add ViT for small datasets https://arxiv.org/abs/2112.13492

README.md

@@ -24,7 +24,6 @@
 - [Simple Masked Image Modeling](#simple-masked-image-modeling)
 - [Masked Patch Prediction](#masked-patch-prediction)
 - [Adaptive Token Sampling](#adaptive-token-sampling)
-- [Vision Transformer for Small Datasets](#vision-transformer-for-small-datasets)
 - [Dino](#dino)
 - [Accessing Attention](#accessing-attention)
 - [Research Ideas](#research-ideas)
@@ -744,7 +743,7 @@ preds, token_ids = v(img, return_sampled_token_ids = True) # (1, 1000), (1, <=8)
 
 <img src="./images/vit_for_small_datasets.png" width="400px"></img>
 
-This <a href="https://arxiv.org/abs/2112.13492">paper</a> proposes a new image to patch function that incorporates shifts of the image, before normalizing and dividing the image into patches. I have found shifting to be extremely helpful in some other transformers work, so decided to include this for further explorations. It also includes the `LSA` with the learned temperature and masking out of a token's attention to itself.
+This paper proposes a new image to patch function that incorporates shifts of the image, before normalizing and dividing the image into patches. I have found shifting to be extremely helpful in some other transformers work, so decided to include this for further explorations. It also includes the `LRA` with the learned temperature and masking out of token attention to itself.
 
 You can use as follows:
 

setup.py

@@ -3,7 +3,7 @@ from setuptools import setup, find_packages
 setup(
   name = 'vit-pytorch',
   packages = find_packages(exclude=['examples']),
-  version = '0.26.2',
+  version = '0.26.0',
   license='MIT',
   description = 'Vision Transformer (ViT) - Pytorch',
   author = 'Phil Wang',

vit_pytorch/mae.py

@@ -14,13 +14,11 @@ class MAE(nn.Module):
         masking_ratio = 0.75,
         decoder_depth = 1,
         decoder_heads = 8,
-        decoder_dim_head = 64,
-        apply_decoder_pos_emb_all = False # whether to (re)apply decoder positional embedding to encoder unmasked tokens
+        decoder_dim_head = 64
     ):
         super().__init__()
         assert masking_ratio > 0 and masking_ratio < 1, 'masking ratio must be kept between 0 and 1'
         self.masking_ratio = masking_ratio
-        self.apply_decoder_pos_emb_all = apply_decoder_pos_emb_all
 
         # extract some hyperparameters and functions from encoder (vision transformer to be trained)
 
@@ -73,11 +71,6 @@ class MAE(nn.Module):
 
         decoder_tokens = self.enc_to_dec(encoded_tokens)
 
-        # reapply decoder position embedding to unmasked tokens, if desired
-
-        if self.apply_decoder_pos_emb_all:
-            decoder_tokens = decoder_tokens + self.decoder_pos_emb(unmasked_indices)
-
         # repeat mask tokens for number of masked, and add the positions using the masked indices derived above
 
         mask_tokens = repeat(self.mask_token, 'd -> b n d', b = batch, n = num_masked)

vit_pytorch/mobile_vit.py

@@ -1,27 +1,40 @@
+"""
+An implementation of MobileViT Model as defined in:
+MobileViT: Light-weight, General-purpose, and Mobile-friendly Vision Transformer
+Arxiv: https://arxiv.org/abs/2110.02178
+Origin Code: https://github.com/murufeng/awesome_lightweight_networks
+"""
+
 import torch
 import torch.nn as nn
 
 from einops import rearrange
 from einops.layers.torch import Reduce
 
-# helpers
+def _make_divisible(v, divisor, min_value=None):
+    if min_value is None:
+        min_value = divisor
+    new_v = max(min_value, int(v + divisor / 2) // divisor * divisor)
+    if new_v < 0.9 * v:
+        new_v += divisor
+    return new_v
+
+
+def conv_bn_relu(inp, oup, kernel, stride=1):
+    return nn.Sequential(
+        nn.Conv2d(inp, oup, kernel_size=kernel, stride=stride, padding=1, bias=False),
+        nn.BatchNorm2d(oup),
+        nn.ReLU6(inplace=True)
+    )
+
 
 def conv_1x1_bn(inp, oup):
     return nn.Sequential(
         nn.Conv2d(inp, oup, 1, 1, 0, bias=False),
         nn.BatchNorm2d(oup),
-        nn.SiLU()
+        nn.ReLU6(inplace=True)
     )
 
-def conv_nxn_bn(inp, oup, kernal_size=3, stride=1):
-    return nn.Sequential(
-        nn.Conv2d(inp, oup, kernal_size, stride, 1, bias=False),
-        nn.BatchNorm2d(oup),
-        nn.SiLU()
-    )
-
-# classes
-
 class PreNorm(nn.Module):
     def __init__(self, dim, fn):
         super().__init__()
@@ -31,11 +44,10 @@ class PreNorm(nn.Module):
     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(
+        self.ffn = nn.Sequential(
             nn.Linear(dim, hidden_dim),
             nn.SiLU(),
             nn.Dropout(dropout),
@@ -44,7 +56,8 @@ class FeedForward(nn.Module):
         )
 
     def forward(self, x):
-        return self.net(x)
+        return self.ffn(x)
+
 
 class Attention(nn.Module):
     def __init__(self, dim, heads=8, dim_head=64, dropout=0.):
@@ -63,8 +76,7 @@ class Attention(nn.Module):
 
     def forward(self, x):
         qkv = self.to_qkv(x).chunk(3, dim=-1)
-        q, k, v = map(lambda t: rearrange(
-            t, 'b p n (h d) -> b p h n d', h=self.heads), qkv)
+        q, k, v = map(lambda t: rearrange(t, 'b p n (h d) -> b p h n d', h=self.heads), qkv)
 
         dots = torch.matmul(q, k.transpose(-1, -2)) * self.scale
         attn = self.attend(dots)
@@ -72,19 +84,15 @@ class Attention(nn.Module):
         out = rearrange(out, 'b p h n d -> b p n (h d)')
         return self.to_out(out)
 
-class Transformer(nn.Module):
-    """Transformer block described in ViT.
-    Paper: https://arxiv.org/abs/2010.11929
-    Based on: https://github.com/lucidrains/vit-pytorch
-    """
 
-    def __init__(self, dim, depth, heads, dim_head, mlp_dim, dropout=0.):
+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, dim_head, dropout)),
-                PreNorm(dim, FeedForward(dim, mlp_dim, dropout))
+                PreNorm(dim, Attention(dim, heads = heads, dim_head = dim_head, dropout = dropout)),
+                PreNorm(dim, FeedForward(dim, mlp_dim, dropout = dropout))
             ]))
 
     def forward(self, x):
@@ -94,24 +102,17 @@ class Transformer(nn.Module):
         return x
 
 class MV2Block(nn.Module):
-    """MV2 block described in MobileNetV2.
-    Paper: https://arxiv.org/pdf/1801.04381
-    Based on: https://github.com/tonylins/pytorch-mobilenet-v2
-    """
-
-    def __init__(self, inp, oup, stride=1, expansion=4):
-        super().__init__()
-        self.stride = stride
+    def __init__(self, inp, oup, stride=1, expand_ratio=4):
+        super(MV2Block, self).__init__()
         assert stride in [1, 2]
 
-        hidden_dim = int(inp * expansion)
-        self.use_res_connect = self.stride == 1 and inp == oup
+        hidden_dim = round(inp * expand_ratio)
+        self.identity = stride == 1 and inp == oup
 
-        if expansion == 1:
+        if expand_ratio == 1:
             self.conv = nn.Sequential(
                 # dw
-                nn.Conv2d(hidden_dim, hidden_dim, 3, stride,
-                          1, groups=hidden_dim, bias=False),
+                nn.Conv2d(hidden_dim, hidden_dim, 3, stride, 1, groups=hidden_dim, bias=False),
                 nn.BatchNorm2d(hidden_dim),
                 nn.SiLU(),
                 # pw-linear
@@ -125,8 +126,7 @@ class MV2Block(nn.Module):
                 nn.BatchNorm2d(hidden_dim),
                 nn.SiLU(),
                 # dw
-                nn.Conv2d(hidden_dim, hidden_dim, 3, stride,
-                          1, groups=hidden_dim, bias=False),
+                nn.Conv2d(hidden_dim, hidden_dim, 3, stride, 1, groups=hidden_dim, bias=False),
                 nn.BatchNorm2d(hidden_dim),
                 nn.SiLU(),
                 # pw-linear
@@ -136,7 +136,8 @@ class MV2Block(nn.Module):
 
     def forward(self, x):
         out = self.conv(x)
-        if self.use_res_connect:
+
+        if self.identity:
             out = out + x
         return out
 
@@ -145,13 +146,13 @@ class MobileViTBlock(nn.Module):
         super().__init__()
         self.ph, self.pw = patch_size
 
-        self.conv1 = conv_nxn_bn(channel, channel, kernel_size)
+        self.conv1 = conv_bn_relu(channel, channel, kernel_size)
         self.conv2 = conv_1x1_bn(channel, dim)
 
-        self.transformer = Transformer(dim, depth, 4, 8, mlp_dim, dropout)
+        self.transformer = Transformer(dim, depth, 1, 32, mlp_dim, dropout)
 
         self.conv3 = conv_1x1_bn(dim, channel)
-        self.conv4 = conv_nxn_bn(2 * channel, channel, kernel_size)
+        self.conv4 = conv_bn_relu(2 * channel, channel, kernel_size)
 
     def forward(self, x):
         y = x.clone()
@@ -162,11 +163,9 @@ class MobileViTBlock(nn.Module):
 
         # Global representations
         _, _, h, w = x.shape
-        x = rearrange(x, 'b d (h ph) (w pw) -> b (ph pw) (h w) d',
-                      ph=self.ph, pw=self.pw)
+        x = rearrange(x, 'b d (h ph) (w pw) -> b (ph pw) (h w) d', ph=self.ph, pw=self.pw)
         x = self.transformer(x)
-        x = rearrange(x, 'b (ph pw) (h w) d -> b d (h ph) (w pw)',
-                      h=h//self.ph, w=w//self.pw, ph=self.ph, pw=self.pw)
+        x = rearrange(x, 'b (ph pw) (h w) d -> b d (h ph) (w pw)', h=h // self.ph, w=w // self.pw, ph=self.ph, pw=self.pw)
 
         # Fusion
         x = self.conv3(x)
@@ -174,22 +173,18 @@ class MobileViTBlock(nn.Module):
         x = self.conv4(x)
         return x
 
-class MobileViT(nn.Module):
-    """MobileViT.
-    Paper: https://arxiv.org/abs/2110.02178
-    Based on: https://github.com/chinhsuanwu/mobilevit-pytorch
-    """
 
+class MobileViT(nn.Module):
     def __init__(
         self,
         image_size,
         dims,
         channels,
         num_classes,
-        expansion=4,
-        kernel_size=3,
-        patch_size=(2, 2),
-        depths=(2, 4, 3)
+        expansion = 4,
+        kernel_size = 3,
+        patch_size = (2, 2),
+        depths = (2, 4, 3)
     ):
         super().__init__()
         assert len(dims) == 3, 'dims must be a tuple of 3'
@@ -201,31 +196,28 @@ class MobileViT(nn.Module):
 
         init_dim, *_, last_dim = channels
 
-        self.conv1 = conv_nxn_bn(3, init_dim, stride=2)
+        self.conv1 = conv_bn_relu(3, init_dim, kernel=3, stride=2)
 
         self.stem = nn.ModuleList([])
         self.stem.append(MV2Block(channels[0], channels[1], 1, expansion))
         self.stem.append(MV2Block(channels[1], channels[2], 2, expansion))
         self.stem.append(MV2Block(channels[2], channels[3], 1, expansion))
         self.stem.append(MV2Block(channels[2], channels[3], 1, expansion))
-
+    
         self.trunk = nn.ModuleList([])
         self.trunk.append(nn.ModuleList([
             MV2Block(channels[3], channels[4], 2, expansion),
-            MobileViTBlock(dims[0], depths[0], channels[5],
-                           kernel_size, patch_size, int(dims[0] * 2))
+            MobileViTBlock(dims[0], depths[0], channels[5], kernel_size, patch_size, int(dims[0] * 2))
         ]))
 
         self.trunk.append(nn.ModuleList([
             MV2Block(channels[5], channels[6], 2, expansion),
-            MobileViTBlock(dims[1], depths[1], channels[7],
-                           kernel_size, patch_size, int(dims[1] * 4))
+            MobileViTBlock(dims[1], depths[1], channels[7], kernel_size, patch_size, int(dims[1] * 4))
         ]))
 
         self.trunk.append(nn.ModuleList([
             MV2Block(channels[7], channels[8], 2, expansion),
-            MobileViTBlock(dims[2], depths[2], channels[9],
-                           kernel_size, patch_size, int(dims[2] * 4))
+            MobileViTBlock(dims[2], depths[2], channels[9], kernel_size, patch_size, int(dims[2] * 4))
         ]))
 
         self.to_logits = nn.Sequential(