From d2e3d24d2c6e82c93b8396118cb121ca3e78a1bd Mon Sep 17 00:00:00 2001 From: Andre Henriques Date: Tue, 20 Feb 2024 23:59:53 +0000 Subject: [PATCH] chore: Initial Commit --- .gitignore | 7 + Lab1_2/A1_template.csv | 2 + Lab1_2/A1_template_template.csv | 2 + Lab1_2/Assignment1.csv | 50 + Lab1_2/Lab1&2_Transformers-base.ipynb | 1146 ++++ Lab1_2/Lab1&2_Transformers.ipynb | 93 + Lab3/Week3_Autoencoder+MAE - Copy.ipynb | 7058 +++++++++++++++++++++++ Lab3/Week3_Autoencoder+MAE - Copy.py | 562 ++ 8 files changed, 8920 insertions(+) create mode 100644 .gitignore create mode 100644 Lab1_2/A1_template.csv create mode 100644 Lab1_2/A1_template_template.csv create mode 100644 Lab1_2/Assignment1.csv create mode 100644 Lab1_2/Lab1&2_Transformers-base.ipynb create mode 100644 Lab1_2/Lab1&2_Transformers.ipynb create mode 100644 Lab3/Week3_Autoencoder+MAE - Copy.ipynb create mode 100644 Lab3/Week3_Autoencoder+MAE - Copy.py diff --git a/.gitignore b/.gitignore new file mode 100644 index 0000000..9197318 --- /dev/null +++ b/.gitignore @@ -0,0 +1,7 @@ +Lab3/dataset/ +Lab3/mae +Lab3/masked +Lab3/st +Lab3/st2 +*.pth +.ipynb_checkpoints diff --git a/Lab1_2/A1_template.csv b/Lab1_2/A1_template.csv new file mode 100644 index 0000000..298104f --- /dev/null +++ b/Lab1_2/A1_template.csv @@ -0,0 +1,2 @@ +URN,Q,K,V,ANSWER +6644818,"[[-0.19737370312213898, -1.0540887117385864, 0.02383515052497387, 0.46185705065727234], [-1.2415547370910645, 0.8366656303405762, 0.3741966784000397, 0.9099264740943909], [0.3436168134212494, 0.6154376268386841, 1.1926648616790771, 1.6477248668670654]]","[[1.9663442373275757, 0.15551914274692535, -0.8715013861656189, 0.32070425152778625], [-5.85474967956543, 1.7047394514083862, -1.0024793148040771, 1.3307985067367554], [0.06319630891084671, -2.030783176422119, -5.436811447143555, -0.42979586124420166]]","[[-82.127197265625, 0.9534303545951843, -28.78610610961914, -10.762138366699219], [-16.467313766479492, 60.92831802368164, -36.08392333984375, 31.648052215576172], 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-9.186692237854004, -20.234752655029297]]","[[6.580479145050049, 1.9564368724822998, -7.401310920715332, -8.521329879760742], [-3.6674721240997314, -5.470401763916016, 11.234831809997559, -4.046061038970947], [4.957516193389893, -7.476752758026123, 2.370039224624634, -5.951333999633789]]", diff --git a/Lab1_2/Lab1&2_Transformers-base.ipynb b/Lab1_2/Lab1&2_Transformers-base.ipynb new file mode 100644 index 0000000..02c6d80 --- /dev/null +++ b/Lab1_2/Lab1&2_Transformers-base.ipynb @@ -0,0 +1,1146 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "Cv-9Vzunb_tf" + }, + "source": [ + "# Import Necessary Library" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "id": "4f-K54nHb-Uq" + }, + "outputs": [], + "source": [ + "import torch\n", + "import torch.nn as nn\n", + "import torch.nn.functional as F\n", + "import torch.optim as optim\n", + "import torch.utils.data as data\n", + "import math\n", + "import os\n", + "import urllib.request\n", + "import pandas as pd\n", + "from functools import partial\n", + "from urllib.error import HTTPError\n", + "from datetime import datetime" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "BtW5eDFocsMA" + }, + "source": [ + "# What is Attention?\n", + "\n", + "Attention in neural networks, particularly relevant for sequential tasks, refers to a mechanism that selectively focuses on certain parts of input data. This concept has gained significant interest in recent years. In essence, attention computes a weighted average of elements in a sequence, with the weights being dynamically determined based on the relevance of each element to a specific query. This allows the model to prioritize certain inputs over others.\n", + "\n", + "The attention mechanism consists of four primary components:\n", + "\n", + "* **Query**: A feature vector representing the target of the attention, essentially indicating the information the model seeks within the sequence.\n", + "* **Keys**: Feature vectors corresponding to each input element, describing the content or relevance of the elements. The keys help the model identify which elements to focus on, relative to the query.\n", + "* **Values**: Feature vectors representing the actual content from each input element that the model should aggregate.\n", + "* **Score function**: A function used to calculate attention weights, representing the relevance of each key-query pair. Common implementations include simple operations like the dot product or more complex structures like a small neural network.\n", + "\n", + "The attention mechanism operates by first computing scores between the query and each key using the score function. These scores determine the attention weights through a softmax function, ensuring that they sum to one and are non-negative. The output is then calculated as the weighted sum of the value vectors, with weights corresponding to the calculated attention scores.\n", + "\n", + "Mathematically, this process can be represented as:\n", + "\n", + "$$\n", + "\\alpha_i = \\frac{\\exp\\left(f_{attn}\\left(\\text{key}_i, \\text{query}\\right)\\right)}{\\sum_j \\exp\\left(f_{attn}\\left(\\text{key}_j, \\text{query}\\right)\\right)}, \\hspace{5mm} \\text{out} = \\sum_i \\alpha_i \\cdot \\text{value}_i\n", + "$$\n", + "\n", + "In practice, attention mechanisms can vary based on the choice of queries, the definition of key and value vectors, and the specific score function used. A prominent example is the **self-attention** mechanism used in the Transformer architecture, where each element in a sequence provides its own key, value, and query. The self-attention mechanism allows each element to attend to all elements in the sequence, including itself, resulting in a representation that incorporates information from the entire sequence.\n", + "\n", + "The above explanation provides a conceptual understanding of the attention mechanism, highlighting its components and operational principles without delving into the specific details of any particular implementation, such as the scaled dot product attention used in Transformers." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "1DFh9Ic8dp-u" + }, + "source": [ + "### Scaled Dot Product Attention\n", + "\n", + "The scaled dot product attention is a fundamental component of the self-attention mechanism, enabling elements within a sequence to efficiently attend to one another. It operates on queries $Q\\in\\mathbb{R}^{T\\times d_k}$, keys $K\\in\\mathbb{R}^{T\\times d_k}$, and values $V\\in\\mathbb{R}^{T\\times d_v}$, where $T$ represents the sequence length and $d_k$, $d_v$ denote the dimensions of queries/keys and values, respectively.\n", + "\n", + "The mechanism calculates the attention values based on the dot product similarity between each query $Q_i$ and key $K_j$, and scales the results by the square root of the dimensionality of the keys, $d_k$. The formula for this calculation is:\n", + "\n", + "$$\\text{Attention}(Q, K, V) = \\text{softmax}\\left(\\frac{QK^T}{\\sqrt{d_k}}\\right)V$$\n", + "\n", + "Here, the matrix product $QK^T$ computes the dot product between all pairs of queries and keys, forming a $T\\times T$ matrix where each entry represents the attention score from one element to another. After applying the softmax function, these scores are used as weights to compute a weighted average of the value vectors.\n", + "\n", + "The scaling factor $1/\\sqrt{d_k}$ is critical for maintaining the variance of the attention scores at an appropriate level. Without this scaling, the variance of the dot products could become too large, leading to a situation where the softmax function saturates, with most of its output concentrated on a single element. This would hinder learning by resulting in gradients that are almost zero.\n", + "\n", + "Additionally, the mechanism can include an optional masking step (denoted as `Mask (opt.)` in the diagram), useful in situations like batch processing of sequences of varying lengths. Padding is used to equalize the lengths of sequences, and the mask ensures that the padded positions do not affect the attention calculation, typically by assigning a very low value to these positions in the attention scores.\n", + "\n", + "In summary, the scaled dot product attention efficiently enables each element in a sequence to attend to all others, considering the relevance of each element, and is crucial for models that rely on self-attention, such as Transformers." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "VKvaGxqIdvba" + }, + "source": [ + "### Implementing Scaled Dot Product Attention\n", + "\n", + "Scaled dot product attention is a core mechanism allowing each element in a sequence to consider all other elements efficiently, which is fundamental in self-attention models like Transformers. Here's a detailed guide to implementing scaled dot product attention, breaking down the components and the steps involved.\n", + "\n", + "#### Inputs to the Attention Mechanism\n", + "The attention function takes three inputs:\n", + "1. **Queries (Q)**: $Q\\in\\mathbb{R}^{T\\times d_k}$, where $T$ is the sequence length and $d_k$ is the dimensionality of the queries and keys.\n", + "2. **Keys (K)**: $K\\in\\mathbb{R}^{T\\times d_k}$.\n", + "3. **Values (V)**: $V\\in\\mathbb{R}^{T\\times d_v}$, where $d_v$ is the dimensionality of the values.\n", + "\n", + "#### Step-by-Step Calculation\n", + "1. **Dot Product of Queries and Keys**: Calculate the dot product between each query and all keys to obtain a measure of compatibility or relevance between each query-key pair. This results in a matrix of shape $T \\times T$, where each element $(i, j)$ represents the dot product between query $i$ and key $j$.\n", + " \n", + " $$\\text{Score Matrix} = QK^T$$\n", + "\n", + "2. **Scaling**: Scale the scores obtained in the previous step by dividing by $\\sqrt{d_k}$ to ensure stable gradients, as larger values of $d_k$ can lead to extremely small gradients, which can slow down learning and model convergence.\n", + "\n", + " $$\\text{Scaled Score Matrix} = \\frac{\\text{Score Matrix}}{\\sqrt{d_k}}$$\n", + "\n", + "3. **Optional Masking**: If masking is required (e.g., for padded positions in a batch of sequences), apply the mask by setting the scores for masked positions to a very large negative value, ensuring that they have minimal impact after the softmax step.\n", + "\n", + "4. **Softmax**: Apply the softmax function to the scaled scores along each row. This step converts the scores into probabilities, indicating the importance of each key relative to each query.\n", + "\n", + " $$\\text{Attention Weights} = \\text{softmax}(\\text{Scaled Score Matrix})$$\n", + "\n", + "5. **Output Calculation**: Multiply the attention weights by the value vectors to obtain the final output. This step computes a weighted average of the value vectors, where the weights are determined by the attention scores.\n", + "\n", + " $$\\text{Output} = \\text{Attention Weights} \\times V$$\n", + "\n", + "#### Implementation Tips\n", + "- **Dimensionality**: Ensure the dimensions of your matrices are correct. Matrix multiplication will not be possible if the inner dimensions do not match.\n", + "- **Numerical Stability**: When implementing the softmax function, ensure numerical stability by subtracting the maximum value in each row of the scores matrix before applying the exponential function.\n", + "- **Batch Processing**: If implementing attention in batch, include an additional batch dimension in your matrices (e.g., $Q\\in\\mathbb{R}^{B\\times T\\times d_k}$ for a batch size of $B$) and ensure your implementation supports this.\n", + "- **Testing**: Verify the correctness of your implementation with simple test cases to ensure it behaves as expected.\n", + "\n", + "This framework should provide a clear structure for students to implement scaled dot product attention, enhancing their understanding of its role and functionality in self-attention models." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "jsFoInPLeFk9" + }, + "source": [ + "# Task: Please implement a scaled dot product function" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(2, 2)" + ] + }, + "execution_count": 30, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "dk = 2\n", + "t = 3\n", + "v = torch.randn(t, dk)\n", + "\n", + "len(v[0]),v.shape[-1]" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_54620/1567451330.py:14: UserWarning: Implicit dimension choice for softmax has been deprecated. Change the call to include dim=X as an argument.\n", + " softmax = torch.nn.functional.softmax(scaled)\n" + ] + }, + { + "data": { + "text/plain": [ + "(tensor([[5., 5.],\n", + " [5., 5.],\n", + " [5., 5.]]),\n", + " tensor([[5., 5.],\n", + " [5., 5.],\n", + " [5., 5.]]),\n", + " tensor([[5., 5.],\n", + " [5., 5.],\n", + " [5., 5.]]),\n", + " tensor([[5., 5., 5.],\n", + " [5., 5., 5.]]),\n", + " tensor([[50., 50., 50.],\n", + " [50., 50., 50.],\n", + " [50., 50., 50.]]),\n", + " tensor([[35.3553, 35.3553, 35.3553],\n", + " [35.3553, 35.3553, 35.3553],\n", + " [35.3553, 35.3553, 35.3553]]),\n", + " tensor([[0.3333, 0.3333, 0.3333],\n", + " [0.3333, 0.3333, 0.3333],\n", + " [0.3333, 0.3333, 0.3333]]),\n", + " tensor([[5., 5.],\n", + " [5., 5.],\n", + " [5., 5.]]),\n", + " 2,\n", + " tensor([5., 5.]))" + ] + }, + "execution_count": 31, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "dk = 2\n", + "t = 3\n", + "\n", + "a = torch.zeros(t, dk) + 5\n", + "b = torch.zeros(t, dk) + 5\n", + "v = torch.zeros(t, dk) + 5\n", + "\n", + "bt = b.mT\n", + "\n", + "dot = torch.mm(a, bt)\n", + "\n", + "scaled = dot/math.sqrt(dk)\n", + "\n", + "softmax = torch.nn.functional.softmax(scaled)\n", + "\n", + "output = torch.mm(softmax, v)\n", + "\n", + "a,b,v,bt, dot, scaled, softmax, output, len(a[0]),a[1]" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "id": "XCv8_IzSdut4" + }, + "outputs": [], + "source": [ + "def scaled_dot_product(q, k, v, mask=None):\n", + " # implemented by the student, you can ignore the mask implementation currently\n", + " # just assignment all the mask is on\n", + "\n", + " shape_len = len(k.shape)\n", + "\n", + " transpose = k.mT\n", + " d = k.shape[-1]\n", + "\n", + " score_scale = torch.matmul(q, transpose)/math.sqrt(d)\n", + "\n", + " attention_weight = torch.nn.functional.softmax(score_scale, 1)\n", + "\n", + " output = torch.matmul(attention_weight, v)\n", + "\n", + " return output, attention_weight" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [], + "source": [ + "Q = torch.Tensor([[-0.19737370312213898, -1.0540887117385864, 0.02383515052497387, 0.46185705065727234], [-1.2415547370910645, 0.8366656303405762, 0.3741966784000397, 0.9099264740943909], [0.3436168134212494, 0.6154376268386841, 1.1926648616790771, 1.6477248668670654]])\n", + "K = torch.Tensor([[1.9663442373275757, 0.15551914274692535, -0.8715013861656189, 0.32070425152778625], [-5.85474967956543, 1.7047394514083862, -1.0024793148040771, 1.3307985067367554], [0.06319630891084671, -2.030783176422119, -5.436811447143555, -0.42979586124420166]])\n", + "V = torch.Tensor([[-82.127197265625, 0.9534303545951843, -28.78610610961914, -10.762138366699219], [-16.467313766479492, 60.92831802368164, -36.08392333984375, 31.648052215576172], [20.485767364501953, 45.4570198059082, 15.208494186401367, 31.43212890625]])\n", + "\n", + "ans = scaled_dot_product(Q, K, V)[0].tolist()\n", + "\n", + "pf = pd.read_csv(\"A1_template_template.csv\")\n", + "\n", + "pf.loc[0] = [6644818, Q.tolist(), K.tolist(), V.tolist(), ans]\n", + "\n", + "pf.to_csv('A1_template.csv', sep=',', index=False)" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "wMIShH5wcrUK", + "outputId": "b5e6f270-0cae-4f2c-d388-e0e26ed28b6a" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "tensor([[ 1.2840, 0.9623],\n", + " [ 1.0821, -0.2264],\n", + " [ 0.4840, -1.0348]])\n", + "tensor([[ 0.0392, 0.2658],\n", + " [ 3.1410, 1.9842],\n", + " [ 1.2559, -1.1543]])\n", + "tensor([[ 0.2172, -0.7752],\n", + " [-1.0788, -1.9513],\n", + " [ 0.9364, -1.2229]])\n", + "tensor([[-1.0142, -1.9154],\n", + " [-0.4535, -1.6679],\n", + " [ 0.5474, -1.2476]])\n", + "tensor(-1.2095e-05)\n" + ] + } + ], + "source": [ + "# Test case\n", + "seq_len, d_k = 3, 2\n", + "torch.manual_seed(3025)\n", + "q = torch.randn(seq_len, d_k)\n", + "k = torch.randn(seq_len, d_k)\n", + "v = torch.randn(seq_len, d_k)\n", + "valid = torch.tensor([[-1.0142, -1.9154],\n", + " [-0.4535, -1.6679],\n", + " [ 0.5474, -1.2476]])\n", + "output, attention_weight = scaled_dot_product(q,k,v)\n", + "differences = (output - valid).mean()\n", + "print(q)\n", + "print(k)\n", + "print(v)\n", + "print(output)\n", + "print(differences)\n", + "assert torch.abs(differences) < 0.0001, 'the product must be similar output as expected'" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "DnDq4vT7kEGE" + }, + "source": [ + "# Multi-Head Attention\n", + "\n", + "Multi-Head Attention is an advancement over the scaled dot product attention, enabling the model to concurrently attend to information from different representation subspaces at different positions. This is particularly useful when dealing with complex data where different elements of the sequence may have different types of relevance or relationships to other elements.\n", + "\n", + "#### Concept\n", + "Instead of a single attention \"head,\" Multi-Head Attention uses multiple sets of Query, Key, and Value weight matrices to project the input into different subspaces, allowing the model to capture various aspects of the information. Each set of projections is referred to as a \"head.\" The attention outputs from each head are then concatenated and linearly transformed into the expected dimension.\n", + "\n", + "#### Mathematical Representation\n", + "Given Query, Key, and Value matrices (Q, K, V), the process can be mathematically described as:\n", + "\n", + "$$\n", + "\\begin{split}\n", + " \\text{Multihead}(Q,K,V) & = \\text{Concat}(\\text{head}_1,...,\\text{head}_h)W^{O}\\\\\n", + " \\text{where } \\text{head}_i & = \\text{Attention}(QW_i^Q,KW_i^K, VW_i^V)\n", + "\\end{split}\n", + "$$\n", + "\n", + "In this formula:\n", + "- $W_i^Q \\in \\mathbb{R}^{D \\times d_k}$, $W_i^K \\in \\mathbb{R}^{D \\times d_k}$, and $W_i^V \\in \\mathbb{R}^{D \\times d_v}$ are parameter matrices for the $i$-th attention head.\n", + "- $W^O \\in \\mathbb{R}^{h \\cdot d_k \\times d_{out}}$ is the parameter matrix for the linear transformation after concatenating the heads.\n", + "- $D$ is the dimensionality of the input, $h$ is the number of heads, and $d_{out}$ is the output dimensionality.\n", + "\n", + "#### Integration in Neural Networks\n", + "In a neural network, the Multi-Head Attention layer is typically applied to a feature map $X \\in \\mathbb{R}^{B \\times T \\times d_{\\text{model}}}$, where $B$ is the batch size, $T$ is the sequence length, and $d_{\\text{model}}$ is the dimensionality of the model's hidden layer. Here, $X$ serves as $Q$, $K$, and $V$. The transformation to query, key, and value representations is done using separate learnable weight matrices $W^Q$, $W^K$, and $W^V$.\n", + "\n", + "#### Implementation Notes\n", + "- **Heads**: Each head captures different aspects of the input data. More heads allow the model to simultaneously focus on different subspaces.\n", + "- **Dimensionality**: Ensure the dimensions of your weight matrices and inputs align correctly.\n", + "- **Efficiency**: Despite the increased complexity, Multi-Head Attention can be efficiently parallelized, making it suitable for large-scale problems.\n", + "\n", + "By utilizing Multi-Head Attention, models can gain a more nuanced understanding of the data, capturing various types of relationships within the sequence. This is especially beneficial in complex tasks like language understanding, where different words or phrases may have different kinds of relationships with others in the sequence." + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(tensor([0, 1, 2, 3]), tensor([4, 5, 6, 7]), tensor([ 8, 9, 10]))" + ] + }, + "execution_count": 35, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "torch.arange(11).chunk(3, dim=-1)" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": { + "id": "zOiDz_FkkDDm" + }, + "outputs": [], + "source": [ + "class MultiheadAttention(nn.Module):\n", + " def __init__(self, input_dim, embed_dim, num_heads):\n", + " super().__init__()\n", + " assert embed_dim % num_heads == 0, \"Embedding dimension must be 0 modulo number of heads.\"\n", + "\n", + " self.embed_dim = embed_dim\n", + " self.num_heads = num_heads\n", + " self.head_dim = embed_dim // num_heads\n", + " self.qkv_proj = nn.Linear(input_dim, 3 * embed_dim)\n", + " self.o_proj = nn.Linear(embed_dim, embed_dim)\n", + "\n", + " self._reset_parameters()\n", + "\n", + " def _reset_parameters(self):\n", + " # Original Transformer initialization, see PyTorch documentation\n", + " nn.init.xavier_uniform_(self.qkv_proj.weight)\n", + " self.qkv_proj.bias.data.fill_(0)\n", + " nn.init.xavier_uniform_(self.o_proj.weight)\n", + " self.o_proj.bias.data.fill_(0)\n", + "\n", + " def forward(self, x, mask=None, return_attention=False):\n", + " batch_size, seq_length, embed_dim = x.size()\n", + " qkv = self.qkv_proj(x)\n", + " qkv = qkv.reshape(batch_size, seq_length, self.num_heads, 3 * self.head_dim)\n", + " qkv = qkv.permute(0, 2, 1, 3) # [Batch, Head, SeqLen, Dims]\n", + " q, k, v = qkv.chunk(3, dim=-1)\n", + " values, attention = scaled_dot_product(q, k, v, mask=mask)\n", + " values = values.permute(0, 2, 1, 3) # [Batch, SeqLen, Head, Dims]\n", + " values = values.reshape(batch_size, seq_length, embed_dim)\n", + " o = self.o_proj(values)\n", + "\n", + " if return_attention:\n", + " return o, attention\n", + " else:\n", + " return o" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "sLI_NEVtlSNI" + }, + "source": [ + "# Transformer Encoder\n", + "\n", + "The Transformer Encoder plays a crucial role in transforming input sequences into rich, attention-based representations, primarily used in Sequence-to-Sequence tasks like machine translation. While the original Transformer model consists of both encoder and decoder, the encoder alone has been foundational in numerous advances in NLP and beyond. This section focuses on the encoder's architecture, function, and key components.\n", + "\n", + "#### Overview\n", + "The Transformer Encoder is composed of a stack of $N$ identical layers, each containing two main sub-layers:\n", + "\n", + "1. **Multi-Head Attention Mechanism**: Enables the model to attend to different positions of the input sequence simultaneously.\n", + "2. **Position-wise Feed-Forward Networks**: Consists of fully connected layers applied to each position separately, allowing for individual processing of each sequence element.\n", + "\n", + "#### Encoder Architecture\n", + "Each layer in the encoder includes the following steps:\n", + "\n", + "1. **Input Processing**: The input $x$ (where $x$ can be $Q$, $K$, and $V$) is first passed through the Multi-Head Attention mechanism.\n", + "2. **Residual Connection and Layer Normalization**: The output from the Multi-Head Attention is then added back to the input $x$ through a residual connection, followed by layer normalization:\n", + " \n", + " $$\\text{LayerNorm}(x + \\text{Multihead}(x, x, x))$$\n", + "\n", + " The residual connections help in maintaining the flow of the original input information through the network and are crucial for training deeper models by improving gradient flow. Layer Normalization is used to stabilize the learning process and ensure consistent feature magnitude across sequence elements.\n", + "\n", + "3. **Position-wise Feed-Forward Networks (FFN)**: Each position is processed individually by a two-layered feed-forward network with ReLU activation in between:\n", + " \n", + " $$\n", + " \\begin{split}\n", + " \\text{FFN}(x) & = \\max(0, xW_1 + b_1)W_2 + b_2\\\\\n", + " x & = \\text{LayerNorm}(x + \\text{FFN}(x))\n", + " \\end{split}\n", + " $$\n", + "\n", + " This component allows for further processing of the information added by the attention mechanism, preparing it for the next layer.\n", + "\n", + "#### Considerations in Design\n", + "- **Layer Normalization**: Chosen over Batch Normalization due to its independence from batch size and better performance in language tasks.\n", + "- **Dimensionality of MLP in FFN**: Typically 2-8 times larger than the dimensionality of the input $x$ ($d_{\\text{model}}$), allowing for more complex transformations and faster parallelizable execution.\n", + "- **Dropout**: Applied in MLP and on the outputs of MLP and Multi-Head Attention for regularization.\n", + "\n", + "The Transformer Encoder's architecture, with its repetitive yet intricate structure, allows for effective processing and transformation of sequence data, making it a powerful tool in various sequence modeling tasks. The next steps involve implementing the encoder block, paying close attention to the integration of Multi-Head Attention, residual connections, layer normalization, and feed-forward networks within each layer." + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": { + "id": "a1HiddBnlW4J" + }, + "outputs": [], + "source": [ + "class EncoderBlock(nn.Module):\n", + " def __init__(self, input_dim, num_heads, dim_feedforward, dropout=0.0):\n", + " \"\"\"EncoderBlock.\n", + "\n", + " Args:\n", + " input_dim: Dimensionality of the input\n", + " num_heads: Number of heads to use in the attention block\n", + " dim_feedforward: Dimensionality of the hidden layer in the MLP\n", + " dropout: Dropout probability to use in the dropout layers\n", + " \"\"\"\n", + " super().__init__()\n", + "\n", + " # Attention layer\n", + " self.self_attn = MultiheadAttention(input_dim, input_dim, num_heads)\n", + "\n", + " # Two-layer MLP\n", + " self.linear_net = nn.Sequential(\n", + " nn.Linear(input_dim, dim_feedforward),\n", + " nn.Dropout(dropout),\n", + " nn.ReLU(inplace=True),\n", + " nn.Linear(dim_feedforward, input_dim),\n", + " )\n", + "\n", + " # Layers to apply in between the main layers\n", + " self.norm1 = nn.LayerNorm(input_dim)\n", + " self.norm2 = nn.LayerNorm(input_dim)\n", + " self.dropout = nn.Dropout(dropout)\n", + "\n", + " def forward(self, x, mask=None):\n", + " # Attention part\n", + " attn_out = self.self_attn(x, mask=mask)\n", + " x = x + self.dropout(attn_out)\n", + " x = self.norm1(x)\n", + "\n", + " # MLP part\n", + " linear_out = self.linear_net(x)\n", + " x = x + self.dropout(linear_out)\n", + " x = self.norm2(x)\n", + "\n", + " return x\n", + "\n", + "\n", + "\n", + "\n", + "class TransformerEncoder(nn.Module):\n", + " def __init__(self, num_layers, **block_args):\n", + " super().__init__()\n", + " self.layers = nn.ModuleList([EncoderBlock(**block_args) for _ in range(num_layers)])\n", + "\n", + " def forward(self, x, mask=None):\n", + " for layer in self.layers:\n", + " x = layer(x, mask=mask)\n", + " return x\n", + "\n", + " def get_attention_maps(self, x, mask=None):\n", + " attention_maps = []\n", + " for layer in self.layers:\n", + " _, attn_map = layer.self_attn(x, mask=mask, return_attention=True)\n", + " attention_maps.append(attn_map)\n", + " x = layer(x)\n", + " return attention_maps\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "class PositionalEncoding(nn.Module):\n", + " def __init__(self, d_model, max_len=5000):\n", + " \"\"\"Positional Encoding.\n", + "\n", + " Args:\n", + " d_model: Hidden dimensionality of the input.\n", + " max_len: Maximum length of a sequence to expect.\n", + " \"\"\"\n", + " super().__init__()\n", + "\n", + " # Create matrix of [SeqLen, HiddenDim] representing the positional encoding for max_len inputs\n", + " pe = torch.zeros(max_len, d_model)\n", + " position = torch.arange(0, max_len, dtype=torch.float).unsqueeze(1)\n", + " div_term = torch.exp(torch.arange(0, d_model, 2).float() * (-math.log(10000.0) / d_model))\n", + " pe[:, 0::2] = torch.sin(position * div_term)\n", + " pe[:, 1::2] = torch.cos(position * div_term)\n", + " pe = pe.unsqueeze(0)\n", + "\n", + " # register_buffer => Tensor which is not a parameter, but should be part of the modules state.\n", + " # Used for tensors that need to be on the same device as the module.\n", + " # persistent=False tells PyTorch to not add the buffer to the state dict (e.g. when we save the model)\n", + " self.register_buffer(\"pe\", pe, persistent=False)\n", + "\n", + " def forward(self, x):\n", + " x = x + self.pe[:, : x.size(1)]\n", + " return x" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "bKeMF9xLmQH0" + }, + "source": [ + "# Sequence to Sequence Tasks\n", + "\n", + "Sequence to Sequence (Seq2Seq) tasks involve converting an input sequence into an output sequence, where the input and output may vary in length. This model structure is commonly used in applications like machine translation, text summarization, and more. Typically, a Seq2Seq model comprises an encoder to interpret the input sequence and a decoder to generate the output sequence autoregressively.\n", + "\n", + "#### Simplified Task: Sequence Reversal\n", + "For educational purposes, we'll focus on a simplified Seq2Seq task: reversing a sequence of numbers. Despite its simplicity, this task is a good testbed for understanding Seq2Seq models, especially since it requires capturing long-term dependencies, something traditional RNNs might struggle with, but Transformers are well-equipped to handle.\n", + "\n", + "#### Task Description:\n", + "- **Input**: A sequence of $N$ numbers ranging from $0$ to $M$.\n", + "- **Output**: The reversed sequence of the input.\n", + "\n", + "In Numpy, if our input sequence is $x$, the desired output is $x$[::-1]. Although straightforward, this task provides a clear demonstration of a model's ability to handle sequences and understand dependencies across positions.\n", + "\n", + "#### Implementation Steps:\n", + "- **Create a Dataset Class**: The first step is to create a dataset class that can generate sequences of numbers and their reversed counterparts. This class will be used to train and evaluate the Seq2Seq model.\n", + "\n", + "By starting with this simple task, we can focus on the mechanics and capabilities of the Transformer encoder in handling sequences, setting the stage for tackling more complex Seq2Seq tasks in the future." + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": { + "id": "PSBkeOmtmPhX" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "(16, 10)" + ] + }, + "execution_count": 38, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "class ReverseDataset(data.Dataset):\n", + " def __init__(self, num_categories, seq_len, size):\n", + " super().__init__()\n", + " self.num_categories = num_categories\n", + " self.seq_len = seq_len\n", + " self.size = size\n", + "\n", + " self.data = torch.randint(self.num_categories, size=(self.size, self.seq_len))\n", + "\n", + " def __len__(self):\n", + " return self.size\n", + "\n", + " def __getitem__(self, idx):\n", + " inp_data = self.data[idx]\n", + " labels = torch.flip(inp_data, dims=(0,))\n", + " return inp_data, labels\n", + "\n", + "seq_len = 16\n", + "num_categories = 10\n", + "batch_size = 128\n", + "dataset = partial(ReverseDataset, num_categories, seq_len)\n", + "train_loader = data.DataLoader(dataset(10000), batch_size=batch_size, shuffle=True, drop_last=True, pin_memory=True)\n", + "val_loader = data.DataLoader(dataset(1000), batch_size=64, drop_last=True, shuffle=False)\n", + "\n", + "seq_len, num_categories" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "VZ52A-Hhma4b" + }, + "source": [ + "# Compose the network" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": { + "id": "JxlCGvdomaDJ" + }, + "outputs": [], + "source": [ + "class TransformerPredictor(nn.Module):\n", + " def __init__(\n", + " self,\n", + " input_dim,\n", + " model_dim,\n", + " num_classes,\n", + " num_heads,\n", + " num_layers,\n", + " dropout=0.0,\n", + " input_dropout=0.0,\n", + " ):\n", + " \"\"\"TransformerPredictor.\n", + "\n", + " Args:\n", + " input_dim: Hidden dimensionality of the input\n", + " model_dim: Hidden dimensionality to use inside the Transformer\n", + " num_classes: Number of classes to predict per sequence element\n", + " num_heads: Number of heads to use in the Multi-Head Attention blocks\n", + " num_layers: Number of encoder blocks to use.\n", + " dropout: Dropout to apply inside the model\n", + " input_dropout: Dropout to apply on the input features\n", + " \"\"\"\n", + " super().__init__()\n", + " # Input dim -> Model dim\n", + " self.input_net = nn.Sequential(\n", + " nn.Dropout(input_dropout),\n", + " nn.Linear(input_dim, model_dim)\n", + " )\n", + " # Positional encoding for sequences\n", + " self.positional_encoding = PositionalEncoding(d_model=model_dim)\n", + " # Transformer\n", + " self.transformer = TransformerEncoder(\n", + " num_layers=num_layers,\n", + " input_dim=model_dim,\n", + " dim_feedforward=2 * model_dim,\n", + " num_heads=num_heads,\n", + " dropout=dropout,\n", + " )\n", + " # Output classifier per sequence lement\n", + " self.output_net = nn.Sequential(\n", + " nn.Linear(model_dim, model_dim),\n", + " nn.LayerNorm(model_dim),\n", + " nn.ReLU(inplace=True),\n", + " nn.Dropout(dropout),\n", + " nn.Linear(model_dim, num_classes),\n", + " )\n", + "\n", + " def forward(self, x, mask=None, add_positional_encoding=True):\n", + " \"\"\"\n", + " Args:\n", + " x: Input features of shape [Batch, SeqLen, input_dim]\n", + " mask: Mask to apply on the attention outputs (optional)\n", + " add_positional_encoding: If True, we add the positional encoding to the input.\n", + " Might not be desired for some tasks.\n", + " \"\"\"\n", + " x = self.input_net(x)\n", + " if add_positional_encoding:\n", + " x = self.positional_encoding(x)\n", + " x = self.transformer(x, mask=mask)\n", + " x = self.output_net(x)\n", + " return x\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "uUuW7DbBnjsS" + }, + "source": [ + "# Task: Writing Training Loop" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "1" + ] + }, + "execution_count": 40, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "torch.cuda.device_count()" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'NVIDIA GeForce RTX 3090 Ti'" + ] + }, + "execution_count": 41, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "torch.cuda.get_device_name(0)" + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "cZaOx-7qni7y", + "outputId": "a181d978-f0e0-451b-9e95-587ce9d8c2bd" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "EPOCH 1:\n", + "LOSS train 2.3047391891479494\n", + "EPOCH 2:\n", + "LOSS train 2.3059343338012694\n", + "EPOCH 3:\n", + "LOSS train 2.303365612030029\n", + "EPOCH 4:\n", + "LOSS train 2.278448724746704\n", + "EPOCH 5:\n", + "LOSS train 2.271419906616211\n", + "EPOCH 6:\n", + "LOSS train 2.272932434082031\n", + "EPOCH 7:\n", + "LOSS train 2.2925734519958496\n", + "EPOCH 8:\n", + "LOSS train 2.273321104049683\n", + "EPOCH 9:\n", + "LOSS train 2.2358773708343507\n", + "EPOCH 10:\n", + "LOSS train 2.213832139968872\n", + "EPOCH 11:\n", + "LOSS train 2.1974945068359375\n", + "EPOCH 12:\n", + "LOSS train 2.138561820983887\n", + "EPOCH 13:\n", + "LOSS train 2.131034755706787\n", + "EPOCH 14:\n", + "LOSS train 2.092240905761719\n", + "EPOCH 15:\n", + "LOSS train 2.028573417663574\n", + "EPOCH 16:\n", + "LOSS train 2.009480619430542\n", + "EPOCH 17:\n", + "LOSS train 2.0119842529296874\n", + "EPOCH 18:\n", + "LOSS train 1.966892457008362\n", + "EPOCH 19:\n", + "LOSS train 1.9739716291427611\n", + "EPOCH 20:\n", + "LOSS train 1.9691336631774903\n", + "EPOCH 21:\n", + "LOSS train 1.9692431688308716\n", + "EPOCH 22:\n", + "LOSS train 1.9720533609390258\n", + "EPOCH 23:\n", + "LOSS train 1.9636199712753295\n", + "EPOCH 24:\n", + "LOSS train 2.0134324550628664\n", + "EPOCH 25:\n", + "LOSS train 1.9760711431503295\n", + "EPOCH 26:\n", + "LOSS train 1.9674718379974365\n", + "EPOCH 27:\n", + "LOSS train 1.9762607574462892\n", + "EPOCH 28:\n", + "LOSS train 1.9764994859695435\n", + "EPOCH 29:\n", + "LOSS train 1.9699514150619506\n", + "EPOCH 30:\n", + "LOSS train 1.95670006275177\n", + "EPOCH 31:\n", + "LOSS train 1.946057105064392\n", + "EPOCH 32:\n", + "LOSS train 1.9565371990203857\n", + "EPOCH 33:\n", + "LOSS train 1.9599705457687377\n", + "EPOCH 34:\n", + "LOSS train 1.9696622133255004\n", + "EPOCH 35:\n", + "LOSS train 1.9993957996368408\n", + "EPOCH 36:\n", + "LOSS train 1.9636467695236206\n", + "EPOCH 37:\n", + "LOSS train 1.980830192565918\n", + "EPOCH 38:\n", + "LOSS train 1.9654539108276368\n", + "EPOCH 39:\n", + "LOSS train 1.9689129829406737\n", + "EPOCH 40:\n", + "LOSS train 1.955962347984314\n", + "EPOCH 41:\n", + "LOSS train 1.9647478580474853\n", + "EPOCH 42:\n", + "LOSS train 1.9532663106918335\n", + "EPOCH 43:\n", + "LOSS train 1.9503717422485352\n", + "EPOCH 44:\n", + "LOSS train 1.9499874591827393\n", + "EPOCH 45:\n", + "LOSS train 1.9529696941375732\n", + "EPOCH 46:\n", + "LOSS train 1.9518198251724244\n", + "EPOCH 47:\n", + "LOSS train 1.9523835182189941\n", + "EPOCH 48:\n", + "LOSS train 1.9561205148696899\n", + "EPOCH 49:\n", + "LOSS train 1.9675297260284423\n", + "EPOCH 50:\n", + "LOSS train 2.123178768157959\n", + "EPOCH 51:\n", + "LOSS train 1.970911931991577\n", + "EPOCH 52:\n", + "LOSS train 1.9587018251419068\n", + "EPOCH 53:\n", + "LOSS train 1.9622526168823242\n", + "EPOCH 54:\n", + "LOSS train 1.9551706790924073\n", + "EPOCH 55:\n", + "LOSS train 1.953707218170166\n", + "EPOCH 56:\n", + "LOSS train 1.9466333389282227\n", + "EPOCH 57:\n", + "LOSS train 1.9582770824432374\n", + "EPOCH 58:\n", + "LOSS train 1.9466321229934693\n", + "EPOCH 59:\n", + "LOSS train 1.9557215929031373\n", + "EPOCH 60:\n", + "LOSS train 1.9505679607391357\n", + "EPOCH 61:\n", + "LOSS train 1.9520682334899901\n", + "EPOCH 62:\n", + "LOSS train 1.955586814880371\n", + "EPOCH 63:\n", + "LOSS train 1.9475157499313354\n", + "EPOCH 64:\n", + "LOSS train 1.9377191305160522\n", + "EPOCH 65:\n", + "LOSS train 1.938973307609558\n", + "EPOCH 66:\n", + "LOSS train 1.9429319143295287\n", + "EPOCH 67:\n", + "LOSS train 1.9438214540481566\n", + "EPOCH 68:\n", + "LOSS train 1.9364233016967773\n", + "EPOCH 69:\n", + "LOSS train 1.9589627027511596\n", + "EPOCH 70:\n", + "LOSS train 1.9416004180908204\n", + "EPOCH 71:\n", + "LOSS train 1.9382025003433228\n", + "EPOCH 72:\n", + "LOSS train 1.9310474157333375\n", + "EPOCH 73:\n", + "LOSS train 1.939470887184143\n", + "EPOCH 74:\n", + "LOSS train 1.9370584964752198\n", + "EPOCH 75:\n", + "LOSS train 1.9400960445404052\n", + "EPOCH 76:\n", + "LOSS train 1.9455738306045531\n", + "EPOCH 77:\n", + "LOSS train 1.9308057308197022\n", + "EPOCH 78:\n", + "LOSS train 1.9302024841308594\n", + "EPOCH 79:\n", + "LOSS train 1.9345638751983643\n", + "EPOCH 80:\n", + "LOSS train 1.9394316673278809\n", + "EPOCH 81:\n", + "LOSS train 1.9305338621139527\n", + "EPOCH 82:\n", + "LOSS train 1.9336636304855346\n", + "EPOCH 83:\n", + "LOSS train 1.921869659423828\n", + "EPOCH 84:\n", + "LOSS train 1.9273949146270752\n", + "EPOCH 85:\n", + "LOSS train 1.916986870765686\n", + "EPOCH 86:\n", + "LOSS train 1.9170085191726685\n", + "EPOCH 87:\n", + "LOSS train 1.9086025476455688\n", + "EPOCH 88:\n", + "LOSS train 1.911173439025879\n", + "EPOCH 89:\n", + "LOSS train 1.8953789472579956\n", + "EPOCH 90:\n", + "LOSS train 1.9057527542114259\n", + "EPOCH 91:\n", + "LOSS train 1.883132243156433\n", + "EPOCH 92:\n", + "LOSS train 1.895125651359558\n", + "EPOCH 93:\n", + "LOSS train 1.8788848161697387\n", + "EPOCH 94:\n", + "LOSS train 1.870681929588318\n", + "EPOCH 95:\n", + "LOSS train 1.8580753803253174\n", + "EPOCH 96:\n", + "LOSS train 1.8578021287918092\n", + "EPOCH 97:\n", + "LOSS train 1.8478908300399781\n", + "EPOCH 98:\n", + "LOSS train 1.8258821010589599\n", + "EPOCH 99:\n", + "LOSS train 1.8657661914825439\n", + "EPOCH 100:\n", + "LOSS train 1.8161733627319336\n" + ] + } + ], + "source": [ + "input_dim = 10 # This needs to be 10 because yes\n", + "model_dim = 1024 # size of the hidden layer (transformers)\n", + "num_classes = train_loader.dataset.num_categories\n", + "num_heads = 8\n", + "num_layers = 1\n", + "with torch.cuda.device(torch.device('cuda')):\n", + " \n", + " # please create the model\n", + " model = TransformerPredictor(input_dim, model_dim, num_classes, num_heads, num_layers).cuda()\n", + "\n", + " # please create the optimizer\n", + " optimizer = torch.optim.Adam(model.parameters())\n", + " loss_fn = torch.nn.CrossEntropyLoss()\n", + " # please train the model, with the whole training pipeline\n", + " \n", + " def train(epoch_index, tb_writer):\n", + " running_loss = 0\n", + " last_loss = 0\n", + " \n", + " for i, data in enumerate(train_loader):\n", + " inputs, labels = data\n", + " \n", + " # inputs = inputs.to(torch.float32)\n", + " inputs = F.one_hot(inputs, num_classes=num_classes).float().cuda()\n", + "\n", + " labels = labels.cuda()\n", + " \n", + " outputs = model(inputs)\n", + " \n", + " loss = loss_fn(outputs.view(-1, 10), labels.view(-1))\n", + " optimizer.zero_grad()\n", + " loss.backward()\n", + " \n", + " # Adjust learning weights\n", + " optimizer.step()\n", + " \n", + " running_loss += loss.item()\n", + " if i % 5 == 0:\n", + " last_loss = running_loss / 5 # loss per batch\n", + " # print(' batch {} loss: {}'.format(i + 1, last_loss))\n", + " tb_x = epoch_index * len(train_loader) + i + 1\n", + " # tb_writer.add_scalar('Loss/train', last_loss, tb_x)\n", + " running_loss = 0.\n", + " \n", + " return last_loss\n", + " \n", + " \n", + " timestamp = datetime.now().strftime('%Y%m%d_%H%M%S')\n", + " # writer = torch.utils.tensorboard.writer.SummaryWriter('runs/fashion_trainer_{}'.format(timestamp))\n", + " epoch_number = 0\n", + " \n", + " EPOCHS = 100\n", + " \n", + " best_vloss = 1_000_000.\n", + " \n", + " for epoch in range(EPOCHS):\n", + " print('EPOCH {}:'.format(epoch_number + 1))\n", + " \n", + " # Make sure gradient tracking is on, and do a pass over the data\n", + " model.train(True)\n", + " avg_loss = train(epoch_number, None)\n", + " \n", + " avg_vloss = 0\n", + " print('LOSS train {}'.format(avg_loss))\n", + " \n", + " epoch_number += 1\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "NVqbotkCrCSy" + }, + "source": [ + "# Evaluation\n", + "Here is the evaluation code, can you do better than 2.0?" + ] + }, + { + "cell_type": "code", + "execution_count": 218, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "vkRNkGBspuZh", + "outputId": "f5a0ff6d-e24c-4a94-d5f8-8113956f3b18" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Validation Loss: 2.3046957651774087\n" + ] + } + ], + "source": [ + "# Validating the validation loss\n", + "criterion = nn.CrossEntropyLoss()\n", + "# Validation loop\n", + "model.eval()\n", + "with torch.no_grad():\n", + " val_loss = 0\n", + " for inputs, labels in val_loader:\n", + " inp_data = F.one_hot(inputs, num_classes=10).float().cuda()\n", + " outputs = model(inp_data)\n", + " loss = criterion(outputs.view(1024,10), labels.view(-1).cuda())\n", + " val_loss += loss.item()\n", + " print(f\"Validation Loss: {val_loss / len(val_loader)}\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "colab": { + "provenance": [], + "toc_visible": true + }, + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.7" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/Lab1_2/Lab1&2_Transformers.ipynb b/Lab1_2/Lab1&2_Transformers.ipynb new file mode 100644 index 0000000..a5a6b3f --- /dev/null +++ b/Lab1_2/Lab1&2_Transformers.ipynb @@ -0,0 +1,93 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "Cv-9Vzunb_tf" + }, + "source": [ + "# Import Necessary Library" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "id": "4f-K54nHb-Uq" + }, + "outputs": [], + "source": [ + "import torch\n", + "import torch.nn as nn\n", + "import torch.nn.functional as F\n", + "import torch.optim as optim\n", + "import torch.utils.data as data\n", + "import math\n", + "import os\n", + "import urllib.request\n", + "import pandas as pd\n", + "from functools import partial\n", + "from urllib.error import HTTPError\n", + "from datetime import datetime" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "id": "XCv8_IzSdut4" + }, + "outputs": [], + "source": [ + "def scaled_dot_product(q, k, v, mask=None):\n", + " # implemented by the student, you can ignore the mask implementation currently\n", + " # just assignment all the mask is on\n", + "\n", + " shape_len = len(k.shape)\n", + "\n", + " transpose = k.mT\n", + " d = k.shape[-1]\n", + "\n", + " score_scale = torch.matmul(q, transpose)/math.sqrt(d)\n", + "\n", + " attention_weight = torch.nn.functional.softmax(score_scale, 1)\n", + "\n", + " output = torch.matmul(attention_weight, v)\n", + "\n", + " return output, attention_weight" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "colab": { + "provenance": [], + "toc_visible": true + }, + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.7" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/Lab3/Week3_Autoencoder+MAE - Copy.ipynb b/Lab3/Week3_Autoencoder+MAE - Copy.ipynb new file mode 100644 index 0000000..97d3ff1 --- /dev/null +++ b/Lab3/Week3_Autoencoder+MAE - Copy.ipynb @@ -0,0 +1,7058 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "YLLecc85VRCL" + }, + "source": [ + "# Introduction & Import Necessary Setup\n", + "In this labsheet, we'll delve into the fascinating world of autoencoders (AEs), a type of neural network renowned for its ability to compress and reconstruct data. Autoencoders work by first encoding input data, such as images, into a compact feature vector through an encoder network. This process effectively distills the essence of the data into a smaller, more manageable form. The feature vector, often referred to as the \"bottleneck,\" plays a crucial role in this compression process, allowing us to represent the input data with fewer features.\n", + "\n", + "Following compression, a second neural network, known as the decoder, takes over to reconstruct the original data from the compressed feature vector. This remarkable ability to compress and then reconstruct data makes autoencoders extremely valuable in various applications, including data compression and image comparison at a more meaningful level than mere pixel-by-pixel analysis.\n", + "\n", + "Moreover, our exploration will not stop at the autoencoder framework itself. We will also introduce the concept of \"deconvolution\" (also known as transposed convolution), a powerful operator used to enlarge feature maps in both height and width dimensions. Deconvolution networks are indispensable in scenarios where we begin with a compact feature vector and aim to generate a full-sized image. This technique is pivotal in various advanced neural network applications, such as Variational Autoencoders (VAEs), Generative Adversarial Networks (GANs), and super-resolution.\n", + "\n", + "To kick things off, we'll start by importing our standard libraries, setting the stage for our deep dive into the inner workings and applications of autoencoders." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "4e2G_wAgIWxD", + "outputId": "a8fc0cbc-1aa7-4dd6-a94e-cf5320483c9f" + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_192718/407458918.py:11: DeprecationWarning: `set_matplotlib_formats` is deprecated since IPython 7.23, directly use `matplotlib_inline.backend_inline.set_matplotlib_formats()`\n", + " set_matplotlib_formats('svg', 'pdf') # For export\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Device: cuda:0\n" + ] + } + ], + "source": [ + "## Standard libraries\n", + "import os\n", + "import json\n", + "import math\n", + "import numpy as np\n", + "\n", + "## Imports for plotting\n", + "import matplotlib.pyplot as plt\n", + "%matplotlib inline\n", + "from IPython.display import set_matplotlib_formats\n", + "set_matplotlib_formats('svg', 'pdf') # For export\n", + "from matplotlib.colors import to_rgb\n", + "import matplotlib\n", + "matplotlib.rcParams['lines.linewidth'] = 2.0\n", + "## Progress bar\n", + "from tqdm.notebook import tqdm\n", + "\n", + "## PyTorch\n", + "import torch\n", + "import torch.nn as nn\n", + "import torch.nn.functional as F\n", + "import torch.utils.data as data\n", + "import torch.optim as optim\n", + "# Torchvision\n", + "import torchvision\n", + "from torchvision.datasets import CIFAR10\n", + "from torchvision import transforms\n", + "\n", + "DATASET_PATH = \"dataset\"\n", + "\n", + "device = torch.device(\"cuda:0\") if torch.cuda.is_available() else torch.device(\"cpu\")\n", + "print(\"Device:\", device)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "INLuLKepWdvC" + }, + "source": [ + "# Download and setup the dataset\n", + "In this labsheet, our focus shifts to the CIFAR10 dataset, a collection known for its rich, colored images. Each image within CIFAR10 is equipped with 3 color channels and boasts a resolution of 32x32 pixels. This characteristic is particularly advantageous when working with autoencoders, as they are not bound by the constraints of probabilistic image modeling.\n", + "\n", + "Should you already have the CIFAR10 dataset downloaded in a different directory, it's important to adjust the DATASET_PATH variable accordingly. This step ensures you avoid unnecessary additional downloads, streamlining your workflow and allowing you to dive into the practical exercises more swiftly." + ] + }, + { + "cell_type": "code", + "execution_count": 105, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "yH_BjGbuJIrJ", + "outputId": "fc3e192f-fd42-4cd6-a5f8-82971331940b" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Files already downloaded and verified\n", + "Files already downloaded and verified\n" + ] + }, + { + "ename": "AttributeError", + "evalue": "'list' object has no attribute 'DataLoader'", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[105], line 13\u001b[0m\n\u001b[1;32m 10\u001b[0m test_set \u001b[38;5;241m=\u001b[39m CIFAR10(root\u001b[38;5;241m=\u001b[39mDATASET_PATH, train\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m, transform\u001b[38;5;241m=\u001b[39mtransform, download\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m)\n\u001b[1;32m 12\u001b[0m \u001b[38;5;66;03m# We define a set of data loaders that we can use for various purposes later.\u001b[39;00m\n\u001b[0;32m---> 13\u001b[0m train_loader \u001b[38;5;241m=\u001b[39m \u001b[43mdata\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mDataLoader\u001b[49m(train_set, batch_size\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m256\u001b[39m, shuffle\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m, drop_last\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m, pin_memory\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m, num_workers\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m4\u001b[39m)\n\u001b[1;32m 14\u001b[0m val_loader \u001b[38;5;241m=\u001b[39m data\u001b[38;5;241m.\u001b[39mDataLoader(val_set, batch_size\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m256\u001b[39m, shuffle\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m, drop_last\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m, num_workers\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m4\u001b[39m)\n\u001b[1;32m 15\u001b[0m test_loader \u001b[38;5;241m=\u001b[39m data\u001b[38;5;241m.\u001b[39mDataLoader(test_set, batch_size\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m256\u001b[39m, shuffle\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m, drop_last\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m, num_workers\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m4\u001b[39m)\n", + "\u001b[0;31mAttributeError\u001b[0m: 'list' object has no attribute 'DataLoader'" + ] + } + ], + "source": [ + "# Transformations applied on each image => only make them a tensor\n", + "transform = transforms.Compose([transforms.ToTensor(),\n", + " transforms.Normalize((0.5,),(0.5,))])\n", + "\n", + "# Loading the training dataset. We need to split it into a training and validation part\n", + "train_dataset = CIFAR10(root=DATASET_PATH, train=True, transform=transform, download=True)\n", + "train_set, val_set = torch.utils.data.random_split(train_dataset, [45000, 5000])\n", + "\n", + "# Loading the test set\n", + "test_set = CIFAR10(root=DATASET_PATH, train=False, transform=transform, download=True)\n", + "\n", + "# We define a set of data loaders that we can use for various purposes later.\n", + "train_loader = data.DataLoader(train_set, batch_size=256, shuffle=True, drop_last=True, pin_memory=True, num_workers=4)\n", + "val_loader = data.DataLoader(val_set, batch_size=256, shuffle=False, drop_last=False, num_workers=4)\n", + "test_loader = data.DataLoader(test_set, batch_size=256, shuffle=False, drop_last=False, num_workers=4)\n", + "\n", + "def get_train_images(num):\n", + " return torch.stack([train_dataset[i][0] for i in range(num)], dim=0)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "4Jl0CTGSkym-" + }, + "source": [ + "# Building the autoencoder\n", + "\n", + "In general, an autoencoder consists of an **encoder** that maps the input $x$ to a lower-dimensional feature vector $z$, and a **decoder** that reconstructs the input $\\hat{x}$ from $z$. We train the model by comparing $x$ to $\\hat{x}$ and optimizing the parameters to increase the similarity between $x$ and $\\hat{x}$. See below for a small illustration of the autoencoder framework.\n", + "\n", + "\n", + "![img](https://raw.githubusercontent.com/hqsiswiliam/COM3025_Torch/main/autoencoder.png)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "_lhJZFR3k1e_" + }, + "source": [ + "\n", + "For an educational purpose revision in markdown format, the text could be enhanced as follows:\n", + "\n", + "To kick off our exploration, we initiate with the construction of the encoder. This component is fundamentally a deep convolutional network tailored for progressively diminishing the image's dimensions. This diminution is achieved through the use of strided convolutions, which methodically reduce the image's size layer by layer. Following the thrice-executed downscaling process, we transition the architecture from convolutional layers to a flattened feature representation. This is achieved by flattening the spatial features into a single vector, which is then processed through several linear layers. As a result, we obtain the latent representation, denoted as\n", + "$z$, encapsulating the compressed essence of the input image. The size of this latent vector, $d$, is adjustable, providing flexibility in the encoding capacity of our network." + ] + }, + { + "cell_type": "code", + "execution_count": 59, + "metadata": { + "id": "i6fToFroJMMT" + }, + "outputs": [], + "source": [ + "class Encoder(nn.Module):\n", + "\n", + " def __init__(self,\n", + " num_input_channels : int,\n", + " base_channel_size : int,\n", + " latent_dim : int,\n", + " act_fn : object = nn.GELU):\n", + " \"\"\"\n", + " Inputs:\n", + " - num_input_channels : Number of input channels of the image. For CIFAR, this parameter is 3\n", + " - base_channel_size : Number of channels we use in the first convolutional layers. Deeper layers might use a duplicate of it.\n", + " - latent_dim : Dimensionality of latent representation z\n", + " - act_fn : Activation function used throughout the encoder network\n", + " \"\"\"\n", + " super().__init__()\n", + " c_hid = base_channel_size\n", + " self.net = nn.Sequential(\n", + " nn.Conv2d(num_input_channels, c_hid, kernel_size=3, padding=1, stride=2), # 32x32 => 16x16\n", + " act_fn(),\n", + " nn.Conv2d(c_hid, c_hid, kernel_size=3, padding=1),\n", + " act_fn(),\n", + " nn.Conv2d(c_hid, 2*c_hid, kernel_size=3, padding=1, stride=2), # 16x16 => 8x8\n", + " act_fn(),\n", + " nn.Conv2d(2*c_hid, 2*c_hid, kernel_size=3, padding=1),\n", + " act_fn(),\n", + " nn.Conv2d(2*c_hid, 2*c_hid, kernel_size=3, padding=1, stride=2), # 8x8 => 4x4\n", + " act_fn(),\n", + " nn.Flatten(), # Image grid to single feature vector\n", + " nn.Linear(2*16*c_hid, latent_dim)\n", + " )\n", + "\n", + " # self.flatten = nn.Sequential(\n", + " # nn.Flatten(), # Image grid to single feature vector\n", + " # nn.Linear(2*16*c_hid, latent_dim)\n", + " # )\n", + "\n", + " def forward(self, x):\n", + " # x = self.net(x)\n", + "\n", + " # print(x.shape)\n", + " \n", + " # return self.flatten(x)\n", + "\n", + " return self.net(x)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "AOOi0C4wm99b" + }, + "source": [ + "# Task1\n", + "Now Complete the decoder implementation" + ] + }, + { + "cell_type": "code", + "execution_count": 133, + "metadata": { + "id": "kV2FkEk6JTjk" + }, + "outputs": [], + "source": [ + "class Decoder(nn.Module):\n", + "\n", + " def __init__(self,\n", + " num_input_channels : int,\n", + " base_channel_size : int,\n", + " latent_dim : int,\n", + " act_fn : object = nn.GELU):\n", + " \"\"\"\n", + " Inputs:\n", + " - num_input_channels : Number of channels of the image to reconstruct. For CIFAR, this parameter is 3\n", + " - base_channel_size : Number of channels we use in the last convolutional layers. Early layers might use a duplicate of it.\n", + " - latent_dim : Dimensionality of latent representation z\n", + " - act_fn : Activation function used throughout the decoder network\n", + " \"\"\"\n", + " super().__init__()\n", + " c_hid = base_channel_size\n", + " self.net = nn.Sequential(\n", + " nn.Linear(latent_dim, 2*16*c_hid),\n", + " act_fn(),\n", + " nn.Unflatten(1, (2*c_hid, 4, 4)),\n", + " nn.ConvTranspose2d(2*c_hid, 2*c_hid, kernel_size=3, padding=1, stride=2, output_padding=1), # 8x8 <= 4x4\n", + " act_fn(),\n", + " nn.Conv2d(2*c_hid, 2*c_hid, kernel_size=3, padding=1),\n", + " act_fn(),\n", + " nn.ConvTranspose2d(2*c_hid, c_hid, kernel_size=3, padding=1, stride=2, output_padding=1), # 16x16 <= 8x8\n", + " act_fn(),\n", + " nn.Conv2d(c_hid, c_hid, kernel_size=3, padding=1),\n", + " act_fn(), \n", + " nn.ConvTranspose2d(c_hid, num_input_channels, kernel_size=3, padding=1, stride=2, output_padding=1), # 32x32 <= 16x16\n", + " nn.Tanh(),\n", + " # nn.Sigmoid(),\n", + " )\n", + " # You code goes here.\n", + "\n", + " def forward(self, x):\n", + " return self.net(x)\n", + " # You code goes here." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "-DYpDGTznGVL" + }, + "source": [ + "# Combining Encoder and Decoder\n", + "## Loss Function: Mean Squared Error (MSE)\n", + "\n", + "For our loss function, we opt for the Mean Squared Error (MSE). MSE is particularly effective in emphasizing the significance of accurately predicting pixel values that are substantially misestimated by the network. For instance, a minor deviation, such as predicting 127 instead of 128, is deemed less critical. However, larger discrepancies, like confusing a pixel value of 0 with 128, are considered more severe and detrimental to the reconstruction quality.\n", + "\n", + "Unlike Variational Autoencoders (VAEs) that predict the probability for each pixel value, we employ MSE as a straightforward distance measure. This approach significantly reduces the number of parameters, streamlining the training process. To enhance our understanding of the per-pixel performance, we calculate the summed squared error, averaged across the batch dimension. It's important to note that alternative aggregations (mean or sum) yield equivalent outcomes in terms of resulting parameters.\n", + "\n", + "### Limitations of MSE\n", + "\n", + "Despite its advantages, MSE is not without drawbacks. Primarily, it tends to produce blurrier images, as it inherently removes small noise and high-frequency patterns, which contribute minimally to the overall error. To mitigate this and achieve more realistic reconstructions, integrating Generative Adversarial Networks (GANs) with autoencoders has proven effective. This hybrid approach is explored in various studies ([example 1](https://arxiv.org/abs/1704.02304), [example 2](https://arxiv.org/abs/1511.05644), and [slides](http://elarosca.net/slides/iccv_autoencoder_gans.pdf)).\n", + "\n", + "Furthermore, MSE may not always accurately reflect visual similarity between images. A case in point is when an autoencoder produces an image that is slightly shifted—despite the near-identical appearance, the MSE can significantly increase, showcasing a limitation in capturing true visual fidelity. A potential solution involves leveraging a pre-trained CNN to measure distance based on visual features extracted from lower layers, offering a more nuanced comparison than pixel-level MSE.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 134, + "metadata": { + "id": "hd0hdMVuJxhZ" + }, + "outputs": [], + "source": [ + "class Autoencoder(nn.Module):\n", + "\n", + " def __init__(self,\n", + " base_channel_size: int,\n", + " latent_dim: int,\n", + " encoder_class : object = Encoder,\n", + " decoder_class : object = Decoder,\n", + " num_input_channels: int = 3,\n", + " width: int = 32,\n", + " height: int = 32):\n", + " super().__init__()\n", + " # Creating encoder and decoder\n", + " self.encoder = encoder_class(num_input_channels, base_channel_size, latent_dim)\n", + " self.decoder = decoder_class(num_input_channels, base_channel_size, latent_dim)\n", + " # Example input array needed for visualizing the graph of the network\n", + " self.example_input_array = torch.zeros(2, num_input_channels, width, height)\n", + "\n", + " def forward(self, x):\n", + " z = self.encoder(x)\n", + " x_hat = self.decoder(z)\n", + " return x_hat\n", + "\n", + " def _get_reconstruction_loss(self, batch):\n", + " x = batch # We do not need the labels\n", + " x_hat = self.forward(x)\n", + " loss = F.mse_loss(x, x_hat, reduction=\"none\")\n", + " loss = loss.sum(dim=[1,2,3]).mean(dim=[0])\n", + " return loss\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "AOHmolo8nkBM" + }, + "source": [ + "# Utility code for comparing Images" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 779 + }, + "id": "_ttCZos4JWpr", + "outputId": "aaae7d91-f4ca-4f36-a495-8e4380992e2e" + }, + "outputs": [ + { + "data": { + "application/pdf": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "def compare_imgs(img1, img2, title_prefix=\"\"):\n", + " # Calculate MSE loss between both images\n", + " loss = F.mse_loss(img1, img2, reduction=\"sum\")\n", + " # Plot images for visual comparison\n", + " grid = torchvision.utils.make_grid(torch.stack([img1, img2], dim=0), nrow=2, normalize=True)\n", + " grid = grid.permute(1, 2, 0)\n", + " plt.figure(figsize=(4,2))\n", + " plt.title(f\"{title_prefix} Loss: {loss.item():4.2f}\")\n", + " plt.imshow(grid)\n", + " plt.axis('off')\n", + " plt.show()\n", + "\n", + "for i in range(2):\n", + " # Load example image\n", + " img, _ = train_dataset[i]\n", + " img_mean = img.mean(dim=[1,2], keepdims=True)\n", + "\n", + " # Shift image by one pixel\n", + " SHIFT = 1\n", + " img_shifted = torch.roll(img, shifts=SHIFT, dims=1)\n", + " img_shifted = torch.roll(img_shifted, shifts=SHIFT, dims=2)\n", + " img_shifted[:,:1,:] = img_mean\n", + " img_shifted[:,:,:1] = img_mean\n", + " compare_imgs(img, img_shifted, \"Shifted -\")\n", + "\n", + " # Set half of the image to zero\n", + " img_masked = img.clone()\n", + " img_masked[:,:img_masked.shape[1]//2,:] = img_mean\n", + " compare_imgs(img, img_masked, \"Masked -\")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "Pwn3hj6hnq3z" + }, + "source": [ + "# Task2\n", + "Add training code to train the AutoEncoder" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 337, + "referenced_widgets": [ + "1c5e517cebbc4d96b4d260676eca961f", + "20f188a8bf53479983bdf9c2df2a55e0", + "6e196069369c4126acdc53ac6da328aa", + "f3b39579ce11475ea2ca67198c64f66a", + "b15d030dacc84d3d89a38d3f48c094e7", + "f0a2ddf4f8a54ccd8e301b54943bb88c", + "d9164be3d674410da5eee962bc243727", + "ba9e7a3a9bbb46faaabdcc944650f4af", + "a4c68ce78f024f9aa4bd2fe3ac296d25", + "20a2d55104cc4a98a06c5bf50a80b51c", + 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is not defined", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[2], line 4\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[38;5;66;03m# for batch in tqdm(train_loader, total=len(train_loader)):\u001b[39;00m\n\u001b[1;32m 2\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mtorch\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01moptim\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01moptim\u001b[39;00m\n\u001b[0;32m----> 4\u001b[0m model \u001b[38;5;241m=\u001b[39m \u001b[43mAutoencoder\u001b[49m(\u001b[38;5;241m64\u001b[39m, \u001b[38;5;241m128\u001b[39m, ) \u001b[38;5;66;03m# you code here\u001b[39;00m\n\u001b[1;32m 5\u001b[0m model\u001b[38;5;241m.\u001b[39mto(device)\n\u001b[1;32m 6\u001b[0m optimizer \u001b[38;5;241m=\u001b[39m torch\u001b[38;5;241m.\u001b[39moptim\u001b[38;5;241m.\u001b[39mAdam(model\u001b[38;5;241m.\u001b[39mparameters(), lr\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1e-3\u001b[39m) \u001b[38;5;66;03m# your code here\u001b[39;00m\n", + "\u001b[0;31mNameError\u001b[0m: name 'Autoencoder' is not defined" + ] + } + ], + "source": [ + "# for batch in tqdm(train_loader, total=len(train_loader)):\n", + "import torch.optim as optim\n", + "\n", + "model = Autoencoder(64, 128, ) # you code here\n", + "model.to(device)\n", + "optimizer = torch.optim.Adam(model.parameters(), lr=1e-3) # your code here\n", + "scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(optimizer, 'min') # your code here, can use ReduceLROnPlateau\n", + "# Write training loop here\n", + "\n", + "loss_fn = nn.MSELoss()\n", + "\n", + "n_epoch = 40\n", + "model.train()\n", + "for epoch in range(n_epoch):\n", + " print(f\"\\nEpoch {epoch}:\")\n", + "\n", + " avg_loss = 0\n", + "\n", + " for i, data in enumerate(train_loader):\n", + " inputs, _ = data\n", + "\n", + " inputs = inputs.cuda()\n", + "\n", + " loss = model._get_reconstruction_loss(inputs) #loss_fn(outputs, inputs)\n", + "\n", + " optimizer.zero_grad()\n", + "\n", + " loss.backward()\n", + "\n", + " optimizer.step()\n", + "\n", + " avg_loss += loss\n", + "\n", + " print(f'\\rBatch: {i}: Loss:{loss} avg_Loss: {avg_loss/(i + 1)} ', end='')\n", + "\n", + " scheduler.step(loss)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 144, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 324 + }, + "id": "5OfaUMh-U3eJ", + "outputId": "bd25e0cd-7c0e-40fe-c472-527750e268cc" + }, + "outputs": [ + { + "data": { + "application/pdf": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "def visualize_reconstructions(model, input_imgs):\n", + " # Reconstruct images\n", + " model.eval()\n", + " with torch.no_grad():\n", + " reconst_imgs = model(input_imgs.to(device))\n", + " reconst_imgs = reconst_imgs.cpu()\n", + "\n", + " # Plotting\n", + " imgs = torch.stack([input_imgs, reconst_imgs], dim=1).flatten(0,1)\n", + " grid = torchvision.utils.make_grid(imgs, nrow=4, normalize=True)\n", + " grid = grid.permute(1, 2, 0)\n", + " plt.figure(figsize=(7,4.5))\n", + " plt.title(f\"Reconstructed from model\")\n", + " plt.imshow(grid)\n", + " plt.axis('off')\n", + " plt.show()\n", + " \n", + "input_imgs = get_train_images(6)\n", + "visualize_reconstructions(model, input_imgs)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "Z5keSdPPnzhP" + }, + "source": [ + "# Masked AutoEncoder\n", + "The follow code are the demonstration of Masked Autoencoder implementation and visualization" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "qHp1VzhtoYql" + }, + "source": [ + "# Import Necessary Libraries" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "DKr2eCkDny9B", + "outputId": "b9997a91-52ba-4ca5-c38a-3ac64a224c78" + }, + "outputs": [], + "source": [ + "import sys\n", + "import os\n", + "import requests\n", + "\n", + "import torch\n", + "import numpy as np\n", + "\n", + "import matplotlib.pyplot as plt\n", + "from PIL import Image\n", + "\n", + "# check whether run in Colab\n", + "if 'google.colab' in sys.modules:\n", + " print('Running in Colab.')\n", + " !pip3 install timm==0.4.5 # 0.3.2 does not work in Colab\n", + " !git clone https://github.com/facebookresearch/mae.git\n", + " sys.path.append('./mae')\n", + "else:\n", + " sys.path.append('./mae')\n", + "import models_mae" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "vrdyiqpWod8J" + }, + "source": [ + "# Build up necessary utillities" + ] + }, + { + "cell_type": "code", + "execution_count": 131, + "metadata": { + "id": "_De1rOh8ny51" + }, + "outputs": [], + "source": [ + "# define the utils\n", + "\n", + "imagenet_mean = np.array([0.485, 0.456, 0.406])\n", + "imagenet_std = np.array([0.229, 0.224, 0.225])\n", + "\n", + "def show_image(image, title=''):\n", + " # image is [H, W, 3]\n", + " assert image.shape[2] == 3\n", + " plt.imshow(torch.clip((image * imagenet_std + imagenet_mean) * 255, 0, 255).int())\n", + " plt.title(title, fontsize=16)\n", + " plt.axis('off')\n", + " return\n", + "\n", + "def prepare_model(chkpt_dir, arch='mae_vit_large_patch16'):\n", + " # build model\n", + " model = getattr(models_mae, arch)()\n", + " # load model\n", + " checkpoint = torch.load(chkpt_dir, map_location='cpu')\n", + " msg = model.load_state_dict(checkpoint['model'], strict=False)\n", + " print(msg)\n", + " return model\n", + "\n", + "def run_one_image(img, model):\n", + " x = torch.tensor(img)\n", + "\n", + " # make it a batch-like\n", + " x = x.unsqueeze(dim=0)\n", + " x = torch.einsum('nhwc->nchw', x)\n", + "\n", + " # run MAE\n", + " loss, y, mask = model(x.float(), mask_ratio= 0.75)\n", + " y = model.unpatchify(y)\n", + " y = torch.einsum('nchw->nhwc', y).detach().cpu()\n", + "\n", + " # visualize the mask\n", + " mask = mask.detach()\n", + " mask = mask.unsqueeze(-1).repeat(1, 1, model.patch_embed.patch_size[0]**2 *3) # (N, H*W, p*p*3)\n", + " mask = model.unpatchify(mask) # 1 is removing, 0 is keeping\n", + " mask = torch.einsum('nchw->nhwc', mask).detach().cpu()\n", + "\n", + " x = torch.einsum('nchw->nhwc', x)\n", + "\n", + " # masked image\n", + " im_masked = x * (1 - mask)\n", + "\n", + " # MAE reconstruction pasted with visible patches\n", + " im_paste = x * (1 - mask) + y * mask\n", + "\n", + " # make the plt figure larger\n", + " plt.rcParams['figure.figsize'] = [24, 24]\n", + "\n", + " plt.subplot(1, 4, 1)\n", + " show_image(x[0], \"original\")\n", + "\n", + " plt.subplot(1, 4, 2)\n", + " show_image(im_masked[0], \"masked\")\n", + "\n", + " plt.subplot(1, 4, 3)\n", + " show_image(y[0], \"reconstruction\")\n", + "\n", + " plt.subplot(1, 4, 4)\n", + " show_image(im_paste[0], \"reconstruction + visible\")\n", + "\n", + " plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "8wt9sd2tolyv" + }, + "source": [ + "# Load one image" + ] + }, + { + "cell_type": "code", + "execution_count": 189, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 422 + }, + "id": "_EbEF8gQolnq", + "outputId": "df774563-3070-4585-9809-8a950e3c303e" + }, + "outputs": [ + { + "data": { + "application/pdf": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# load an image\n", + "img_url = 'https://user-images.githubusercontent.com/11435359/147738734-196fd92f-9260-48d5-ba7e-bf103d29364d.jpg' # fox, from ILSVRC2012_val_00046145\n", + "# img_url = 'https://user-images.githubusercontent.com/11435359/147743081-0428eecf-89e5-4e07-8da5-a30fd73cc0ba.jpg' # cucumber, from ILSVRC2012_val_00047851\n", + "img = Image.open(requests.get(img_url, stream=True).raw)\n", + "img = img.resize((224, 224))\n", + "img = np.array(img) / 255.\n", + "\n", + "assert img.shape == (224, 224, 3)\n", + "\n", + "# normalize by ImageNet mean and std\n", + "img = img - imagenet_mean\n", + "img = img / imagenet_std\n", + "\n", + "plt.rcParams['figure.figsize'] = [5, 5]\n", + "show_image(torch.tensor(img))" + ] + }, + { + "cell_type": "code", + "execution_count": 141, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "RaMd6bemoqLB", + "outputId": "a8612b2b-6695-4fdf-eeef-e1b709fccab0" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "File ‘mae_visualize_vit_large.pth’ already there; not retrieving.\n", + "\n", + "\n", + "Model loaded.\n" + ] + } + ], + "source": [ + "# Patch for numpy error\n", + "np.float = float\n", + "np.int = int #module 'numpy' has no attribute 'int'\n", + "np.object = object #module 'numpy' has no attribute 'object'\n", + "np.bool = bool #module 'numpy' has no attribute 'bool'\n", + "# This is an MAE model trained with pixels as targets for visualization (ViT-Large, training mask ratio=0.75)\n", + "\n", + "# download checkpoint if not exist\n", + "!wget -nc https://dl.fbaipublicfiles.com/mae/visualize/mae_visualize_vit_large.pth\n", + "\n", + "chkpt_dir = 'mae_visualize_vit_large.pth'\n", + "model_mae = prepare_model(chkpt_dir, 'mae_vit_large_patch16')\n", + "print('Model loaded.')" + ] + }, + { + "cell_type": "code", + "execution_count": 134, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "RaMd6bemoqLB", + "outputId": "a8612b2b-6695-4fdf-eeef-e1b709fccab0" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "File ‘mae_visualize_vit_huge.pth’ already there; not retrieving.\n", + "\n", + "\n", + "Model loaded.\n" + ] + } + ], + "source": [ + "# Patch for numpy error\n", + "np.float = float\n", + "np.int = int #module 'numpy' has no attribute 'int'\n", + "np.object = object #module 'numpy' has no attribute 'object'\n", + "np.bool = bool #module 'numpy' has no attribute 'bool'\n", + "# This is an MAE model trained with pixels as targets for visualization (ViT-Large, training mask ratio=0.75)\n", + "\n", + "# download checkpoint if not exist\n", + "!wget -nc https://dl.fbaipublicfiles.com/mae/visualize/mae_visualize_vit_huge.pth\n", + "\n", + "chkpt_dir = 'mae_visualize_vit_huge.pth'\n", + "model_mae = prepare_model(chkpt_dir, 'mae_vit_huge_patch14')\n", + "print('Model loaded.')" + ] + }, + { + "cell_type": "code", + "execution_count": 110, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 503 + }, + "id": "xymH8jt4orm6", + "outputId": "a60ce3ce-bfc2-48f4-e92f-451a55b1322a" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "MAE with pixel reconstruction:\n" + ] + }, + { + "ename": "RuntimeError", + "evalue": "einsum(): the number of subscripts in the equation (4) does not match the number of dimensions (3) for operand 0 and no ellipsis was given", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mRuntimeError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[110], line 4\u001b[0m\n\u001b[1;32m 2\u001b[0m torch\u001b[38;5;241m.\u001b[39mmanual_seed(\u001b[38;5;241m2\u001b[39m)\n\u001b[1;32m 3\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mMAE with pixel reconstruction:\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[0;32m----> 4\u001b[0m \u001b[43mrun_one_image\u001b[49m\u001b[43m(\u001b[49m\u001b[43mimg\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmodel_mae\u001b[49m\u001b[43m)\u001b[49m\n", + "Cell \u001b[0;32mIn[46], line 28\u001b[0m, in \u001b[0;36mrun_one_image\u001b[0;34m(img, model)\u001b[0m\n\u001b[1;32m 26\u001b[0m \u001b[38;5;66;03m# make it a batch-like\u001b[39;00m\n\u001b[1;32m 27\u001b[0m x \u001b[38;5;241m=\u001b[39m x\u001b[38;5;241m.\u001b[39munsqueeze(dim\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m0\u001b[39m)\n\u001b[0;32m---> 28\u001b[0m x \u001b[38;5;241m=\u001b[39m \u001b[43mtorch\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43meinsum\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mnhwc->nchw\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mx\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 30\u001b[0m \u001b[38;5;66;03m# run MAE\u001b[39;00m\n\u001b[1;32m 31\u001b[0m loss, y, mask \u001b[38;5;241m=\u001b[39m model(x\u001b[38;5;241m.\u001b[39mfloat(), mask_ratio\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m0.75\u001b[39m)\n", + "File \u001b[0;32m/usr/lib/python3.11/site-packages/torch/functional.py:380\u001b[0m, in \u001b[0;36meinsum\u001b[0;34m(*args)\u001b[0m\n\u001b[1;32m 375\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m einsum(equation, \u001b[38;5;241m*\u001b[39m_operands)\n\u001b[1;32m 377\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(operands) \u001b[38;5;241m<\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[38;5;241m2\u001b[39m \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m opt_einsum\u001b[38;5;241m.\u001b[39menabled:\n\u001b[1;32m 378\u001b[0m \u001b[38;5;66;03m# the path for contracting 0 or 1 time(s) is already optimized\u001b[39;00m\n\u001b[1;32m 379\u001b[0m \u001b[38;5;66;03m# or the user has disabled using opt_einsum\u001b[39;00m\n\u001b[0;32m--> 380\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43m_VF\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43meinsum\u001b[49m\u001b[43m(\u001b[49m\u001b[43mequation\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43moperands\u001b[49m\u001b[43m)\u001b[49m \u001b[38;5;66;03m# type: ignore[attr-defined]\u001b[39;00m\n\u001b[1;32m 382\u001b[0m path \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m 383\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m opt_einsum\u001b[38;5;241m.\u001b[39mis_available():\n", + "\u001b[0;31mRuntimeError\u001b[0m: einsum(): the number of subscripts in the equation (4) does not match the number of dimensions (3) for operand 0 and no ellipsis was given" + ] + } + ], + "source": [ + "# make random mask reproducible (comment out to make it change)\n", + "torch.manual_seed(2)\n", + "print('MAE with pixel reconstruction:')\n", + "run_one_image(img, model_mae)" + ] + }, + { + "cell_type": "code", + "execution_count": 347, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(2400, 2400)\n" + ] + }, + { + "data": { + "text/plain": [ + "118" + ] + }, + "execution_count": 347, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "application/pdf": 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+ "image/svg+xml": [ + "\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " 2024-02-20T23:20:24.414200\n", + " image/svg+xml\n", + " \n", + " \n", + " Matplotlib v3.8.2, https://matplotlib.org/\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "\n" + ], + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "img_url = 'https://user-images.githubusercontent.com/11435359/147738734-196fd92f-9260-48d5-ba7e-bf103d29364d.jpg' # fox, from ILSVRC2012_val_00046145\n", + "# img_url = 'https://user-images.githubusercontent.com/11435359/147743081-0428eecf-89e5-4e07-8da5-a30fd73cc0ba.jpg' # cucumber, from ILSVRC2012_val_00047851\n", + "mine_img = Image.open('./st2/6644818.png', formats=('PNG',)).convert('RGB')# Image.open(requests.get(img_url, stream=True).raw)\n", + "\n", + "print(mine_img.size)\n", + "\n", + "# mine_img.show()\n", + "mine_img = mine_img.resize((224, 224))\n", + "\n", + "mine_img = np.array(mine_img) / 255.\n", + "\n", + "# print(mine_img.shape, mine_img[0][0])\n", + "\n", + "assert mine_img.shape == (224, 224, 3)\n", + "\n", + "# target = np.array([118, 111, 95])\n", + "target = np.array([123, 116, 103])\n", + "\n", + "pre_ids_to_restore = []\n", + "\n", + "for y in range(14):\n", + " for x in range(14):\n", + " if (np.array(mine_img[y * 16 + 8][x * 16 + 8]) * 255 == target).all():\n", + " pre_ids_to_restore.append(x + y * 14)\n", + " #if y == 0: \n", + " # print(np.array([[mine_img[y * 16 + 8][x * 16 + 8]]]) * 255)\n", + " # plt.imshow(np.array([[mine_img[y * 16 + 8][x * 16 + 8]]]))\n", + "\n", + "# normalize by ImageNet mean and std\n", + "mine_img = mine_img - imagenet_mean\n", + "mine_img = mine_img / imagenet_std\n", + "\n", + "plt.rcParams['figure.figsize'] = [5, 5]\n", + "show_image(torch.tensor(mine_img))\n", + "\n", + "len(pre_ids_to_restore)" + ] + }, + { + "cell_type": "code", + "execution_count": 350, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "78 196 [19, 91, 86, 80, 149, 94, 96, 60, 78, 59, 48, 29, 122, 52, 11, 132, 72, 143, 21, 99, 172, 53, 92, 161, 134, 89, 77, 195, 35, 67, 63, 44, 123, 101, 128, 162, 84, 76, 10, 137, 152, 26, 27, 0, 46, 49, 190, 194, 120, 184, 133, 165, 126, 112, 65, 115, 90, 20, 159, 192, 154, 51, 32, 98, 151, 125, 93, 81, 107, 1, 116, 124, 182, 127, 23, 41, 121, 17]\n" + ] + }, + { + "data": { + "text/plain": [ + "(torch.Size([1, 196]), torch.Size([1, 78]))" + ] + }, + "execution_count": 350, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import pandas as pd\n", + "\n", + "d = pd.read_csv('st2/6644818/shuffle_info.csv', header=None)\n", + "\n", + "ids_keep = eval(d.loc[0][1])\n", + "ids_restore = eval(d.loc[1][1])\n", + "print(len(ids_keep[0]), len(ids_restore[0]), [ x for x in ids_restore[0] if x in ids_keep[0]])\n", + "\n", + "\n", + "ids_restore = torch.Tensor(ids_restore).type(torch.int64) # torch.Tensor([ids_keep[0] + ids_restore[0]]).type(torch.int64)\n", + "ids_keep = torch.Tensor(ids_keep).type(torch.int64)\n", + "\n", + "ids_restore.shape, ids_keep.shape\n", + "\n", + "# ids_keep = [ x for x in range(14 * 14) if x not in pre_ids_to_restore ]\n", + "\n", + "# ids_restore = torch.Tensor([ids_keep + pre_ids_to_restore]).type(torch.int64)\n", + "\n", + "# ids_keep = torch.Tensor([ids_keep]).type(torch.int64)\n", + "\n", + "# show_image(torch.tensor(mine_img))\n", + "\n", + "# ids_restore, ids_restore.shape, ids_keep" + ] + }, + { + "cell_type": "code", + "execution_count": 352, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "MAE with pixel reconstruction:\n", + "1024\n", + "tensor([[125, 182, 133, 91, 99, 151, 107, 27, 93, 44, 115, 35, 10, 159,\n", + " 1, 86, 92, 195, 116, 0, 154, 49, 84, 190, 123, 134, 121, 124,\n", + " 65, 26, 76, 19, 162, 194, 59, 90, 23, 63, 51, 29, 41, 192,\n", + " 132, 165, 101, 80, 127, 126, 21, 128, 137, 161, 32, 60, 78, 77,\n", + " 89, 67, 11, 20, 17, 52, 152, 96, 184, 149, 72, 94, 143, 172,\n", + " 122, 53, 46, 98, 48, 120, 112, 81]])\n", + "tensor([[[ 1.0417, 0.7687, 0.1073, ..., 0.5065, 0.5881, 1.1361],\n", + " [ 0.6540, 0.6927, 0.5497, ..., 0.6509, 0.6065, 1.6248],\n", + " [ 1.2860, 1.1067, 0.9193, ..., 0.4753, 0.5549, 1.2062],\n", + " ...,\n", + " [ 1.5381, 1.5673, 1.4636, ..., 0.4356, 0.4163, 1.4357],\n", + " [ 0.7640, 0.8398, 0.6108, ..., 0.6543, 0.6467, 1.4214],\n", + " [-0.3706, -0.1278, -0.1562, ..., 0.5149, 0.7952, 1.8260]]],\n", + " grad_fn=)\n", + "torch.Size([1, 50, 1024]) torch.Size([1, 79, 1024])\n", + "torch.Size([1, 196]) torch.Size([1, 196])\n", + "tensor([[160, 26, 174, 158, 178, 25, 36, 141, 4, 195, 125, 113, 14, 132,\n", + " 137, 116, 191, 129, 11, 179, 133, 54, 6, 150, 190, 20, 105, 134,\n", + " 56, 81, 37, 55, 9, 101, 153, 143, 188, 12, 90, 194, 117, 74,\n", + " 10, 140, 168, 171, 176, 124, 164, 77, 173, 96, 82, 52, 146, 135,\n", + " 157, 40, 49, 189, 46, 43, 76, 70, 34, 172, 111, 138, 166, 39,\n", + " 169, 51, 84, 167, 185, 182, 152, 186, 66, 99, 67, 98, 91, 147,\n", + " 161, 29, 47, 72, 114, 61, 41, 83, 53, 104, 27, 78, 64, 89,\n", + " 170, 159, 71, 7, 30, 24, 94, 57, 31, 126, 106, 86, 65, 181,\n", + " 3, 100, 139, 163, 13, 85, 175, 118, 131, 58, 162, 62, 48, 184,\n", + " 128, 102, 32, 80, 23, 35, 87, 123, 19, 193, 165, 5, 155, 42,\n", + " 21, 187, 60, 69, 112, 149, 107, 75, 108, 50, 28, 127, 110, 145,\n", + " 63, 1, 79, 2, 97, 144, 109, 151, 121, 136, 183, 115, 119, 59,\n", + " 154, 177, 33, 192, 130, 88, 8, 0, 22, 16, 38, 73, 18, 17,\n", + " 93, 15, 95, 120, 122, 156, 148, 44, 68, 103, 180, 142, 45, 92]])\n" + ] + }, + { + "data": { + "application/pdf": 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+ "\n", + " x = my_masking(model, x)\n", + "\n", + " # append cls token\n", + " cls_token = model.cls_token + model.pos_embed[:, :1, :]\n", + " cls_tokens = cls_token.expand(x.shape[0], -1, -1)\n", + " x = torch.cat((cls_tokens, x), dim=1)\n", + "\n", + " # apply Transformer blocks\n", + " for blk in model.blocks:\n", + " x = blk(x)\n", + " x = model.norm(x)\n", + "\n", + " return x\n", + "\n", + "def restore_one_image(img, model):\n", + " x = torch.tensor(img)\n", + "\n", + " # make it a batch-like\n", + " x = x.unsqueeze(dim=0)\n", + " x = torch.einsum('nhwc->nchw', x)\n", + "\n", + " # run MAE\n", + " # loss, ty, mask = model(x.float(), mask_ratio=0)\n", + "\n", + " tx = forward_encoder(model, x.float())\n", + "\n", + " l, m, i = model.forward_encoder(x.float(), 0.75);\n", + "\n", + " print(l.shape, tx.shape)\n", + " print(i.shape, ids_restore.shape)\n", + "\n", + " print(i)\n", + "\n", + " ty = model.forward_decoder(tx, ids_restore)\n", + " \n", + " y = model.unpatchify(ty)\n", + " y = torch.einsum('nchw->nhwc', y).detach().cpu()\n", + "\n", + " x = torch.einsum('nchw->nhwc', x)\n", + " \n", + " #mask = model.unpatchify(x.float()) # 1 is removing, 0 is keeping\n", + " #mask = torch.einsum('nchw->nhwc', mask).detach().cpu()\n", + "\n", + " # make the plt figure larger\n", + " plt.rcParams['figure.figsize'] = [12, 12]\n", + "\n", + " plt.subplot(1, 2, 1)\n", + " show_image(x[0], \"original\")\n", + "\n", + " plt.subplot(1, 2, 2)\n", + " show_image(y[0], \"reconstruction\")\n", + " \n", + " plt.show()\n", + "\n", + "torch.manual_seed(5)\n", + "print('MAE with pixel reconstruction:')\n", + "restore_one_image(mine_img, model_mae)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "accelerator": "GPU", + "colab": { + "gpuType": "T4", + "provenance": [], + "toc_visible": true + }, + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": 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for its ability to compress and reconstruct data. Autoencoders work by first encoding input data, such as images, into a compact feature vector through an encoder network. This process effectively distills the essence of the data into a smaller, more manageable form. The feature vector, often referred to as the "bottleneck," plays a crucial role in this compression process, allowing us to represent the input data with fewer features. +# +# Following compression, a second neural network, known as the decoder, takes over to reconstruct the original data from the compressed feature vector. This remarkable ability to compress and then reconstruct data makes autoencoders extremely valuable in various applications, including data compression and image comparison at a more meaningful level than mere pixel-by-pixel analysis. +# +# Moreover, our exploration will not stop at the autoencoder framework itself. We will also introduce the concept of "deconvolution" (also known as transposed convolution), a powerful operator used to enlarge feature maps in both height and width dimensions. Deconvolution networks are indispensable in scenarios where we begin with a compact feature vector and aim to generate a full-sized image. This technique is pivotal in various advanced neural network applications, such as Variational Autoencoders (VAEs), Generative Adversarial Networks (GANs), and super-resolution. +# +# To kick things off, we'll start by importing our standard libraries, setting the stage for our deep dive into the inner workings and applications of autoencoders. + +# In[1]: + + +## Standard libraries +import os +import json +import math +import numpy as np + +## Imports for plotting +import matplotlib.pyplot as plt +get_ipython().run_line_magic('matplotlib', 'inline') +from IPython.display import set_matplotlib_formats +set_matplotlib_formats('svg', 'pdf') # For export +from matplotlib.colors import to_rgb +import matplotlib +matplotlib.rcParams['lines.linewidth'] = 2.0 +## Progress bar +from tqdm.notebook import tqdm + +## PyTorch +import torch +import torch.nn as nn +import torch.nn.functional as F +import torch.utils.data as data +import torch.optim as optim +# Torchvision +import torchvision +from torchvision.datasets import CIFAR10 +from torchvision import transforms + +DATASET_PATH = "dataset" + +device = torch.device("cuda:0") if torch.cuda.is_available() else torch.device("cpu") +print("Device:", device) + + +# # Download and setup the dataset +# In this labsheet, our focus shifts to the CIFAR10 dataset, a collection known for its rich, colored images. Each image within CIFAR10 is equipped with 3 color channels and boasts a resolution of 32x32 pixels. This characteristic is particularly advantageous when working with autoencoders, as they are not bound by the constraints of probabilistic image modeling. +# +# Should you already have the CIFAR10 dataset downloaded in a different directory, it's important to adjust the DATASET_PATH variable accordingly. This step ensures you avoid unnecessary additional downloads, streamlining your workflow and allowing you to dive into the practical exercises more swiftly. + +# In[105]: + + +# Transformations applied on each image => only make them a tensor +transform = transforms.Compose([transforms.ToTensor(), + transforms.Normalize((0.5,),(0.5,))]) + +# Loading the training dataset. We need to split it into a training and validation part +train_dataset = CIFAR10(root=DATASET_PATH, train=True, transform=transform, download=True) +train_set, val_set = torch.utils.data.random_split(train_dataset, [45000, 5000]) + +# Loading the test set +test_set = CIFAR10(root=DATASET_PATH, train=False, transform=transform, download=True) + +# We define a set of data loaders that we can use for various purposes later. +train_loader = data.DataLoader(train_set, batch_size=256, shuffle=True, drop_last=True, pin_memory=True, num_workers=4) +val_loader = data.DataLoader(val_set, batch_size=256, shuffle=False, drop_last=False, num_workers=4) +test_loader = data.DataLoader(test_set, batch_size=256, shuffle=False, drop_last=False, num_workers=4) + +def get_train_images(num): + return torch.stack([train_dataset[i][0] for i in range(num)], dim=0) + + +# # Building the autoencoder +# +# In general, an autoencoder consists of an **encoder** that maps the input $x$ to a lower-dimensional feature vector $z$, and a **decoder** that reconstructs the input $\hat{x}$ from $z$. We train the model by comparing $x$ to $\hat{x}$ and optimizing the parameters to increase the similarity between $x$ and $\hat{x}$. See below for a small illustration of the autoencoder framework. +# +# +# ![img](https://raw.githubusercontent.com/hqsiswiliam/COM3025_Torch/main/autoencoder.png) + +# +# For an educational purpose revision in markdown format, the text could be enhanced as follows: +# +# To kick off our exploration, we initiate with the construction of the encoder. This component is fundamentally a deep convolutional network tailored for progressively diminishing the image's dimensions. This diminution is achieved through the use of strided convolutions, which methodically reduce the image's size layer by layer. Following the thrice-executed downscaling process, we transition the architecture from convolutional layers to a flattened feature representation. This is achieved by flattening the spatial features into a single vector, which is then processed through several linear layers. As a result, we obtain the latent representation, denoted as +# $z$, encapsulating the compressed essence of the input image. The size of this latent vector, $d$, is adjustable, providing flexibility in the encoding capacity of our network. + +# In[59]: + + +class Encoder(nn.Module): + + def __init__(self, + num_input_channels : int, + base_channel_size : int, + latent_dim : int, + act_fn : object = nn.GELU): + """ + Inputs: + - num_input_channels : Number of input channels of the image. For CIFAR, this parameter is 3 + - base_channel_size : Number of channels we use in the first convolutional layers. Deeper layers might use a duplicate of it. + - latent_dim : Dimensionality of latent representation z + - act_fn : Activation function used throughout the encoder network + """ + super().__init__() + c_hid = base_channel_size + self.net = nn.Sequential( + nn.Conv2d(num_input_channels, c_hid, kernel_size=3, padding=1, stride=2), # 32x32 => 16x16 + act_fn(), + nn.Conv2d(c_hid, c_hid, kernel_size=3, padding=1), + act_fn(), + nn.Conv2d(c_hid, 2*c_hid, kernel_size=3, padding=1, stride=2), # 16x16 => 8x8 + act_fn(), + nn.Conv2d(2*c_hid, 2*c_hid, kernel_size=3, padding=1), + act_fn(), + nn.Conv2d(2*c_hid, 2*c_hid, kernel_size=3, padding=1, stride=2), # 8x8 => 4x4 + act_fn(), + nn.Flatten(), # Image grid to single feature vector + nn.Linear(2*16*c_hid, latent_dim) + ) + + # self.flatten = nn.Sequential( + # nn.Flatten(), # Image grid to single feature vector + # nn.Linear(2*16*c_hid, latent_dim) + # ) + + def forward(self, x): + # x = self.net(x) + + # print(x.shape) + + # return self.flatten(x) + + return self.net(x) + + +# # Task1 +# Now Complete the decoder implementation + +# In[133]: + + +class Decoder(nn.Module): + + def __init__(self, + num_input_channels : int, + base_channel_size : int, + latent_dim : int, + act_fn : object = nn.GELU): + """ + Inputs: + - num_input_channels : Number of channels of the image to reconstruct. For CIFAR, this parameter is 3 + - base_channel_size : Number of channels we use in the last convolutional layers. Early layers might use a duplicate of it. + - latent_dim : Dimensionality of latent representation z + - act_fn : Activation function used throughout the decoder network + """ + super().__init__() + c_hid = base_channel_size + self.net = nn.Sequential( + nn.Linear(latent_dim, 2*16*c_hid), + act_fn(), + nn.Unflatten(1, (2*c_hid, 4, 4)), + nn.ConvTranspose2d(2*c_hid, 2*c_hid, kernel_size=3, padding=1, stride=2, output_padding=1), # 8x8 <= 4x4 + act_fn(), + nn.Conv2d(2*c_hid, 2*c_hid, kernel_size=3, padding=1), + act_fn(), + nn.ConvTranspose2d(2*c_hid, c_hid, kernel_size=3, padding=1, stride=2, output_padding=1), # 16x16 <= 8x8 + act_fn(), + nn.Conv2d(c_hid, c_hid, kernel_size=3, padding=1), + act_fn(), + nn.ConvTranspose2d(c_hid, num_input_channels, kernel_size=3, padding=1, stride=2, output_padding=1), # 32x32 <= 16x16 + nn.Tanh(), + # nn.Sigmoid(), + ) + # You code goes here. + + def forward(self, x): + return self.net(x) + # You code goes here. + + +# # Combining Encoder and Decoder +# ## Loss Function: Mean Squared Error (MSE) +# +# For our loss function, we opt for the Mean Squared Error (MSE). MSE is particularly effective in emphasizing the significance of accurately predicting pixel values that are substantially misestimated by the network. For instance, a minor deviation, such as predicting 127 instead of 128, is deemed less critical. However, larger discrepancies, like confusing a pixel value of 0 with 128, are considered more severe and detrimental to the reconstruction quality. +# +# Unlike Variational Autoencoders (VAEs) that predict the probability for each pixel value, we employ MSE as a straightforward distance measure. This approach significantly reduces the number of parameters, streamlining the training process. To enhance our understanding of the per-pixel performance, we calculate the summed squared error, averaged across the batch dimension. It's important to note that alternative aggregations (mean or sum) yield equivalent outcomes in terms of resulting parameters. +# +# ### Limitations of MSE +# +# Despite its advantages, MSE is not without drawbacks. Primarily, it tends to produce blurrier images, as it inherently removes small noise and high-frequency patterns, which contribute minimally to the overall error. To mitigate this and achieve more realistic reconstructions, integrating Generative Adversarial Networks (GANs) with autoencoders has proven effective. This hybrid approach is explored in various studies ([example 1](https://arxiv.org/abs/1704.02304), [example 2](https://arxiv.org/abs/1511.05644), and [slides](http://elarosca.net/slides/iccv_autoencoder_gans.pdf)). +# +# Furthermore, MSE may not always accurately reflect visual similarity between images. A case in point is when an autoencoder produces an image that is slightly shifted—despite the near-identical appearance, the MSE can significantly increase, showcasing a limitation in capturing true visual fidelity. A potential solution involves leveraging a pre-trained CNN to measure distance based on visual features extracted from lower layers, offering a more nuanced comparison than pixel-level MSE. +# + +# In[134]: + + +class Autoencoder(nn.Module): + + def __init__(self, + base_channel_size: int, + latent_dim: int, + encoder_class : object = Encoder, + decoder_class : object = Decoder, + num_input_channels: int = 3, + width: int = 32, + height: int = 32): + super().__init__() + # Creating encoder and decoder + self.encoder = encoder_class(num_input_channels, base_channel_size, latent_dim) + self.decoder = decoder_class(num_input_channels, base_channel_size, latent_dim) + # Example input array needed for visualizing the graph of the network + self.example_input_array = torch.zeros(2, num_input_channels, width, height) + + def forward(self, x): + z = self.encoder(x) + x_hat = self.decoder(z) + return x_hat + + def _get_reconstruction_loss(self, batch): + x = batch # We do not need the labels + x_hat = self.forward(x) + loss = F.mse_loss(x, x_hat, reduction="none") + loss = loss.sum(dim=[1,2,3]).mean(dim=[0]) + return loss + + +# # Utility code for comparing Images + +# In[14]: + + +def compare_imgs(img1, img2, title_prefix=""): + # Calculate MSE loss between both images + loss = F.mse_loss(img1, img2, reduction="sum") + # Plot images for visual comparison + grid = torchvision.utils.make_grid(torch.stack([img1, img2], dim=0), nrow=2, normalize=True) + grid = grid.permute(1, 2, 0) + plt.figure(figsize=(4,2)) + plt.title(f"{title_prefix} Loss: {loss.item():4.2f}") + plt.imshow(grid) + plt.axis('off') + plt.show() + +for i in range(2): + # Load example image + img, _ = train_dataset[i] + img_mean = img.mean(dim=[1,2], keepdims=True) + + # Shift image by one pixel + SHIFT = 1 + img_shifted = torch.roll(img, shifts=SHIFT, dims=1) + img_shifted = torch.roll(img_shifted, shifts=SHIFT, dims=2) + img_shifted[:,:1,:] = img_mean + img_shifted[:,:,:1] = img_mean + compare_imgs(img, img_shifted, "Shifted -") + + # Set half of the image to zero + img_masked = img.clone() + img_masked[:,:img_masked.shape[1]//2,:] = img_mean + compare_imgs(img, img_masked, "Masked -") + + +# # Task2 +# Add training code to train the AutoEncoder + +# In[2]: + + +# for batch in tqdm(train_loader, total=len(train_loader)): +import torch.optim as optim + +model = Autoencoder(64, 128, ) # you code here +model.to(device) +optimizer = torch.optim.Adam(model.parameters(), lr=1e-3) # your code here +scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(optimizer, 'min') # your code here, can use ReduceLROnPlateau +# Write training loop here + +loss_fn = nn.MSELoss() + +n_epoch = 40 +model.train() +for epoch in range(n_epoch): + print(f"\nEpoch {epoch}:") + + avg_loss = 0 + + for i, data in enumerate(train_loader): + inputs, _ = data + + inputs = inputs.cuda() + + loss = model._get_reconstruction_loss(inputs) #loss_fn(outputs, inputs) + + optimizer.zero_grad() + + loss.backward() + + optimizer.step() + + avg_loss += loss + + print(f'\rBatch: {i}: Loss:{loss} avg_Loss: {avg_loss/(i + 1)} ', end='') + + scheduler.step(loss) + + +# In[144]: + + +def visualize_reconstructions(model, input_imgs): + # Reconstruct images + model.eval() + with torch.no_grad(): + reconst_imgs = model(input_imgs.to(device)) + reconst_imgs = reconst_imgs.cpu() + + # Plotting + imgs = torch.stack([input_imgs, reconst_imgs], dim=1).flatten(0,1) + grid = torchvision.utils.make_grid(imgs, nrow=4, normalize=True) + grid = grid.permute(1, 2, 0) + plt.figure(figsize=(7,4.5)) + plt.title(f"Reconstructed from model") + plt.imshow(grid) + plt.axis('off') + plt.show() + +input_imgs = get_train_images(6) +visualize_reconstructions(model, input_imgs) + + +# # Masked AutoEncoder +# The follow code are the demonstration of Masked Autoencoder implementation and visualization + +# # Import Necessary Libraries + +# In[4]: + + +import sys +import os +import requests + +import torch +import numpy as np + +import matplotlib.pyplot as plt +from PIL import Image + +# check whether run in Colab +if 'google.colab' in sys.modules: + print('Running in Colab.') + get_ipython().system('pip3 install timm==0.4.5 # 0.3.2 does not work in Colab') + get_ipython().system('git clone https://github.com/facebookresearch/mae.git') + sys.path.append('./mae') +else: + sys.path.append('./mae') +import models_mae + + +# # Build up necessary utillities + +# In[131]: + + +# define the utils + +imagenet_mean = np.array([0.485, 0.456, 0.406]) +imagenet_std = np.array([0.229, 0.224, 0.225]) + +def show_image(image, title=''): + # image is [H, W, 3] + assert image.shape[2] == 3 + plt.imshow(torch.clip((image * imagenet_std + imagenet_mean) * 255, 0, 255).int()) + plt.title(title, fontsize=16) + plt.axis('off') + return + +def prepare_model(chkpt_dir, arch='mae_vit_large_patch16'): + # build model + model = getattr(models_mae, arch)() + # load model + checkpoint = torch.load(chkpt_dir, map_location='cpu') + msg = model.load_state_dict(checkpoint['model'], strict=False) + print(msg) + return model + +def run_one_image(img, model): + x = torch.tensor(img) + + # make it a batch-like + x = x.unsqueeze(dim=0) + x = torch.einsum('nhwc->nchw', x) + + # run MAE + loss, y, mask = model(x.float(), mask_ratio= 0.75) + y = model.unpatchify(y) + y = torch.einsum('nchw->nhwc', y).detach().cpu() + + # visualize the mask + mask = mask.detach() + mask = mask.unsqueeze(-1).repeat(1, 1, model.patch_embed.patch_size[0]**2 *3) # (N, H*W, p*p*3) + mask = model.unpatchify(mask) # 1 is removing, 0 is keeping + mask = torch.einsum('nchw->nhwc', mask).detach().cpu() + + x = torch.einsum('nchw->nhwc', x) + + # masked image + im_masked = x * (1 - mask) + + # MAE reconstruction pasted with visible patches + im_paste = x * (1 - mask) + y * mask + + # make the plt figure larger + plt.rcParams['figure.figsize'] = [24, 24] + + plt.subplot(1, 4, 1) + show_image(x[0], "original") + + plt.subplot(1, 4, 2) + show_image(im_masked[0], "masked") + + plt.subplot(1, 4, 3) + show_image(y[0], "reconstruction") + + plt.subplot(1, 4, 4) + show_image(im_paste[0], "reconstruction + visible") + + plt.show() + + +# # Load one image + +# In[189]: + + +# load an image +img_url = 'https://user-images.githubusercontent.com/11435359/147738734-196fd92f-9260-48d5-ba7e-bf103d29364d.jpg' # fox, from ILSVRC2012_val_00046145 +# img_url = 'https://user-images.githubusercontent.com/11435359/147743081-0428eecf-89e5-4e07-8da5-a30fd73cc0ba.jpg' # cucumber, from ILSVRC2012_val_00047851 +img = Image.open(requests.get(img_url, stream=True).raw) +img = img.resize((224, 224)) +img = np.array(img) / 255. + +assert img.shape == (224, 224, 3) + +# normalize by ImageNet mean and std +img = img - imagenet_mean +img = img / imagenet_std + +plt.rcParams['figure.figsize'] = [5, 5] +show_image(torch.tensor(img)) + + +# In[141]: + + +# Patch for numpy error +np.float = float +np.int = int #module 'numpy' has no attribute 'int' +np.object = object #module 'numpy' has no attribute 'object' +np.bool = bool #module 'numpy' has no attribute 'bool' +# This is an MAE model trained with pixels as targets for visualization (ViT-Large, training mask ratio=0.75) + +# download checkpoint if not exist +get_ipython().system('wget -nc https://dl.fbaipublicfiles.com/mae/visualize/mae_visualize_vit_large.pth') + +chkpt_dir = 'mae_visualize_vit_large.pth' +model_mae = prepare_model(chkpt_dir, 'mae_vit_large_patch16') +print('Model loaded.') + + +mine_img = Image.open('./st2/6644818.png', formats=('PNG',)).convert('RGB')# Image.open(requests.get(img_url, stream=True).raw) + +# mine_img.show() +mine_img = mine_img.resize((224, 224)) + +mine_img = np.array(mine_img) / 255. + +# print(mine_img.shape, mine_img[0][0]) + +assert mine_img.shape == (224, 224, 3) + +# normalize by ImageNet mean and std +mine_img = mine_img - imagenet_mean +mine_img = mine_img / imagenet_std + +plt.rcParams['figure.figsize'] = [5, 5] +show_image(torch.tensor(mine_img)) + + +import pandas as pd + +d = pd.read_csv('st2/6644818/shuffle_info.csv', header=None) + +ids_keep = torch.Tensor(eval(d.loc[0][1])).type(torch.int64) +ids_restore = torch.Tensor(eval(d.loc[1][1])).type(torch.int64) + +def masking(self, x): + N, L, D = x.shape # batch, length, dim + return torch.gather(x, dim=1, index=ids_keep.unsqueeze(-1).repeat(1, 1, D)) # Creates the masked images + +def forward_encoder(self, x): + # embed patches + x = self.patch_embed(x) + + # add pos embed w/o cls token + x = x + self.pos_embed[:, 1:, :] + + x = masking(self, x) + + # append cls token + cls_token = self.cls_token + model.pos_embed[:, :1, :] + cls_tokens = cls_token.expand(x.shape[0], -1, -1) + x = torch.cat((cls_tokens, x), dim=1) + + # apply Transformer blocks + for blk in self.blocks: + x = blk(x) + x = self.norm(x) + + return x + +def restore_one_image(img, model): + x = torch.tensor(img) + + # make it a batch-like + x = x.unsqueeze(dim=0) + x = torch.einsum('nhwc->nchw', x) + + temp_x = forward_encoder(model, x.float()) + + y = model.forward_decoder(temp_x, ids_restore) + y = model.unpatchify(y) + y = torch.einsum('nchw->nhwc', y).detach().cpu() + + x = torch.einsum('nchw->nhwc', x) + + # make the plt figure larger + plt.rcParams['figure.figsize'] = [12, 12] + + plt.subplot(1, 2, 1) + show_image(x[0], "original") + + plt.subplot(1, 2, 2) + show_image(y[0], "reconstruction") + + plt.show() + +torch.manual_seed(5) +print('MAE with pixel reconstruction:') +restore_one_image(mine_img, model_mae) + + +# In[ ]: + + + +