vit_pytorch.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Introduction to Vision Transformers (ViT)\n",
    "\n",
    "TOC:\n",
    "- Attention, Self Attention and Multi-head Self Attention mechanisms\n",
    "- Transformers\n",
    "- Vision Transformers (ViT) paper by Google Research\n",
    "- (Optional) Deit paper by FAIR\n",
    "\n",
    "\n",
    "References:\n",
    "\n",
    "- Official implementation with Jax/Flax by Google: https://github.com/google-research/vision_transformer\n",
    "- Paper:  https://arxiv.org/abs/2010.11929\n",
    "- One of PyTorch implementations:  https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/vision_transformer.py\n",
    "\n",
    "\n",
    "![img](https://tse2.mm.bing.net/th?id=OIP.cyCA4XEM1F4ueNS2ADCa9wAAAA&pid=Api) | ![im](https://raw.githubusercontent.com/google-research/vision_transformer/master/figure1.png)\n",
    "---|---\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Attention, Self Attention and Multi-head Self Attention mechanisms\n",
    "\n",
    "#### Attention mechanism\n",
    "\n",
    "Refs:\n",
    "- https://d2l.ai/chapter_attention-mechanisms/index.html\n",
    "- https://d2l.ai/chapter_attention-mechanisms/multihead-attention.html\n",
    "- https://d2l.ai/chapter_attention-mechanisms/self-attention-and-positional-encoding.html\n",
    "\n",
    "The attention mechanism is based on a trainable associative memory with (key, value) vector pairs.\n",
    "\n",
    "- Sequence of N query vectors: $Q \\in \\mathbb{R}^{N \\times d}$ matched vs\n",
    "- Set of $k$ key vectors: $K \\in \\mathbb{R}^{k \\times d}$.\n",
    "- These inner products are then scaled and normalized with a softmax function to obtain $k$ weights.   \n",
    "- The output of the attention is the weighted sum of a set of $k$ value vectors (packed into $V \\in \\mathbb{R}^{k \\times v}$).\n",
    "\n",
    "$$\n",
    "Attention(Q,K,V) = Softmax(Q K^T / \\sqrt{d}) V \\in \\mathbb{R}^{N \\times v}\n",
    "$$\n",
    "\n",
    "with dropout on attention weights.\n",
    "\n",
    "\n",
    "A more generalized form of nonparametric attention pooling:\n",
    "$$\n",
    "f(q) = \\sum_{i=1}^{k}\\alpha(q, k_i) v_i\n",
    "$$\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[1., 0., 0.],\n",
       "        [1., 0., 1.],\n",
       "        [0., 1., 0.],\n",
       "        [0., 0., 0.],\n",
       "        [0., 0., 0.]])"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import math\n",
    "import torch\n",
    "\n",
    "torch.manual_seed(1)\n",
    "\n",
    "k = 4\n",
    "d = 3\n",
    "N = 5\n",
    "\n",
    "\n",
    "Q = (torch.rand(N, d) > 0.7).float()\n",
    "K = torch.eye(k, d)\n",
    "V = torch.arange(k * d, dtype=torch.float32).reshape(k, d) * 4.5\n",
    "Q"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[1., 0., 0.],\n",
       "        [0., 1., 0.],\n",
       "        [0., 0., 1.],\n",
       "        [0., 0., 0.]])"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "K"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[ 0.0000,  4.5000,  9.0000],\n",
       "        [13.5000, 18.0000, 22.5000],\n",
       "        [27.0000, 31.5000, 36.0000],\n",
       "        [40.5000, 45.0000, 49.5000]])"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "V"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[0.3726, 0.2091, 0.2091, 0.2091],\n",
       "        [0.3202, 0.1798, 0.3202, 0.1798],\n",
       "        [0.2091, 0.3726, 0.2091, 0.2091],\n",
       "        [0.2500, 0.2500, 0.2500, 0.2500],\n",
       "        [0.2500, 0.2500, 0.2500, 0.2500]])"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "W = torch.softmax(Q @ K.transpose(0, 1) / math.sqrt(d), dim=1)\n",
    "W"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[16.9410, 21.4410, 25.9410],\n",
       "        [18.3538, 22.8538, 27.3538],\n",
       "        [19.1470, 23.6470, 28.1470],\n",
       "        [20.2500, 24.7500, 29.2500],\n",
       "        [20.2500, 24.7500, 29.2500]])"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "W @ V"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Self Attention\n",
    "\n",
    "Query,  key  and  values  matrices are  themselves  computed  from  a  sequence  of N input  vectors: $X \\in \\mathbb{R}^{N \\times D}$\n",
    "\n",
    "- $Q = X W_{Q} \\in \\mathbb{R}^{N \\times d}$\n",
    "- $K = X W_{K} \\in \\mathbb{R}^{N \\times d}$\n",
    "- $V = X W_{V} \\in \\mathbb{R}^{N \\times d}$\n",
    "\n",
    "Attention (recall):\n",
    "$$\n",
    "Attention(Q,K,V) = Softmax(Q K^T / \\sqrt{d}) V \\in \\mathbb{R}^{N \\times v}\n",
    "$$\n",
    "\n",
    "Self-Attention (single head):\n",
    "\n",
    "$$\n",
    "Head = SelfAttention(X, \\{W_{Q}, W_{K}, W_{V}\\}) = Softmax(X W_{Q} (X W_{K})^T / \\sqrt{d}) (X W_{V}) \n",
    "$$\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(torch.Size([5, 3]), torch.Size([5, 3]), torch.Size([5, 3]))"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "N = 5\n",
    "D = 4\n",
    "d = 3\n",
    "\n",
    "x = torch.rand(N, D)\n",
    "W_q = torch.rand(D, d)\n",
    "W_k = torch.rand(D, d)\n",
    "W_v = torch.rand(D, d)\n",
    "\n",
    "Q = x @ W_q\n",
    "K = x @ W_k\n",
    "V = x @ W_v\n",
    "Q.shape, K.shape, V.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([5, 3])"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from torch.nn.functional import dropout\n",
    "\n",
    "W = dropout(torch.softmax(Q @ K.transpose(0, 1) / math.sqrt(d), dim=1))\n",
    "(W @ V).shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Multi-head Self Attention\n",
    "\n",
    "- Head is a single self attention layer\n",
    "- Heads are concatenated\n",
    "- Output linear transform applied\n",
    "\n",
    "\n",
    "$$\n",
    "MultiHeadAttention(X, \\{W^{0,1...h-1}_{Q}, W^{0,1...h-1}_{K}, W^{0,1...h-1}_{V}\\}) = Concat(Head_0, Head_1, ..., Head_{h-1}) W_{O}\n",
    "$$\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(torch.Size([5, 8]), torch.Size([5, 8]), torch.Size([5, 8]))"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "N = 5\n",
    "D = 6\n",
    "d = 4\n",
    "o = 3\n",
    "\n",
    "num_heads = 2\n",
    "\n",
    "x = torch.rand(N, D)\n",
    "W_qkv = torch.rand(D, 3 * num_heads * d)\n",
    "W_o = torch.rand(num_heads * d, o)\n",
    "\n",
    "Q, K, V = (x @ W_qkv).chunk(3, dim=1)\n",
    "Q.shape, K.shape, V.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([5, 8])"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "W = dropout(torch.softmax(Q @ K.transpose(0, 1) / math.sqrt(d), dim=1))\n",
    "heads = W @ V\n",
    "heads.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([5, 3])"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(heads @ W_o).shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Multi-head Self Attention in Vision\n",
    "\n",
    "\n",
    "- Input images to patches : `(B, C, H, W) -> (B, H // p * W // p, D)`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([4, 3, 32, 32]) -> torch.Size([4, 64, 48])\n"
     ]
    }
   ],
   "source": [
    "import torch.nn as nn\n",
    "\n",
    "\n",
    "patch_size = 4\n",
    "embed_dim = 3 * patch_size * patch_size\n",
    "conv = nn.Conv2d(3, embed_dim, kernel_size=patch_size, stride=patch_size)\n",
    "\n",
    "batch_images = torch.rand(4, 3, 32, 32)\n",
    "patches = conv(batch_images)\n",
    "patches = patches.flatten(start_dim=2)\n",
    "patches = patches.transpose(1, 2)\n",
    "print(batch_images.shape, \"->\", patches.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- multi-head attention ops:\n",
    "\n",
    "```python\n",
    "num_heads\n",
    "head_dim = embed_dim // num_heads\n",
    "\n",
    "x, (B, N, embed_dim) -> \n",
    "            -> Q = X * W_q, (B, num_heads, N, head_dim) -> \n",
    "            -> K = X * W_k, (B, num_heads, N, head_dim) ->\n",
    "            -> V = X * W_v, (B, num_heads, N, head_dim) ->\n",
    "            -> A = softmax(Q @ K^t * scale), (B, num_heads, N, N) -> Dropout(A) ->\n",
    "            -> H = A @ V, (B, num_heads, N, head_dim) ->\n",
    "            -> y = H @ W_o, (B, N, embed_dim) ->\n",
    "            -> output\n",
    "\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "class VisionAttention(nn.Module):\n",
    "    \"\"\"Vision Multi-Head Attention layer with trainable parameters:\n",
    "    - W_q, W_k, W_k : embed_dim * head_dim * num_heads * 3\n",
    "    - W_o : head_dim * num_heads * embed_dim\n",
    "    \n",
    "    .. code-block:: text\n",
    "\n",
    "        x, (B, N, embed_dim) -> \n",
    "                    -> Q = X * W_q, (B, num_heads, N, head_dim) -> \n",
    "                    -> K = X * W_k, (B, num_heads, N, head_dim) ->\n",
    "                    -> V = X * W_v, (B, num_heads, N, head_dim) ->\n",
    "                    -> A = softmax(Q @ K^t * scale), (B, num_heads, N, N) -> Dropout(A) ->\n",
    "                    -> H = A @ V, (B, num_heads, N, head_dim) ->\n",
    "                    -> y = H @ W_o, (B, N, embed_dim) ->\n",
    "                    -> output\n",
    "                    \n",
    "    https://github.com/google/flax/blob/master/flax/nn/attention.py\n",
    "    \"\"\"\n",
    "    \n",
    "    def __init__(self, embed_dim, num_heads=8, qkv_bias=True, attn_drop=0.):\n",
    "        super().__init__()\n",
    "        head_dim = embed_dim // num_heads\n",
    "        self.num_heads = num_heads        \n",
    "        self.scale = float(head_dim) ** -0.5\n",
    "        self.qkv = nn.Linear(embed_dim, num_heads * head_dim * 3, bias=qkv_bias)\n",
    "        self.att_drop = nn.Dropout(attn_drop)\n",
    "        self.proj = nn.Linear(num_heads * head_dim, embed_dim)\n",
    "\n",
    "    def forward(self, x):\n",
    "        B, N, _ = x.shape\n",
    "        # (B, N, num_heads * head_dim * 3) -> (B, N, num_heads, head_dim * 3) ->\n",
    "        # -> (B, num_heads, N, head_dim * 3) -> (B, num_heads, N, head_dim) x 3\n",
    "        qkv = self.qkv(x).reshape(B, N, self.num_heads, -1).transpose(1, 2)\n",
    "        Q, K, V = qkv.chunk(3, dim=-1)\n",
    "        attention = (Q @ K.transpose(-1, -2)) * self.scale\n",
    "        attention = attention.softmax(dim=-1)\n",
    "        attention = self.att_drop(attention)\n",
    "        heads = attention @ V\n",
    "        # Heads is (B, num_heads, N, head_dim) => (B, N, num_heads * head_dim)\n",
    "        heads = heads.transpose(1, 2).reshape(B, N, -1)\n",
    "        output = self.proj(heads)\n",
    "        return output"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "num_heads = 6\n",
    "att = VisionAttention(embed_dim, num_heads=num_heads)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(torch.Size([4, 6, 64, 8]),\n",
       " torch.Size([4, 6, 64, 8]),\n",
       " torch.Size([4, 6, 64, 8]))"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Compute Q, K, V\n",
    "B, N, _ = patches.shape\n",
    "qkv = att.qkv(patches).reshape(B, N, att.num_heads, -1).transpose(1, 2)\n",
    "Q, K, V = qkv.chunk(3, dim=-1)\n",
    "Q.shape, K.shape, V.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([4, 6, 64, 64])"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Compute heads\n",
    "attention = (Q @ K.transpose(-1, -2)) * att.scale\n",
    "attention = attention.softmax(dim=-1)\n",
    "attention.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([4, 6, 64, 8])"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "heads = attention @ V\n",
    "heads.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(torch.Size([4, 64, 48]), True)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "heads = heads.transpose(1, 2).reshape(B, N, -1)\n",
    "output = att.proj(heads)\n",
    "output.shape, output.shape == att(patches).shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Transformers\n",
    "\n",
    "Refs:\n",
    "- https://d2l.ai/chapter_attention-mechanisms/transformer.html\n",
    "\n",
    "\n",
    "In NLP Transformers is composed of an encoder and a decoder. \n",
    "The input (source) and output (target) sequence embeddings are added with positional \n",
    "encoding before being fed into the encoder and the decoder that stack modules based on self-attention.\n",
    "\n",
    "<div style=\"background: white;\">\n",
    "<img src=\"https://d2l.ai/_images/transformer.svg\" />\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### [Vision Transformer for classification](https://arxiv.org/abs/2010.11929)\n",
    "\n",
    "![im](https://raw.githubusercontent.com/google-research/vision_transformer/master/figure1.png)\n",
    "\n",
    "Inspired  by  the  Transformer  scaling  successes  in  NLP, authors  experiment  with  applying  a  standard Transformer directly to images, with the fewest possible modifications. To do so, they split an image into patches and provide the sequence of linear embeddings of these patches as an input to a Transformer. Image patches are treated the same way as tokens (words) in an NLP application. They train the model on image classification in supervised fashion.\n",
    "\n",
    "3 types of models:\n",
    "- ViT-Base, 86M params (vs ResNet50, 23M params)\n",
    "- ViT-Large, 307M params\n",
    "- ViT-Huge, 632M params (vs ResNet152x4 from [Big Transfer](https://arxiv.org/abs/1912.11370), 936M)\n",
    "\n",
    "ImageNet (1.3M images): models give modest accuracies of a few percentage points below ResNets of comparable size. Transformers **lack some of the inductive biases** inherent to CNNs, such as translation equivariance and locality, and therefore do not generalize well when trained on insufficient amounts of data.\n",
    "\n",
    "Larger datasets (14M-300M images): large scale training trumps inductive bias. Vision Transformer (ViT) attains \"excellent\" results when pre-trained at sufficient scale and transferred to tasks with fewer datapoints. \n",
    "\n",
    "When pre-trained on the public ImageNet-21k dataset or Google's JFT-300M dataset, ViT approaches or beats state of the art on multiple image recognition benchmarks. \n",
    "In particular, the best model reaches the accuracy of 88.55% on ImageNet.\n",
    "\n",
    "SOTA on ImageNet: https://paperswithcode.com/sota/image-classification-on-imagenet\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's implement main blocks:\n",
    "- EncoderBlock: layer norms, visual attention and mlp\n",
    "\n",
    "- Visual Transformer: Patchs and Position Embedding + EncoderBlocks + classification head"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "class MLP(nn.Module):\n",
    "    \"\"\"The positionwise feed-forward network or MLP.    \n",
    "    \"\"\"\n",
    "    def __init__(self, in_features, hidden_features=None, out_features=None, drop_rate=0.):\n",
    "        super().__init__()\n",
    "        out_features = out_features or in_features\n",
    "        hidden_features = hidden_features or in_features\n",
    "        self.fc1 = nn.Linear(in_features, hidden_features)\n",
    "        self.gelu = nn.GELU()\n",
    "        self.fc2 = nn.Linear(hidden_features, out_features)\n",
    "        self.drop = nn.Dropout(drop_rate)\n",
    "\n",
    "    def forward(self, x):\n",
    "        x = self.fc1(x)\n",
    "        x = self.gelu(x)\n",
    "        x = self.drop(x)\n",
    "        x = self.fc2(x)\n",
    "        x = self.drop(x)\n",
    "        return x\n",
    "\n",
    "\n",
    "class EncoderBlock(nn.Module):\n",
    "    \"\"\"ViT EncoderBlock\n",
    "    \n",
    "    https://github.com/google-research/vision_transformer/blob/9dbeb0269e0ed1b94701c30933222b49189aa33c/vit_jax/models.py#L94\n",
    "    \"\"\"\n",
    "    \n",
    "    def __init__(self, embed_dim, num_heads, mlp_ratio=4, drop_rate=0., attn_drop_rate=0.):\n",
    "        super().__init__()\n",
    "        self.lnorm1 = nn.LayerNorm(embed_dim, eps=1e-6)\n",
    "        self.lnorm2 = nn.LayerNorm(embed_dim, eps=1e-6)\n",
    "        self.attention = VisionAttention(\n",
    "            embed_dim, num_heads=num_heads, attn_drop=attn_drop_rate\n",
    "        )\n",
    "        self.dropout = nn.Dropout(drop_rate)\n",
    "        self.mlp = MLP(embed_dim, int(embed_dim * mlp_ratio), drop_rate=drop_rate)\n",
    "        \n",
    "    def forward(self, x):\n",
    "        y = self.lnorm1(x)\n",
    "        y = self.attention(y)\n",
    "        y = self.dropout(y)\n",
    "        x = x + y\n",
    "        \n",
    "        y = self.lnorm2(x)\n",
    "        y = self.mlp(y)\n",
    "        return x + y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([4, 64, 48])"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "block = EncoderBlock(embed_dim, num_heads)\n",
    "out = block(patches)\n",
    "out.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Positional encoding\n",
    "\n",
    "All tokens are processed at once without any spatial relationship.\n",
    "The idea is to add a learnable parameter to the tokens to retain positional information.\n",
    "\n",
    "According to the paper, they use 1d positional embedding, considering the inputs as a sequence of patches in the raster order.\n",
    "Authors have not observed significant performance gains from using more advanced 2D-aware position embeddings.\n",
    "\n",
    "$$\n",
    "x = x + pe\n",
    "$$\n",
    "where $pe$ is randomly initialized with using Normal distribution."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "class VisionTransformer(nn.Module):\n",
    "    \"\"\"VisionTransformer model\n",
    "    \n",
    "    https://github.com/google-research/vision_transformer/\n",
    "    \"\"\"\n",
    "    \n",
    "    def __init__(\n",
    "        self, \n",
    "        patch_size=16,\n",
    "        hidden_size=768,\n",
    "        input_channels=3,\n",
    "        input_size=224,\n",
    "        num_classes=1000,\n",
    "        num_layers=12,\n",
    "        num_heads=12,\n",
    "        mlp_dim=3072,\n",
    "        drop_rate=0.1, \n",
    "        attn_drop_rate=0.0,\n",
    "    ):\n",
    "        super().__init__()\n",
    "\n",
    "        self.patch_embed = nn.Conv2d(\n",
    "            input_channels, hidden_size, kernel_size=patch_size, stride=patch_size\n",
    "        )\n",
    "        self.cls_token = nn.Parameter(torch.zeros(1, 1, hidden_size))\n",
    "        \n",
    "        num_patches = (input_size // patch_size) ** 2\n",
    "        self.pos_embed = nn.Parameter(torch.zeros(1, num_patches + 1, hidden_size))\n",
    "        self.pos_dropout = nn.Dropout(p=drop_rate)  \n",
    "    \n",
    "        # Define encoder blocks\n",
    "        kwargs = {\n",
    "            \"embed_dim\": hidden_size,\n",
    "            \"num_heads\": num_heads,\n",
    "            \"mlp_ratio\": mlp_dim / hidden_size,\n",
    "            \"drop_rate\": drop_rate,\n",
    "            \"attn_drop_rate\": attn_drop_rate,\n",
    "        }\n",
    "        blocks = [EncoderBlock(**kwargs) for _ in range(num_layers)]\n",
    "        self.blocks = nn.Sequential(*blocks)        \n",
    "        self.lnorm = nn.LayerNorm(hidden_size, eps=1e-6)\n",
    "\n",
    "        self.mlp_head = nn.Linear(hidden_size, num_classes)\n",
    "    \n",
    "    def features(self, x):\n",
    "        patches = self.patch_embed(x)\n",
    "        patches = patches.flatten(start_dim=2)\n",
    "        patches = patches.transpose(1, 2)\n",
    "        \n",
    "        batch_size = patches.shape[0]\n",
    "\n",
    "        cls_token = self.cls_token.expand(batch_size, -1, -1)\n",
    "        x = torch.cat([cls_token, patches], dim=1)\n",
    "        x = x + self.pos_embed\n",
    "        x = self.pos_dropout(x)\n",
    "\n",
    "        x = self.blocks(x)\n",
    "        x = self.lnorm(x)\n",
    "        \n",
    "        # Return the first token\n",
    "        return x[:, 0, ...]        \n",
    "    \n",
    "    def forward(self, x):\n",
    "        f = self.features(x)\n",
    "        y = self.mlp_head(f)        \n",
    "        return y\n",
    "    \n",
    "\n",
    "def vit_b16(num_classes=1000, input_channels=3, input_size=224):\n",
    "    return VisionTransformer(\n",
    "        num_classes=num_classes,\n",
    "        input_channels=input_channels,\n",
    "        input_size=input_size,\n",
    "        patch_size=16,\n",
    "        hidden_size=768,\n",
    "        num_layers=12,\n",
    "        num_heads=12,\n",
    "        mlp_dim=3072,\n",
    "        drop_rate=0.1, \n",
    "        attn_drop_rate=0.0,        \n",
    "    )\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([4, 1000])"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = vit_b16()\n",
    "\n",
    "x = torch.rand(4, 3, 224, 224)\n",
    "model(x).shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "86.567656"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sum([m.numel() for m in model.parameters()]) * 1e-6"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "def vit_tiny(num_classes=10, input_channels=3, input_size=32):\n",
    "    return VisionTransformer(\n",
    "        num_classes=num_classes,\n",
    "        input_channels=input_channels,\n",
    "        input_size=input_size,\n",
    "        patch_size=4,\n",
    "        hidden_size=512,\n",
    "        num_layers=4,\n",
    "        num_heads=6,\n",
    "        mlp_dim=1024,\n",
    "        drop_rate=0.0,\n",
    "        attn_drop_rate=0.0,\n",
    "    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8.459762"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = vit_tiny()\n",
    "sum([m.numel() for m in model.parameters()]) * 1e-6"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Compare with [PyTorch-Image-Models](https://github.com/rwightman/pytorch-image-models)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "# !pip install timm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "def timm_vit_tiny(num_classes=10, input_channels=3):\n",
    "    from functools import partial\n",
    "    import torch.nn as nn\n",
    "    from timm.models.vision_transformer import VisionTransformer as TimmVisionTransformer\n",
    "\n",
    "    return TimmVisionTransformer(\n",
    "        img_size=32, patch_size=4, embed_dim=512, depth=4, num_heads=6, mlp_ratio=2, qkv_bias=True,\n",
    "        num_classes=10, norm_layer=partial(nn.LayerNorm, eps=1e-6)\n",
    "    )\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8.47617"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "timm_model = timm_vit_tiny()\n",
    "sum([m.numel() for m in timm_model.parameters()]) * 1e-6"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\u001b[0;31mSignature:\u001b[0m\n",
       "\u001b[0mtimm_model\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__init__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mimg_size\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m224\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mpatch_size\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m16\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0min_chans\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mnum_classes\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1000\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0membed_dim\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m768\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mdepth\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m12\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mnum_heads\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m12\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mmlp_ratio\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m4.0\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mqkv_bias\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mqk_scale\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mdrop_rate\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0.0\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mattn_drop_rate\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0.0\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mdrop_path_rate\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0.0\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mhybrid_backbone\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mnorm_layer\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m<\u001b[0m\u001b[0;32mclass\u001b[0m \u001b[0;34m'torch.nn.modules.normalization.LayerNorm'\u001b[0m\u001b[0;34m>\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
       "\u001b[0;31mDocstring:\u001b[0m Initializes internal Module state, shared by both nn.Module and ScriptModule.\n",
       "\u001b[0;31mSource:\u001b[0m   \n",
       "    \u001b[0;32mdef\u001b[0m \u001b[0m__init__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mimg_size\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m224\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpatch_size\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m16\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0min_chans\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnum_classes\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1000\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0membed_dim\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m768\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdepth\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m12\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m                 \u001b[0mnum_heads\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m12\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmlp_ratio\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m4.\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mqkv_bias\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mqk_scale\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdrop_rate\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0.\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mattn_drop_rate\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0.\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m                 \u001b[0mdrop_path_rate\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0.\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhybrid_backbone\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnorm_layer\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mnn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mLayerNorm\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m        \u001b[0msuper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__init__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m        \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnum_classes\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnum_classes\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m        \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnum_features\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0membed_dim\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0membed_dim\u001b[0m  \u001b[0;31m# num_features for consistency with other models\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m        \u001b[0;32mif\u001b[0m \u001b[0mhybrid_backbone\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m            \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpatch_embed\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mHybridEmbed\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m                \u001b[0mhybrid_backbone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mimg_size\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mimg_size\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0min_chans\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0min_chans\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0membed_dim\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0membed_dim\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m        \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m            \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpatch_embed\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mPatchEmbed\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m                \u001b[0mimg_size\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mimg_size\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpatch_size\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mpatch_size\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0min_chans\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0min_chans\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0membed_dim\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0membed_dim\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m        \u001b[0mnum_patches\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpatch_embed\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnum_patches\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m        \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcls_token\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mParameter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mzeros\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0membed_dim\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m        \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpos_embed\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mParameter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mzeros\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnum_patches\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0membed_dim\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m        \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpos_drop\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mDropout\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mp\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdrop_rate\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m        \u001b[0mdpr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitem\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mx\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlinspace\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdrop_path_rate\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdepth\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m  \u001b[0;31m# stochastic depth decay rule\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m        \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mblocks\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mModuleList\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m            \u001b[0mBlock\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m                \u001b[0mdim\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0membed_dim\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnum_heads\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mnum_heads\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmlp_ratio\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmlp_ratio\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mqkv_bias\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mqkv_bias\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mqk_scale\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mqk_scale\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m                \u001b[0mdrop\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdrop_rate\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mattn_drop\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mattn_drop_rate\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdrop_path\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdpr\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnorm_layer\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mnorm_layer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m            \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdepth\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m        \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnorm\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnorm_layer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0membed_dim\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m        \u001b[0;31m# NOTE as per official impl, we could have a pre-logits representation dense layer + tanh here\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m        \u001b[0;31m#self.repr = nn.Linear(embed_dim, representation_size)\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m        \u001b[0;31m#self.repr_act = nn.Tanh()\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m        \u001b[0;31m# Classifier head\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m        \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhead\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mLinear\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0membed_dim\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnum_classes\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mnum_classes\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m0\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0mnn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mIdentity\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m        \u001b[0mtrunc_normal_\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpos_embed\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstd\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m.02\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m        \u001b[0mtrunc_normal_\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcls_token\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstd\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m.02\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m        \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapply\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_init_weights\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
       "\u001b[0;31mFile:\u001b[0m      /opt/conda/lib/python3.8/site-packages/timm/models/vision_transformer.py\n",
       "\u001b[0;31mType:\u001b[0m      method\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "timm_model.__init__??"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\u001b[0;31mSignature:\u001b[0m \u001b[0ma\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__init__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mimg_size\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m224\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpatch_size\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m16\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0min_chans\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0membed_dim\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m768\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
       "\u001b[0;31mDocstring:\u001b[0m Initializes internal Module state, shared by both nn.Module and ScriptModule.\n",
       "\u001b[0;31mSource:\u001b[0m   \n",
       "    \u001b[0;32mdef\u001b[0m \u001b[0m__init__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mimg_size\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m224\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpatch_size\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m16\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0min_chans\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0membed_dim\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m768\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m        \u001b[0msuper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__init__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m        \u001b[0mimg_size\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mto_2tuple\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mimg_size\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m        \u001b[0mpatch_size\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mto_2tuple\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpatch_size\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m        \u001b[0mnum_patches\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mimg_size\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m//\u001b[0m \u001b[0mpatch_size\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mimg_size\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m//\u001b[0m \u001b[0mpatch_size\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m        \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mimg_size\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mimg_size\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m        \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpatch_size\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpatch_size\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m        \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnum_patches\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnum_patches\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m        \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mproj\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mConv2d\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0min_chans\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0membed_dim\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkernel_size\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mpatch_size\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstride\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mpatch_size\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
       "\u001b[0;31mFile:\u001b[0m      /opt/conda/lib/python3.8/site-packages/timm/models/vision_transformer.py\n",
       "\u001b[0;31mType:\u001b[0m      method\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "a = timm_model.patch_embed\n",
    "a.__init__??"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\u001b[0;31mSignature:\u001b[0m\n",
       "\u001b[0ma\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__init__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mdim\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mnum_heads\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mmlp_ratio\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m4.0\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mqkv_bias\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mqk_scale\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mdrop\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0.0\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mattn_drop\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0.0\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mdrop_path\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0.0\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mact_layer\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m<\u001b[0m\u001b[0;32mclass\u001b[0m \u001b[0;34m'torch.nn.modules.activation.GELU'\u001b[0m\u001b[0;34m>\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mnorm_layer\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m<\u001b[0m\u001b[0;32mclass\u001b[0m \u001b[0;34m'torch.nn.modules.normalization.LayerNorm'\u001b[0m\u001b[0;34m>\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
       "\u001b[0;31mDocstring:\u001b[0m Initializes internal Module state, shared by both nn.Module and ScriptModule.\n",
       "\u001b[0;31mSource:\u001b[0m   \n",
       "    \u001b[0;32mdef\u001b[0m \u001b[0m__init__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdim\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnum_heads\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmlp_ratio\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m4.\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mqkv_bias\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mqk_scale\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdrop\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0.\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mattn_drop\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0.\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m                 \u001b[0mdrop_path\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0.\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mact_layer\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mnn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mGELU\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnorm_layer\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mnn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mLayerNorm\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m        \u001b[0msuper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__init__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m        \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnorm1\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnorm_layer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdim\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m        \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mattn\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mAttention\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m            \u001b[0mdim\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnum_heads\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mnum_heads\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mqkv_bias\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mqkv_bias\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mqk_scale\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mqk_scale\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mattn_drop\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mattn_drop\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mproj_drop\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdrop\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m        \u001b[0;31m# NOTE: drop path for stochastic depth, we shall see if this is better than dropout here\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m        \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdrop_path\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mDropPath\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdrop_path\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mdrop_path\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m0.\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0mnn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mIdentity\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m        \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnorm2\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnorm_layer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdim\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m        \u001b[0mmlp_hidden_dim\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdim\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mmlp_ratio\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m        \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmlp\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mMlp\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0min_features\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdim\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhidden_features\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmlp_hidden_dim\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mact_layer\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mact_layer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdrop\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdrop\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
       "\u001b[0;31mFile:\u001b[0m      /opt/conda/lib/python3.8/site-packages/timm/models/vision_transformer.py\n",
       "\u001b[0;31mType:\u001b[0m      method\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "a = timm_model.blocks[0]\n",
    "a.__init__??"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Train ViT on CIFAR10\n",
    "\n",
    "- On TensorBoard.dev: https://tensorboard.dev/experiment/14IXJkjzT8OEagHAl5os2w"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "# Train ViT on CIFAR10 with [PyTorch-Ignite](https://github.com/pytorch/ignite)\n",
      "\n",
      "\n",
      "We define ViT models adapted for CIFAR10 images of 32x32 size:\n",
      "- vit_tiny_patch4_32x32 : 32x32 input size and patch of 4 pixels\n",
      "- vit_b4_32x32 : our base ViT reimplementation with 32x32 input size and patch of 4 pixels\n",
      "- vit_b3_32x32 : our base ViT reimplementation with 32x32 input size and patch of 3 pixels\n",
      "- vit_b2_32x32 : our base ViT reimplementation with 32x32 input size and patch of 2 pixels\n",
      "- timm_vit_b4_32x32 : timm reimplementation of base ViT with 32x32 input size and patch of 4 pixels\n",
      "\n"
     ]
    }
   ],
   "source": [
    "!head -10 cifar10/README.md"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Self-Supervision \n",
    "\n",
    "With self-supervised pre-training e.g. BERT in NLP, smaller ViT-B/16 model achieves 79.9% accuracy on ImageNet, as ignificant improvement of 2% to training from scratch, but still 4% behind supervised pre-training.\n",
    "\n",
    "\n",
    "> We employ the masked patch prediction objective for preliminary self-supervision experiments. Todo so we corrupt 50% of patch embeddings by either replacing their embeddings with a learnable [mask] embedding  (80%),  a  random  other  patch  embedding  (10%)  or  just  keeping  them  as is (10%). This setup is very similar to the one used for language by Devlin et al. (2019).  Finally, we predict the 3-bit, mean color (i.e., 512 colors in total) of every corrupted patch using their respective patch representations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Takeaway\n",
    "\n",
    "- ViT without any handmade image-specific inductive biases\n",
    "- ViT matches or exceeds SOTA on many image classification datasets, whilst being relatively cheap to pre-train.\n",
    "- Initial experiments show improvement from self-supervised pre-training, but there is still large gap between self-supervised and large-scale supervised pre-training. Finally, further scaling of ViT would likely lead to improved performance.\n",
    "\n",
    "\n",
    "- **ViT needs a large training dataset** (e.g. ImageNet-21k, 14M images /21k classes) for supervised pre-training in order to outperform CNN on a fine-tuning task.\n",
    "- **Large pre-training time**: \"ViT-L/16 model (307M params) pre-trained on the public ImageNet-21k dataset ... taking fewer resources to pre-train: it could be trained using a standard cloud TPUv3 with 8 cores in ap-proximately 30 days\"\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### [Training data-efficient image transformers & distillation through attention](https://arxiv.org/abs/2012.12877)\n",
    "\n",
    "<div>\n",
    "<img src=\"https://raw.githubusercontent.com/facebookresearch/deit/main/.github/deit.png\" width=50%/>\n",
    "</div>\n",
    "\n",
    "- https://github.com/facebookresearch/deit\n",
    "- https://ai.facebook.com/blog/data-efficient-image-transformers-a-promising-new-technique-for-image-classification\n",
    "\n",
    "\n",
    "- Paper's DeiT-B model == original ViT-B/16, (86M params) trained in a special way on Imagenet only on a single 8-GPU node in less than 3 days\n",
    "- Model achieves top-1 accuracy of 83.1% (single-crop) on ImageNet with no external data vs 78% by original ViT-B/16.\n",
    "- Distilled training\n"
   ]
  },
  {
   "attachments": {
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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Training tricks\n",
    "\n",
    "- Data-augmentations\n",
    "    - Rand-Augment: https://arxiv.org/abs/1909.13719v2\n",
    "    - MixUp: https://arxiv.org/abs/1710.09412\n",
    "    - CutMix: https://arxiv.org/abs/1905.04899v2\n",
    "    - Erasing: ~Cutout\n",
    "    - Repeated Augmentations: https://openaccess.thecvf.com/content_CVPR_2020/papers/Hoffer_Augment_Your_Batch_Improving_Generalization_Through_Instance_Repetition_CVPR_2020_paper.pdf\n",
    "- Optimizer AdamW: https://arxiv.org/abs/1711.05101v1\n",
    "- Stochastic Depth: https://arxiv.org/abs/1603.09382\n",
    "\n",
    "\n",
    "![image.png](attachment:f98ab4d4-52a4-47b5-8f85-f2b650858e52.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Data-augmentations\n",
    "\n",
    "**Rand-Augment**: N transformations with a uniformly chosen parameters from K possible sets.\n",
    "    \n",
    "Transformations applied include: identity, autoContrast, equalize, rotation, solarization, etc.\n",
    "\n",
    "For example, N=2\n",
    "```\n",
    "1) autoContrast(param_3/K) -> rotation(param_K-1/K)\n",
    "2) solarization(param_10/K) -> identity\n",
    "```\n",
    "\n",
    "**MixUp**: Two interpolated samples and targets\n",
    "\n",
    "```\n",
    "loss = criterion(output, target_a) * lam + criterion(output, target_b) * (1. - lam)\n",
    "```\n",
    "https://github.com/clovaai/CutMix-PyTorch\n",
    "\n",
    "\n",
    "**Repeated Augmentations**: samples (but differently augmented) are seen multiple times in the same batch\n",
    "\n",
    "https://github.com/eladhoffer/convNet.pytorch"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Stochastic Depth**: randomly drop layer and keeps skip connection\n",
    "\n",
    "```\n",
    "y = x + drop_path(f(x))\n",
    "  = x, p < drop_path_rate\n",
    "  = x + f(x), otherwise\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Takeaway\n",
    "\n",
    "- Data-efficient training process\n",
    "- Distillation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Fun facts:\n",
    "\n",
    "1) Paper published 24/12/2020 says:\n",
    "\n",
    "> For reference, the current state of the art of 88.55% achieved with extra training data was obtained by the ViT-H model (600M parameters) trained on JFT-300M at resolution 512.\n",
    "\n",
    "However, today (25/01/2021) SOTA on ImageNet with extra data is 90.2% by \"[Meta Pseudo Labels](https://arxiv.org/abs/2003.10580v3)\" and 88.61% by \"[Sharpness-Aware Minimization for Efficiently Improving Generalization](https://arxiv.org/abs/2010.01412v2)\"\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## More references:\n",
    "\n",
    "- [Visual Transformers: Token-based Image Representation and Processing for Computer Vision](https://arxiv.org/abs/2006.03677v3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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