fix mpp

* Create mp3.py

* Implementation: Position Prediction as an Effective Pretraining Strategy

* Added description for Masked Position Prediction

* MP3 image added

.github/workflows/python-test.yml

@@ -27,6 +27,7 @@ jobs:
       run: |
         python -m pip install --upgrade pip
         python -m pip install pytest
+        python -m pip install wheel
         if [ -f requirements.txt ]; then pip install -r requirements.txt; fi
     - name: Test with pytest
       run: |

README.md

@@ -27,6 +27,7 @@
 - [Masked Autoencoder](#masked-autoencoder)
 - [Simple Masked Image Modeling](#simple-masked-image-modeling)
 - [Masked Patch Prediction](#masked-patch-prediction)
+- [Masked Position Prediction](#masked-position-prediction)
 - [Adaptive Token Sampling](#adaptive-token-sampling)
 - [Patch Merger](#patch-merger)
 - [Vision Transformer for Small Datasets](#vision-transformer-for-small-datasets)
@@ -303,7 +304,7 @@ cct = CCT(
     pooling_padding = 1,
     num_layers = 14,
     num_heads = 6,
-    mlp_radio = 3.,
+    mlp_ratio = 3.,
     num_classes = 1000,
     positional_embedding = 'learnable', # ['sine', 'learnable', 'none']
 )
@@ -844,6 +845,44 @@ for _ in range(100):
 torch.save(model.state_dict(), './pretrained-net.pt')
 ```
 
+## Masked Position Prediction
+
+<img src="./images/mp3.png" width="400px"></img>
+
+New <a href="https://arxiv.org/abs/2207.07611">paper</a> that introduces masked position prediction pre-training criteria. This strategy is more efficient than the Masked Autoencoder strategy and has comparable performance.  
+
+```python
+import torch
+from vit_pytorch.mp3 import ViT, MP3
+
+v = ViT(
+    num_classes = 1000,
+    image_size = 256,
+    patch_size = 8,
+    dim = 1024,
+    depth = 6,
+    heads = 8,
+    mlp_dim = 2048,
+    dropout = 0.1,
+)
+
+mp3 = MP3(
+    vit = v,
+    masking_ratio = 0.75
+)
+
+images = torch.randn(8, 3, 256, 256)
+
+loss = mp3(images)
+loss.backward()
+
+# that's all!
+# do the above in a for loop many times with a lot of images and your vision transformer will learn
+
+# save your improved vision transformer
+torch.save(v.state_dict(), './trained-vit.pt')
+```
+
 ## Adaptive Token Sampling
 
 <img src="./images/ats.png" width="400px"></img>
@@ -1043,7 +1082,7 @@ cct = CCT(
     pooling_padding = 1,
     num_layers = 14,
     num_heads = 6,
-    mlp_radio = 3.,
+    mlp_ratio = 3.,
     num_classes = 1000,
     positional_embedding = 'learnable'
 )
@@ -1873,6 +1912,28 @@ Coming from computer vision and new to transformers? Here are some resources tha
 }
 ```
 
+```bibtex
+@article{Liu2022PatchDropoutEV,
+    title   = {PatchDropout: Economizing Vision Transformers Using Patch Dropout},
+    author  = {Yue Liu and Christos Matsoukas and Fredrik Strand and Hossein Azizpour and Kevin Smith},
+    journal = {ArXiv},
+    year    = {2022},
+    volume  = {abs/2208.07220}
+}
+```
+
+```bibtex
+@misc{https://doi.org/10.48550/arxiv.2302.01327,
+    doi     = {10.48550/ARXIV.2302.01327},
+    url     = {https://arxiv.org/abs/2302.01327},
+    author  = {Kumar, Manoj and Dehghani, Mostafa and Houlsby, Neil},
+    title   = {Dual PatchNorm},
+    publisher = {arXiv},
+    year    = {2023},
+    copyright = {Creative Commons Attribution 4.0 International}
+}
+```
+
 ```bibtex
 @misc{vaswani2017attention,
     title   = {Attention Is All You Need},
@@ -1885,12 +1946,11 @@ Coming from computer vision and new to transformers? Here are some resources tha
 ```
 
 ```bibtex
-@article{Liu2022PatchDropoutEV,
-    title   = {PatchDropout: Economizing Vision Transformers Using Patch Dropout},
-    author  = {Yue Liu and Christos Matsoukas and Fredrik Strand and Hossein Azizpour and Kevin Smith},
-    journal = {ArXiv},
-    year    = {2022},
-    volume  = {abs/2208.07220}
+@inproceedings{dao2022flashattention,
+    title   = {Flash{A}ttention: Fast and Memory-Efficient Exact Attention with {IO}-Awareness},
+    author  = {Dao, Tri and Fu, Daniel Y. and Ermon, Stefano and Rudra, Atri and R{\'e}, Christopher},
+    booktitle = {Advances in Neural Information Processing Systems},
+    year    = {2022}
 }
 ```
 

setup.py

@@ -3,7 +3,7 @@ from setuptools import setup, find_packages
 setup(
   name = 'vit-pytorch',
   packages = find_packages(exclude=['examples']),
-  version = '0.40.0',
+  version = '1.2.4',
   license='MIT',
   description = 'Vision Transformer (ViT) - Pytorch',
   long_description_content_type = 'text/markdown',
@@ -16,7 +16,7 @@ setup(
     'image recognition'
   ],
   install_requires=[
-    'einops>=0.6.0',
+    'einops>=0.6.1',
     'torch>=1.10',
     'torchvision'
   ],

vit_pytorch/__init__.py

@@ -1,3 +1,10 @@
+import torch
+from packaging import version
+
+if version.parse(torch.__version__) >= version.parse('2.0.0'):
+    from einops._torch_specific import allow_ops_in_compiled_graph
+    allow_ops_in_compiled_graph()
+
 from vit_pytorch.vit import ViT
 from vit_pytorch.simple_vit import SimpleViT
 

vit_pytorch/ats_vit.py

@@ -230,7 +230,9 @@ class ViT(nn.Module):
 
         self.to_patch_embedding = nn.Sequential(
             Rearrange('b c (h p1) (w p2) -> b (h w) (p1 p2 c)', p1 = patch_height, p2 = patch_width),
+            nn.LayerNorm(patch_dim),
             nn.Linear(patch_dim, dim),
+            nn.LayerNorm(dim)
         )
 
         self.pos_embedding = nn.Parameter(torch.randn(1, num_patches + 1, dim))

vit_pytorch/cait.py

@@ -150,7 +150,9 @@ class CaiT(nn.Module):
 
         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.LayerNorm(patch_dim),
             nn.Linear(patch_dim, dim),
+            nn.LayerNorm(dim)
         )
 
         self.pos_embedding = nn.Parameter(torch.randn(1, num_patches, dim))

vit_pytorch/cross_vit.py

@@ -186,7 +186,9 @@ class ImageEmbedder(nn.Module):
 
         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.LayerNorm(patch_dim),
             nn.Linear(patch_dim, dim),
+            nn.LayerNorm(dim)
         )
 
         self.pos_embedding = nn.Parameter(torch.randn(1, num_patches + 1, dim))

vit_pytorch/deepvit.py

@@ -105,7 +105,9 @@ class DeepViT(nn.Module):
 
         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.LayerNorm(patch_dim),
             nn.Linear(patch_dim, dim),
+            nn.LayerNorm(dim)
         )
 
         self.pos_embedding = nn.Parameter(torch.randn(1, num_patches + 1, dim))

vit_pytorch/efficient.py

@@ -17,7 +17,9 @@ class ViT(nn.Module):
 
         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.LayerNorm(patch_dim),
             nn.Linear(patch_dim, dim),
+            nn.LayerNorm(dim)
         )
 
         self.pos_embedding = nn.Parameter(torch.randn(1, num_patches + 1, dim))

vit_pytorch/learnable_memory_vit.py

@@ -118,7 +118,9 @@ class ViT(nn.Module):
 
         self.to_patch_embedding = nn.Sequential(
             Rearrange('b c (h p1) (w p2) -> b (h w) (p1 p2 c)', p1 = patch_height, p2 = patch_width),
+            nn.LayerNorm(patch_dim),
             nn.Linear(patch_dim, dim),
+            nn.LayerNorm(dim)
         )
 
         self.pos_embedding = nn.Parameter(torch.randn(1, num_patches + 1, dim))

vit_pytorch/local_vit.py

@@ -126,7 +126,9 @@ class LocalViT(nn.Module):
 
         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.LayerNorm(patch_dim),
             nn.Linear(patch_dim, dim),
+            nn.LayerNorm(dim),
         )
 
         self.pos_embedding = nn.Parameter(torch.randn(1, num_patches + 1, dim))

vit_pytorch/mae.py

@@ -24,8 +24,11 @@ class MAE(nn.Module):
 
         self.encoder = encoder
         num_patches, encoder_dim = encoder.pos_embedding.shape[-2:]
-        self.to_patch, self.patch_to_emb = encoder.to_patch_embedding[:2]
-        pixel_values_per_patch = self.patch_to_emb.weight.shape[-1]
+
+        self.to_patch = encoder.to_patch_embedding[0]
+        self.patch_to_emb = nn.Sequential(*encoder.to_patch_embedding[1:])
+
+        pixel_values_per_patch = encoder.to_patch_embedding[2].weight.shape[-1]
 
         # decoder parameters
         self.decoder_dim = decoder_dim

vit_pytorch/mp3.py

@@ -0,0 +1,186 @@
+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 pair(t):
+    return t if isinstance(t, tuple) else (t, t)
+
+# positional embedding
+
+def posemb_sincos_2d(patches, temperature = 10000, dtype = torch.float32):
+    _, h, w, dim, device, dtype = *patches.shape, patches.device, patches.dtype
+
+    y, x = torch.meshgrid(torch.arange(h, device = device), torch.arange(w, device = device), indexing = 'ij')
+    assert (dim % 4) == 0, 'feature dimension must be multiple of 4 for sincos emb'
+    omega = torch.arange(dim // 4, device = device) / (dim // 4 - 1)
+    omega = 1. / (temperature ** omega)
+
+    y = y.flatten()[:, None] * omega[None, :]
+    x = x.flatten()[:, None] * omega[None, :]
+    pe = torch.cat((x.sin(), x.cos(), y.sin(), y.cos()), dim = 1)
+    return pe.type(dtype)
+
+# feedforward
+
+class FeedForward(nn.Module):
+    def __init__(self, dim, hidden_dim, dropout = 0.):
+        super().__init__()
+        self.net = nn.Sequential(
+            nn.LayerNorm(dim),
+            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)
+
+# (cross)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.dropout = nn.Dropout(dropout)
+
+        self.norm = nn.LayerNorm(dim)
+
+        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):
+        b, n, _, h = *x.shape, self.heads
+
+        x = self.norm(x)
+
+        context = self.norm(context) if exists(context) else x
+
+        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)
+        attn = self.dropout(attn)
+
+        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([
+                Attention(dim, heads = heads, dim_head = dim_head, dropout = dropout),
+                FeedForward(dim, mlp_dim, dropout = dropout)
+            ]))
+    def forward(self, x, context = None):
+        for attn, ff in self.layers:
+            x = attn(x, context = context) + x
+            x = ff(x) + x
+        return x
+
+class ViT(nn.Module):
+    def __init__(self, *, num_classes, image_size, patch_size, dim, depth, heads, mlp_dim, channels = 3, dim_head = 64, dropout = 0.):
+        super().__init__()
+        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
+
+        self.dim = dim
+        self.num_patches = num_patches
+
+        self.to_patch_embedding = nn.Sequential(
+            Rearrange('b c (h p1) (w p2) -> b h w (p1 p2 c)', p1 = patch_height, p2 = patch_width),
+            nn.LayerNorm(patch_dim),
+            nn.Linear(patch_dim, dim),
+            nn.LayerNorm(dim),
+        )
+
+        self.transformer = Transformer(dim, depth, heads, dim_head, mlp_dim, dropout)
+
+        self.to_latent = nn.Identity()
+        self.linear_head = nn.Sequential(
+            nn.LayerNorm(dim),
+            nn.Linear(dim, num_classes)
+        )
+
+    def forward(self, img):
+        *_, h, w, dtype = *img.shape, img.dtype
+
+        x = self.to_patch_embedding(img)
+        pe = posemb_sincos_2d(x)
+        x = rearrange(x, 'b ... d -> b (...) d') + pe
+
+        x = self.transformer(x)
+        x = x.mean(dim = 1)
+
+        x = self.to_latent(x)
+        return self.linear_head(x)
+
+# Masked Position Prediction Pre-Training
+
+class MP3(nn.Module):
+    def __init__(self, vit: ViT, masking_ratio):
+        super().__init__()
+        self.vit = vit
+
+        assert masking_ratio > 0 and masking_ratio < 1, 'masking ratio must be kept between 0 and 1'
+        self.masking_ratio = masking_ratio
+
+        dim = vit.dim
+        self.mlp_head = nn.Sequential(
+            nn.LayerNorm(dim),
+            nn.Linear(dim, vit.num_patches)
+        )
+
+    def forward(self, img):
+        device = img.device
+        tokens = self.vit.to_patch_embedding(img)
+        tokens = rearrange(tokens, 'b ... d -> b (...) d')
+
+        batch, num_patches, *_ = tokens.shape
+
+        # Masking
+        num_masked = int(self.masking_ratio * num_patches)
+        rand_indices = torch.rand(batch, num_patches, device = device).argsort(dim = -1)
+        masked_indices, unmasked_indices = rand_indices[:, :num_masked], rand_indices[:, num_masked:]
+
+        batch_range = torch.arange(batch, device = device)[:, None]
+        tokens_unmasked = tokens[batch_range, unmasked_indices]
+
+        attended_tokens = self.vit.transformer(tokens, tokens_unmasked)
+        logits = rearrange(self.mlp_head(attended_tokens), 'b n d -> (b n) d')
+        
+        # Define labels
+        labels = repeat(torch.arange(num_patches, device = device), 'n -> (b n)', b = batch)
+        loss = F.cross_entropy(logits, labels)
+
+        return loss

vit_pytorch/mpp.py

@@ -96,6 +96,9 @@ class MPP(nn.Module):
         self.loss = MPPLoss(patch_size, channels, output_channel_bits,
                             max_pixel_val, mean, std)
 
+        # extract patching function
+        self.patch_to_emb = nn.Sequential(transformer.to_patch_embedding[1:])
+
         # output transformation
         self.to_bits = nn.Linear(dim, 2**(output_channel_bits * channels))
 
@@ -151,7 +154,7 @@ class MPP(nn.Module):
         masked_input[bool_mask_replace] = self.mask_token
 
         # linear embedding of patches
-        masked_input = transformer.to_patch_embedding[-1](masked_input)
+        masked_input = self.patch_to_emb(masked_input)
 
         # add cls token to input sequence
         b, n, _ = masked_input.shape

vit_pytorch/nest.py

@@ -144,7 +144,9 @@ class NesT(nn.Module):
 
         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),
+            LayerNorm(patch_dim),
             nn.Conv2d(patch_dim, layer_dims[0], 1),
+            LayerNorm(layer_dims[0])
         )
 
         block_repeats = cast_tuple(block_repeats, num_hierarchies)

vit_pytorch/simmim.py

@@ -18,8 +18,11 @@ class SimMIM(nn.Module):
 
         self.encoder = encoder
         num_patches, encoder_dim = encoder.pos_embedding.shape[-2:]
-        self.to_patch, self.patch_to_emb = encoder.to_patch_embedding[:2]
-        pixel_values_per_patch = self.patch_to_emb.weight.shape[-1]
+
+        self.to_patch = encoder.to_patch_embedding[0]
+        self.patch_to_emb = nn.Sequential(*encoder.to_patch_embedding[1:])
+
+        pixel_values_per_patch = encoder.to_patch_embedding[2].weight.shape[-1]
 
         # simple linear head
 

vit_pytorch/simple_flash_attn_vit.py

@@ -0,0 +1,176 @@
+from collections import namedtuple
+from packaging import version
+
+import torch
+import torch.nn.functional as F
+from torch import nn
+
+from einops import rearrange
+from einops.layers.torch import Rearrange
+
+# constants
+
+Config = namedtuple('FlashAttentionConfig', ['enable_flash', 'enable_math', 'enable_mem_efficient'])
+
+# helpers
+
+def pair(t):
+    return t if isinstance(t, tuple) else (t, t)
+
+def posemb_sincos_2d(patches, temperature = 10000, dtype = torch.float32):
+    _, h, w, dim, device, dtype = *patches.shape, patches.device, patches.dtype
+
+    y, x = torch.meshgrid(torch.arange(h, device = device), torch.arange(w, device = device), indexing = 'ij')
+    assert (dim % 4) == 0, 'feature dimension must be multiple of 4 for sincos emb'
+    omega = torch.arange(dim // 4, device = device) / (dim // 4 - 1)
+    omega = 1. / (temperature ** omega)
+
+    y = y.flatten()[:, None] * omega[None, :]
+    x = x.flatten()[:, None] * omega[None, :] 
+    pe = torch.cat((x.sin(), x.cos(), y.sin(), y.cos()), dim = 1)
+    return pe.type(dtype)
+
+# main class
+
+class Attend(nn.Module):
+    def __init__(self, use_flash = False):
+        super().__init__()
+        self.use_flash = use_flash
+        assert not (use_flash and version.parse(torch.__version__) < version.parse('2.0.0')), 'in order to use flash attention, you must be using pytorch 2.0 or above'
+
+        # determine efficient attention configs for cuda and cpu
+
+        self.cpu_config = Config(True, True, True)
+        self.cuda_config = None
+
+        if not torch.cuda.is_available() or not use_flash:
+            return
+
+        device_properties = torch.cuda.get_device_properties(torch.device('cuda'))
+
+        if device_properties.major == 8 and device_properties.minor == 0:
+            self.cuda_config = Config(True, False, False)
+        else:
+            self.cuda_config = Config(False, True, True)
+
+    def flash_attn(self, q, k, v):
+        config = self.cuda_config if q.is_cuda else self.cpu_config
+
+        # flash attention - https://arxiv.org/abs/2205.14135
+        
+        with torch.backends.cuda.sdp_kernel(**config._asdict()):
+            out = F.scaled_dot_product_attention(q, k, v)
+
+        return out
+
+    def forward(self, q, k, v):
+        n, device, scale = q.shape[-2], q.device, q.shape[-1] ** -0.5
+
+        if self.use_flash:
+            return self.flash_attn(q, k, v)
+
+        # similarity
+
+        sim = einsum("b h i d, b j d -> b h i j", q, k) * scale
+
+        # attention
+
+        attn = sim.softmax(dim=-1)
+
+        # aggregate values
+
+        out = einsum("b h i j, b j d -> b h i d", attn, v)
+
+        return out
+
+# classes
+
+class FeedForward(nn.Module):
+    def __init__(self, dim, hidden_dim):
+        super().__init__()
+        self.net = nn.Sequential(
+            nn.LayerNorm(dim),
+            nn.Linear(dim, hidden_dim),
+            nn.GELU(),
+            nn.Linear(hidden_dim, dim),
+        )
+    def forward(self, x):
+        return self.net(x)
+
+class Attention(nn.Module):
+    def __init__(self, dim, heads = 8, dim_head = 64, use_flash = True):
+        super().__init__()
+        inner_dim = dim_head *  heads
+        self.heads = heads
+        self.scale = dim_head ** -0.5
+        self.norm = nn.LayerNorm(dim)
+
+        self.attend = Attend(use_flash = use_flash)
+
+        self.to_qkv = nn.Linear(dim, inner_dim * 3, bias = False)
+        self.to_out = nn.Linear(inner_dim, dim, bias = False)
+
+    def forward(self, x):
+        x = self.norm(x)
+
+        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 = self.heads), qkv)
+
+        out = self.attend(q, k, 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, use_flash):
+        super().__init__()
+        self.layers = nn.ModuleList([])
+        for _ in range(depth):
+            self.layers.append(nn.ModuleList([
+                Attention(dim, heads = heads, dim_head = dim_head, use_flash = use_flash),
+                FeedForward(dim, mlp_dim)
+            ]))
+    def forward(self, x):
+        for attn, ff in self.layers:
+            x = attn(x) + x
+            x = ff(x) + x
+        return x
+
+class SimpleViT(nn.Module):
+    def __init__(self, *, image_size, patch_size, num_classes, dim, depth, heads, mlp_dim, channels = 3, dim_head = 64, use_flash = True):
+        super().__init__()
+        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
+
+        self.to_patch_embedding = nn.Sequential(
+            Rearrange('b c (h p1) (w p2) -> b h w (p1 p2 c)', p1 = patch_height, p2 = patch_width),
+            nn.LayerNorm(patch_dim),
+            nn.Linear(patch_dim, dim),
+            nn.LayerNorm(dim),
+        )
+
+        self.transformer = Transformer(dim, depth, heads, dim_head, mlp_dim, use_flash)
+
+        self.to_latent = nn.Identity()
+        self.linear_head = nn.Sequential(
+            nn.LayerNorm(dim),
+            nn.Linear(dim, num_classes)
+        )
+
+    def forward(self, img):
+        *_, h, w, dtype = *img.shape, img.dtype
+
+        x = self.to_patch_embedding(img)
+        pe = posemb_sincos_2d(x)
+        x = rearrange(x, 'b ... d -> b (...) d') + pe
+
+        x = self.transformer(x)
+        x = x.mean(dim = 1)
+
+        x = self.to_latent(x)
+        return self.linear_head(x)

vit_pytorch/simple_vit.py

@@ -91,7 +91,9 @@ class SimpleViT(nn.Module):
 
         self.to_patch_embedding = nn.Sequential(
             Rearrange('b c (h p1) (w p2) -> b h w (p1 p2 c)', p1 = patch_height, p2 = patch_width),
+            nn.LayerNorm(patch_dim),
             nn.Linear(patch_dim, dim),
+            nn.LayerNorm(dim),
         )
 
         self.transformer = Transformer(dim, depth, heads, dim_head, mlp_dim)

vit_pytorch/simple_vit_1d.py

@@ -85,7 +85,9 @@ class SimpleViT(nn.Module):
 
         self.to_patch_embedding = nn.Sequential(
             Rearrange('b c (n p) -> b n (p c)', p = patch_size),
+            nn.LayerNorm(patch_dim),
             nn.Linear(patch_dim, dim),
+            nn.LayerNorm(dim),
         )
 
         self.transformer = Transformer(dim, depth, heads, dim_head, mlp_dim)

vit_pytorch/simple_vit_3d.py

@@ -103,7 +103,9 @@ class SimpleViT(nn.Module):
 
         self.to_patch_embedding = nn.Sequential(
             Rearrange('b c (f pf) (h p1) (w p2) -> b f h w (p1 p2 pf c)', p1 = patch_height, p2 = patch_width, pf = frame_patch_size),
+            nn.LayerNorm(patch_dim),
             nn.Linear(patch_dim, dim),
+            nn.LayerNorm(dim),
         )
 
         self.transformer = Transformer(dim, depth, heads, dim_head, mlp_dim)

vit_pytorch/simple_vit_with_patch_dropout.py

@@ -0,0 +1,143 @@
+import torch
+from torch import nn
+
+from einops import rearrange
+from einops.layers.torch import Rearrange
+
+# helpers
+
+def pair(t):
+    return t if isinstance(t, tuple) else (t, t)
+
+def posemb_sincos_2d(patches, temperature = 10000, dtype = torch.float32):
+    _, h, w, dim, device, dtype = *patches.shape, patches.device, patches.dtype
+
+    y, x = torch.meshgrid(torch.arange(h, device = device), torch.arange(w, device = device), indexing = 'ij')
+    assert (dim % 4) == 0, 'feature dimension must be multiple of 4 for sincos emb'
+    omega = torch.arange(dim // 4, device = device) / (dim // 4 - 1)
+    omega = 1. / (temperature ** omega)
+
+    y = y.flatten()[:, None] * omega[None, :]
+    x = x.flatten()[:, None] * omega[None, :] 
+    pe = torch.cat((x.sin(), x.cos(), y.sin(), y.cos()), dim = 1)
+    return pe.type(dtype)
+
+# patch dropout
+
+class PatchDropout(nn.Module):
+    def __init__(self, prob):
+        super().__init__()
+        assert 0 <= prob < 1.
+        self.prob = prob
+
+    def forward(self, x):
+        if not self.training or self.prob == 0.:
+            return x
+
+        b, n, _, device = *x.shape, x.device
+
+        batch_indices = torch.arange(b, device = device)
+        batch_indices = rearrange(batch_indices, '... -> ... 1')
+        num_patches_keep = max(1, int(n * (1 - self.prob)))
+        patch_indices_keep = torch.randn(b, n, device = device).topk(num_patches_keep, dim = -1).indices
+
+        return x[batch_indices, patch_indices_keep]
+
+# classes
+
+class FeedForward(nn.Module):
+    def __init__(self, dim, hidden_dim):
+        super().__init__()
+        self.net = nn.Sequential(
+            nn.LayerNorm(dim),
+            nn.Linear(dim, hidden_dim),
+            nn.GELU(),
+            nn.Linear(hidden_dim, dim),
+        )
+    def forward(self, x):
+        return self.net(x)
+
+class Attention(nn.Module):
+    def __init__(self, dim, heads = 8, dim_head = 64):
+        super().__init__()
+        inner_dim = dim_head *  heads
+        self.heads = heads
+        self.scale = dim_head ** -0.5
+        self.norm = nn.LayerNorm(dim)
+
+        self.attend = nn.Softmax(dim = -1)
+
+        self.to_qkv = nn.Linear(dim, inner_dim * 3, bias = False)
+        self.to_out = nn.Linear(inner_dim, dim, bias = False)
+
+    def forward(self, x):
+        x = self.norm(x)
+
+        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 = self.heads), qkv)
+
+        dots = torch.matmul(q, k.transpose(-1, -2)) * self.scale
+
+        attn = self.attend(dots)
+
+        out = torch.matmul(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):
+        super().__init__()
+        self.layers = nn.ModuleList([])
+        for _ in range(depth):
+            self.layers.append(nn.ModuleList([
+                Attention(dim, heads = heads, dim_head = dim_head),
+                FeedForward(dim, mlp_dim)
+            ]))
+    def forward(self, x):
+        for attn, ff in self.layers:
+            x = attn(x) + x
+            x = ff(x) + x
+        return x
+
+class SimpleViT(nn.Module):
+    def __init__(self, *, image_size, patch_size, num_classes, dim, depth, heads, mlp_dim, channels = 3, dim_head = 64, patch_dropout = 0.5):
+        super().__init__()
+        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
+
+        self.to_patch_embedding = nn.Sequential(
+            Rearrange('b c (h p1) (w p2) -> b h w (p1 p2 c)', p1 = patch_height, p2 = patch_width),
+            nn.LayerNorm(patch_dim),
+            nn.Linear(patch_dim, dim),
+            nn.LayerNorm(dim)
+        )
+
+        self.patch_dropout = PatchDropout(patch_dropout)
+
+        self.transformer = Transformer(dim, depth, heads, dim_head, mlp_dim)
+
+        self.to_latent = nn.Identity()
+        self.linear_head = nn.Sequential(
+            nn.LayerNorm(dim),
+            nn.Linear(dim, num_classes)
+        )
+
+    def forward(self, img):
+        *_, h, w, dtype = *img.shape, img.dtype
+
+        x = self.to_patch_embedding(img)
+        pe = posemb_sincos_2d(x)
+        x = rearrange(x, 'b ... d -> b (...) d') + pe
+
+        x = self.patch_dropout(x)
+
+        x = self.transformer(x)
+        x = x.mean(dim = 1)
+
+        x = self.to_latent(x)
+        return self.linear_head(x)

vit_pytorch/twins_svt.py

@@ -71,7 +71,12 @@ class PatchEmbedding(nn.Module):
         self.dim = dim
         self.dim_out = dim_out
         self.patch_size = patch_size
-        self.proj = nn.Conv2d(patch_size ** 2 * dim, dim_out, 1)
+
+        self.proj = nn.Sequential(
+            LayerNorm(patch_size ** 2 * dim),
+            nn.Conv2d(patch_size ** 2 * dim, dim_out, 1),
+            LayerNorm(dim_out)
+        )
 
     def forward(self, fmap):
         p = self.patch_size

vit_pytorch/vit.py

@@ -93,7 +93,9 @@ class ViT(nn.Module):
 
         self.to_patch_embedding = nn.Sequential(
             Rearrange('b c (h p1) (w p2) -> b (h w) (p1 p2 c)', p1 = patch_height, p2 = patch_width),
+            nn.LayerNorm(patch_dim),
             nn.Linear(patch_dim, dim),
+            nn.LayerNorm(dim),
         )
 
         self.pos_embedding = nn.Parameter(torch.randn(1, num_patches + 1, dim))

vit_pytorch/vit_1d.py

@@ -84,7 +84,9 @@ class ViT(nn.Module):
 
         self.to_patch_embedding = nn.Sequential(
             Rearrange('b c (n p) -> b n (p c)', p = patch_size),
+            nn.LayerNorm(patch_dim),
             nn.Linear(patch_dim, dim),
+            nn.LayerNorm(dim),
         )
 
         self.pos_embedding = nn.Parameter(torch.randn(1, num_patches + 1, dim))

vit_pytorch/vit_3d.py

@@ -95,7 +95,9 @@ class ViT(nn.Module):
 
         self.to_patch_embedding = nn.Sequential(
             Rearrange('b c (f pf) (h p1) (w p2) -> b (f h w) (p1 p2 pf c)', p1 = patch_height, p2 = patch_width, pf = frame_patch_size),
+            nn.LayerNorm(patch_dim),
             nn.Linear(patch_dim, dim),
+            nn.LayerNorm(dim),
         )
 
         self.pos_embedding = nn.Parameter(torch.randn(1, num_patches + 1, dim))

vit_pytorch/vit_with_patch_dropout.py

@@ -25,7 +25,7 @@ class PatchDropout(nn.Module):
 
         batch_indices = torch.arange(b, device = device)
         batch_indices = rearrange(batch_indices, '... -> ... 1')
-        num_patches_keep = max(1, int(n * self.prob))
+        num_patches_keep = max(1, int(n * (1 - self.prob)))
         patch_indices_keep = torch.randn(b, n, device = device).topk(num_patches_keep, dim = -1).indices
 
         return x[batch_indices, patch_indices_keep]

vit_pytorch/vit_with_patch_merger.py

@@ -121,7 +121,9 @@ class ViT(nn.Module):
 
         self.to_patch_embedding = nn.Sequential(
             Rearrange('b c (h p1) (w p2) -> b (h w) (p1 p2 c)', p1 = patch_height, p2 = patch_width),
+            nn.LayerNorm(patch_dim),
             nn.Linear(patch_dim, dim),
+            nn.LayerNorm(dim)
         )
 
         self.pos_embedding = nn.Parameter(torch.randn(1, num_patches + 1, dim))

vit_pytorch/vivit.py

@@ -120,7 +120,9 @@ class ViT(nn.Module):
 
         self.to_patch_embedding = nn.Sequential(
             Rearrange('b c (f pf) (h p1) (w p2) -> b f (h w) (p1 p2 pf c)', p1 = patch_height, p2 = patch_width, pf = frame_patch_size),
+            nn.LayerNorm(patch_dim),
             nn.Linear(patch_dim, dim),
+            nn.LayerNorm(dim)
         )
 
         self.pos_embedding = nn.Parameter(torch.randn(1, num_frame_patches, num_image_patches, dim))
@@ -144,7 +146,7 @@ class ViT(nn.Module):
         x = self.to_patch_embedding(video)
         b, f, n, _ = x.shape
 
-        x = x + self.pos_embedding
+        x = x + self.pos_embedding[:, :f, :n]
 
         if exists(self.spatial_cls_token):
             spatial_cls_tokens = repeat(self.spatial_cls_token, '1 1 d -> b f 1 d', b = b, f = f)