embedding.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "import torch.nn as nn\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from torch.utils.data import Dataset, DataLoader\n",
    "from nscl.models.embedding.attribute_embedding_space import *\n",
    "from nscl.datasets.clevr_definition import *\n",
    "from torch import optim"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# dimensions\n",
    "input_dim = 64\n",
    "output_dim = 8\n",
    "all_shapes = CLEVRDefinition.attribute_concept_map['shape']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
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     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "# Generate synthetic features\n",
    "shape_features = {shape:torch.randn((1,input_dim)) for shape in all_shapes}\n",
    "for shape, features in shape_features.items():\n",
    "    plt.figure()\n",
    "    plt.axis(\"off\")\n",
    "    plt.title(f'{shape}_object_feature')\n",
    "    plt.imshow(features, cmap='gray')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "tags": [
     "outputPrepend",
     "outputPrepend"
    ]
   },
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
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     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "# Generate synthetic dataset\n",
    "class EmbeddingDataset(Dataset):\n",
    "    def __init__(self, features, labels):\n",
    "        self.features = features\n",
    "        self.labels = labels\n",
    "\n",
    "    def __getitem__(self, index):\n",
    "        return self.features[index], self.labels[index]\n",
    "\n",
    "    def __len__(self):\n",
    "        return len(self.features)\n",
    "\n",
    "num_data_per_shape = 100\n",
    "shape_to_id = {'cube':0, 'sphere':1, 'cylinder':2}\n",
    "id_to_shape = {0:'cube', 1:'sphere', 2:'cylinder'}\n",
    "\n",
    "scenes = []\n",
    "scene_labels = []\n",
    "\n",
    "for i in range(num_data_per_shape):\n",
    "    for shape in all_shapes:\n",
    "        scenes.append(shape_features[shape])\n",
    "        scene_labels.append(shape_to_id[shape])\n",
    "\n",
    "dataset = EmbeddingDataset(scenes, scene_labels)\n",
    "dataloader = DataLoader(dataset, batch_size=10, shuffle=True, drop_last=True)\n",
    "for features, shape_ids in dataloader:\n",
    "    for i in range(len(features)):\n",
    "        plt.figure()\n",
    "        plt.axis(\"off\")\n",
    "        plt.title(f'obj_{i}({id_to_shape[shape_ids[i].item()]})')\n",
    "        plt.imshow(features[i], cmap='gray')\n",
    "    break\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
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     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "# Generate embedding space\n",
    "attribute_concept_map = {'shape':['cube', 'sphere', 'cylinder']}\n",
    "embedding_space = AttributeEmbeddingSpace(attribute_concept_map, input_dim, output_dim)\n",
    "\n",
    "# initial embeddings\n",
    "for shape in all_shapes:\n",
    "    plt.figure()\n",
    "    plt.axis(\"off\")\n",
    "    plt.title(f'Initial {shape}_embedding')\n",
    "    plt.imshow(getattr(embedding_space.concept_embeddings, shape).concept_vector.data.unsqueeze(1).t(), cmap='gray')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "tensor([0.7233, 0.0328, 0.9855], grad_fn=<SigmoidBackward>)\n"
    }
   ],
   "source": [
    "# Test similarity computation\n",
    "scene = torch.cat([scenes[0], scenes[1], scenes[2]])\n",
    "print(embedding_space.similarity(scene, 'cube'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "tags": [
     "outputPrepend",
     "outputPrepend",
     "outputPrepend"
    ]
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": 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    },
    {
     "output_type": "error",
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-15-efe268f876a1>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     12\u001b[0m             \u001b[0mloss\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mloss_function\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mconfidence\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtensor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1.\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     13\u001b[0m             \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mloss\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgrad\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 14\u001b[0;31m             \u001b[0mloss\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbackward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     15\u001b[0m             \u001b[0moptimiser\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstep\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.local/lib/python3.7/site-packages/torch/tensor.py\u001b[0m in \u001b[0;36mbackward\u001b[0;34m(self, gradient, retain_graph, create_graph)\u001b[0m\n\u001b[1;32m    193\u001b[0m                 \u001b[0mproducts\u001b[0m\u001b[0;34m.\u001b[0m \u001b[0mDefaults\u001b[0m \u001b[0mto\u001b[0m\u001b[0;31m \u001b[0m\u001b[0;31m`\u001b[0m\u001b[0;31m`\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;31m`\u001b[0m\u001b[0;31m`\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    194\u001b[0m         \"\"\"\n\u001b[0;32m--> 195\u001b[0;31m         \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mautograd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbackward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgradient\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mretain_graph\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcreate_graph\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    196\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    197\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mregister_hook\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhook\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.local/lib/python3.7/site-packages/torch/autograd/__init__.py\u001b[0m in \u001b[0;36mbackward\u001b[0;34m(tensors, grad_tensors, retain_graph, create_graph, grad_variables)\u001b[0m\n\u001b[1;32m     97\u001b[0m     Variable._execution_engine.run_backward(\n\u001b[1;32m     98\u001b[0m         \u001b[0mtensors\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgrad_tensors\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mretain_graph\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcreate_graph\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 99\u001b[0;31m         allow_unreachable=True)  # allow_unreachable flag\n\u001b[0m\u001b[1;32m    100\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    101\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "# Train embedding space\n",
    "num_epoch = 10\n",
    "dataloader = DataLoader(dataset, batch_size=16, shuffle=True, drop_last=True)\n",
    "loss_function = nn.MSELoss()\n",
    "optimiser = optim.Adam(embedding_space.parameters(), lr=0.01)\n",
    "\n",
    "for i in range(num_epoch):\n",
    "    for features, shape_ids in dataloader:\n",
    "        for feature, shape_id in zip(features, shape_ids):\n",
    "            shape = id_to_shape[shape_id.item()]\n",
    "            confidence = embedding_space.similarity(feature, shape)\n",
    "            loss = loss_function(confidence, torch.tensor([1.]))\n",
    "            loss.backward()\n",
    "            optimiser.step()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
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     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "# Learned concept_embeddings\n",
    "for shape in all_shapes:\n",
    "    plt.figure()\n",
    "    plt.axis(\"off\")\n",
    "    plt.title(f'learned {shape}_embedding')\n",
    "    plt.imshow(getattr(embedding_space.concept_embeddings, shape).concept_vector.data.unsqueeze(1).t(), cmap='gray')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
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   ],
   "source": [
    "# Check accuracy\n",
    "for i in np.random.randint(0, len(scenes), 20):\n",
    "    scene = scenes[i]\n",
    "    similarties = []\n",
    "    for shape in all_shapes:\n",
    "        similarties.append(embedding_space.similarity(scene, shape).item())\n",
    "    plt.figure(figsize=(3,3))\n",
    "    plt.title(f'obj_{i}({id_to_shape[scene_labels[i]]})')\n",
    "    plt.bar(all_shapes, similarties)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "object1_shape : 0\nobject2_shape : 1\nobject3_shape : 2\nobject4_shape : 0\nget_attribute(obj1, shape): tensor([0.9323, 0.0342, 0.0336], grad_fn=<DivBackward0>)\nget_attribute(obj2, shape): tensor([0.0015, 0.5268, 0.4716], grad_fn=<DivBackward0>)\nget_attribute(obj3, shape): tensor([0.0012, 0.4675, 0.5312], grad_fn=<DivBackward0>)\nget_attribute(obj4, shape): tensor([0.9323, 0.0342, 0.0336], grad_fn=<DivBackward0>)\n"
    }
   ],
   "source": [
    "# get_attribute\n",
    "obj1 = scenes[0][0]\n",
    "obj2 = scenes[1][0]\n",
    "obj3 = scenes[2][0]\n",
    "obj4 = scenes[3][0]\n",
    "print(f'object1_shape : {scene_labels[0]}')\n",
    "print(f'object2_shape : {scene_labels[1]}')\n",
    "print(f'object3_shape : {scene_labels[2]}')\n",
    "print(f'object4_shape : {scene_labels[3]}')\n",
    "print(f'get_attribute(obj1, shape): {embedding_space.get_attribute(obj1, \"shape\")}')\n",
    "print(f'get_attribute(obj2, shape): {embedding_space.get_attribute(obj2, \"shape\")}')\n",
    "print(f'get_attribute(obj3, shape): {embedding_space.get_attribute(obj3, \"shape\")}')\n",
    "print(f'get_attribute(obj4, shape): {embedding_space.get_attribute(obj4, \"shape\")}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "obj1_shape : cube\nobj2_shape : sphere\nobj3_shape : cylinder\nobj4_shape : cube\ntensor([0.9323, 0.0342, 0.0336], grad_fn=<DivBackward0>)\n"
    },
    {
     "output_type": "error",
     "ename": "AttributeError",
     "evalue": "'AttributeEmbeddingSpace' object has no attribute 'is_attribute_equal'",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-13-6a3087e58686>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     17\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobj1_concept\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     18\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 19\u001b[0;31m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf'ae(obj1, obj2, shape) : {embedding_space.is_attribute_equal(obj1_concept, obj2_concept, \"shape\")}'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     20\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf'ae(obj1, obj3, shape) : {embedding_space.is_attribute_equal(obj1_concept, obj3_concept, \"shape\")}'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     21\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf'ae(obj1, obj4, shape) : {embedding_space.is_attribute_equal(obj1_concept, obj4_concept, \"shape\")}'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.local/lib/python3.7/site-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m__getattr__\u001b[0;34m(self, name)\u001b[0m\n\u001b[1;32m    574\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mmodules\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    575\u001b[0m         raise AttributeError(\"'{}' object has no attribute '{}'\".format(\n\u001b[0;32m--> 576\u001b[0;31m             type(self).__name__, name))\n\u001b[0m\u001b[1;32m    577\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    578\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__setattr__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mAttributeError\u001b[0m: 'AttributeEmbeddingSpace' object has no attribute 'is_attribute_equal'"
     ]
    }
   ],
   "source": [
    "# is_attribute_equal\n",
    "obj1 = scenes[0][0]\n",
    "obj2 = scenes[1][0]\n",
    "obj3 = scenes[2][0]\n",
    "obj4 = scenes[3][0]\n",
    "\n",
    "print(f'obj1_shape : {id_to_shape[scene_labels[0]]}')\n",
    "print(f'obj2_shape : {id_to_shape[scene_labels[1]]}')\n",
    "print(f'obj3_shape : {id_to_shape[scene_labels[2]]}')\n",
    "print(f'obj4_shape : {id_to_shape[scene_labels[3]]}')\n",
    "\n",
    "obj1_concept = embedding_space.get_attribute(obj1, \"shape\")\n",
    "obj2_concept = embedding_space.get_attribute(obj2, \"shape\")\n",
    "obj3_concept = embedding_space.get_attribute(obj3, \"shape\")\n",
    "obj4_concept = embedding_space.get_attribute(obj4, \"shape\")\n",
    "\n",
    "print(obj1_concept)\n",
    "\n",
    "print(f'ae(obj1, obj2, shape) : {embedding_space.is_attribute_equal(obj1_concept, obj2_concept, \"shape\")}')\n",
    "print(f'ae(obj1, obj3, shape) : {embedding_space.is_attribute_equal(obj1_concept, obj3_concept, \"shape\")}')\n",
    "print(f'ae(obj1, obj4, shape) : {embedding_space.is_attribute_equal(obj1_concept, obj4_concept, \"shape\")}')\n",
    "print(f'ae(obj2, obj3, shape) : {embedding_space.is_attribute_equal(obj2_concept, obj3_concept, \"shape\")}')\n",
    "print(f'ae(obj2, obj4, shape) : {embedding_space.is_attribute_equal(obj2_concept, obj4_concept, \"shape\")}')\n",
    "print(f'ae(obj3, obj4, shape) : {embedding_space.is_attribute_equal(obj3_concept, obj4_concept, \"shape\")}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "tensor([0.0100, 0.0700, 0.1600])\n"
    },
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "tensor(0.2400)"
     },
     "metadata": {},
     "execution_count": 14
    }
   ],
   "source": [
    "indices_vector = torch.tensor(list(range(3)))\n",
    "a = torch.tensor([0.1, 0.1, 0.8])\n",
    "b = torch.tensor([0.1, 0.7, 0.2])\n",
    "print(a * b)\n",
    "(a * b).sum()\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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