dim * 2, 1, bias = False)
+
+        self.to_out = nn.Sequential(
+            nn.Conv2d(inner_dim, dim, 1),
+            nn.Dropout(dropout)
+        )
+
+    def forward(self, fmap):
+        shape, p = fmap.shape, self.patch_size
+        b, n, x, y, h = *shape, self.heads
+        x, y = map(lambda t: t // p, (x, y))
+
+        fmap = rearrange(fmap, 'b c (x p1) (y p2) -> (b x y) c p1 p2', p1 = p, p2 = p)
+
+        q, k, v = (self.to_q(fmap), *self.to_kv(fmap).chunk(2, dim = 1))
+        q, k, v = map(lambda t: rearrange(t, 'b (h d) p1 p2 -> (b h) (p1 p2) d', h = h), (q, k, v))
+
+        dots = einsum('b i d, b j d -> b i j', q, k) * self.scale
+
+        attn = dots.softmax(dim = - 1)
+
+        out = einsum('b i j, b j d -> b i d', attn, v)
+        out = rearrange(out, '(b x y h) (p1 p2) d -> b (h d) (x p1) (y p2)', h = h, x = x, y = y, p1 = p, p2 = p)
+        return self.to_out(out)
+
+class GlobalAttention(nn.Module):
+    def __init__(self, dim, heads = 8, dim_head = 64, dropout = 0., k = 7):
+        super().__init__()
+        inner_dim = dim_head *  heads
+        self.heads = heads
+        self.scale = dim_head ** -0.5
+
+        self.to_q = nn.Conv2d(dim, inner_dim, 1, bias = False)
+        self.to_kv = nn.Conv2d(dim, inner_dim * 2, k, stride = k, 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 = dots.softmax(dim = -1)
+
+        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, depth, heads = 8, dim_head = 64, mlp_mult = 4, local_patch_size = 7, global_k = 7, dropout = 0., has_local = True):
+        super().__init__()
+        self.layers = nn.ModuleList([])
+        for _ in range(depth):
+            self.layers.append(nn.ModuleList([
+                Residual(PreNorm(dim, LocalAttention(dim, heads = heads, dim_head = dim_head, dropout = dropout, patch_size = local_patch_size))) if has_local else nn.Identity(),
+                Residual(PreNorm(dim, FeedForward(dim, mlp_mult, dropout = dropout))) if has_local else nn.Identity(),
+                Residual(PreNorm(dim, GlobalAttention(dim, heads = heads, dim_head = dim_head, dropout = dropout, k = global_k))),
+                Residual(PreNorm(dim, FeedForward(dim, mlp_mult, dropout = dropout)))
+            ]))
+    def forward(self, x):
+        for local_attn, ff1, global_attn, ff2 in self.layers:
+            x = local_attn(x)
+            x = ff1(x)
+            x = global_attn(x)
+            x = ff2(x)
+        return x
+
+class TwinsSVT(nn.Module):
+    def __init__(
+        self,
+        *,
+        num_classes,
+        s1_emb_dim = 64,
+        s1_patch_size = 4,
+        s1_local_patch_size = 7,
+        s1_global_k = 7,
+        s1_depth = 1,
+        s2_emb_dim = 128,
+        s2_patch_size = 2,
+        s2_local_patch_size = 7,
+        s2_global_k = 7,
+        s2_depth = 1,
+        s3_emb_dim = 256,
+        s3_patch_size = 2,
+        s3_local_patch_size = 7,
+        s3_global_k = 7,
+        s3_depth = 5,
+        s4_emb_dim = 512,
+        s4_patch_size = 2,
+        s4_local_patch_size = 7,
+        s4_global_k = 7,
+        s4_depth = 4,
+        peg_kernel_size = 3,
+        dropout = 0.
+    ):
+        super().__init__()
+        kwargs = dict(locals())
+
+        dim = 3
+        layers = []
+
+        for prefix in ('s1', 's2', 's3', 's4'):
+            config, kwargs = group_by_key_prefix_and_remove_prefix(f'{prefix}_', kwargs)
+            is_last = prefix == 's4'
+
+            dim_next = config['emb_dim']
+
+            layers.append(nn.Sequential(
+                PatchEmbedding(dim = dim, dim_out = dim_next, patch_size = config['patch_size']),
+                Transformer(dim = dim_next, depth = 1, local_patch_size = config['local_patch_size'], global_k = config['global_k'], dropout = dropout, has_local = not is_last),
+                PEG(dim = dim_next, kernel_size = peg_kernel_size),
+                Transformer(dim = dim_next, depth = config['depth'],  local_patch_size = config['local_patch_size'], global_k = config['global_k'], dropout = dropout, has_local = not is_last)
+            ))
+
+            dim = dim_next
+
+        self.layers = nn.Sequential(
+            *layers,
+            nn.AdaptiveAvgPool2d(1),
+            Rearrange('... () () -> ...'),
+            nn.Linear(dim, num_classes)
+        )
+
+    def forward(self, x):
+        return self.layers(x)

vit_pytorch/vit.py

@@ -5,6 +5,13 @@ import torch.nn.functional as F
 from einops import rearrange, repeat
 from einops.layers.torch import Rearrange
 
+# helpers
+
+def pair(t):
+    return t if isinstance(t, tuple) else (t, t)
+
+# classes
+
 class PreNorm(nn.Module):
     def __init__(self, dim, fn):
         super().__init__()
@@ -74,13 +81,17 @@ class Transformer(nn.Module):
 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
+        image_height, image_width = pair(image_size)
+        patch_height, patch_width = pair(patch_size)
+
+        assert image_height % patch_height == 0 and image_width % patch_width == 0, 'Image dimensions must be divisible by the patch size.'
+
+        num_patches = (image_height // patch_height) * (image_width // patch_width)
+        patch_dim = channels * patch_height * patch_width
         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),
+            Rearrange('b c (h p1) (w p2) -> b (h w) (p1 p2 c)', p1 = patch_height, p2 = patch_width),
             nn.Linear(patch_dim, dim),
         )