diff --git a/data/imagenet_idx_to_class.json b/data/imagenet_idx_to_class.json new file mode 100644 index 0000000..c0b134d --- /dev/null +++ b/data/imagenet_idx_to_class.json @@ -0,0 +1 @@ +["tench", "goldfish", "great white shark", "tiger shark", "hammerhead", "electric ray", "stingray", "cock", "hen", "ostrich", "brambling", "goldfinch", "house finch", "junco", "indigo bunting", "robin", "bulbul", "jay", "magpie", "chickadee", "water ouzel", "kite", "bald eagle", "vulture", "great grey owl", "European fire salamander", "common newt", "eft", "spotted salamander", "axolotl", "bullfrog", "tree frog", "tailed frog", "loggerhead", "leatherback turtle", "mud turtle", "terrapin", "box turtle", "banded gecko", "common iguana", "American chameleon", "whiptail", "agama", "frilled lizard", "alligator lizard", "Gila monster", "green lizard", "African chameleon", "Komodo dragon", "African crocodile", "American alligator", "triceratops", "thunder snake", "ringneck snake", "hognose snake", "green snake", "king snake", "garter snake", "water snake", "vine snake", "night snake", "boa constrictor", "rock python", "Indian cobra", "green mamba", "sea snake", "horned viper", "diamondback", "sidewinder", "trilobite", "harvestman", "scorpion", "black and gold garden spider", "barn spider", "garden spider", "black widow", "tarantula", "wolf spider", "tick", "centipede", "black grouse", "ptarmigan", "ruffed grouse", "prairie chicken", "peacock", "quail", "partridge", "African grey", "macaw", "sulphur-crested cockatoo", "lorikeet", "coucal", "bee eater", "hornbill", "hummingbird", "jacamar", "toucan", "drake", "red-breasted merganser", "goose", "black swan", "tusker", "echidna", "platypus", "wallaby", "koala", "wombat", "jellyfish", "sea anemone", "brain coral", "flatworm", "nematode", "conch", "snail", "slug", "sea slug", "chiton", "chambered nautilus", "Dungeness crab", "rock crab", "fiddler crab", "king crab", "American lobster", "spiny lobster", "crayfish", "hermit crab", "isopod", "white stork", "black stork", "spoonbill", "flamingo", "little blue heron", "American egret", "bittern", "crane", "limpkin", "European gallinule", "American coot", "bustard", "ruddy turnstone", "red-backed sandpiper", "redshank", "dowitcher", "oystercatcher", "pelican", "king penguin", "albatross", "grey whale", "killer whale", "dugong", "sea lion", "Chihuahua", "Japanese spaniel", "Maltese dog", "Pekinese", "Shih-Tzu", "Blenheim spaniel", "papillon", "toy terrier", "Rhodesian ridgeback", "Afghan hound", "basset", "beagle", "bloodhound", "bluetick", "black-and-tan coonhound", "Walker hound", "English foxhound", "redbone", "borzoi", "Irish wolfhound", "Italian greyhound", "whippet", "Ibizan hound", "Norwegian elkhound", "otterhound", "Saluki", "Scottish deerhound", "Weimaraner", "Staffordshire bullterrier", "American Staffordshire terrier", "Bedlington terrier", "Border terrier", "Kerry blue terrier", "Irish terrier", "Norfolk terrier", "Norwich terrier", "Yorkshire terrier", "wire-haired fox terrier", "Lakeland terrier", "Sealyham terrier", "Airedale", "cairn", "Australian terrier", "Dandie Dinmont", "Boston bull", "miniature schnauzer", "giant schnauzer", "standard schnauzer", "Scotch terrier", "Tibetan terrier", "silky terrier", "soft-coated wheaten terrier", "West Highland white terrier", "Lhasa", "flat-coated retriever", "curly-coated retriever", "golden retriever", "Labrador retriever", "Chesapeake Bay retriever", "German short-haired pointer", "vizsla", "English setter", "Irish setter", "Gordon setter", "Brittany spaniel", "clumber", "English springer", "Welsh springer spaniel", "cocker spaniel", "Sussex spaniel", "Irish water spaniel", "kuvasz", "schipperke", "groenendael", "malinois", "briard", "kelpie", "komondor", "Old English sheepdog", "Shetland sheepdog", "collie", "Border collie", "Bouvier des Flandres", "Rottweiler", "German shepherd", "Doberman", "miniature pinscher", "Greater Swiss Mountain dog", "Bernese mountain dog", "Appenzeller", "EntleBucher", "boxer", "bull mastiff", "Tibetan mastiff", "French bulldog", "Great Dane", "Saint Bernard", "Eskimo dog", "malamute", "Siberian husky", "dalmatian", "affenpinscher", "basenji", "pug", "Leonberg", "Newfoundland", "Great Pyrenees", "Samoyed", "Pomeranian", "chow", "keeshond", "Brabancon griffon", "Pembroke", "Cardigan", "toy poodle", "miniature poodle", "standard poodle", "Mexican hairless", "timber wolf", "white wolf", "red wolf", "coyote", "dingo", "dhole", "African hunting dog", "hyena", "red fox", "kit fox", "Arctic fox", "grey fox", "tabby", "tiger cat", "Persian cat", "Siamese cat", "Egyptian cat", "cougar", "lynx", "leopard", "snow leopard", "jaguar", "lion", "tiger", "cheetah", "brown bear", "American black bear", "ice bear", "sloth bear", "mongoose", "meerkat", "tiger beetle", "ladybug", "ground beetle", "long-horned beetle", "leaf beetle", "dung beetle", "rhinoceros beetle", "weevil", "fly", "bee", "ant", "grasshopper", "cricket", "walking stick", "cockroach", "mantis", "cicada", "leafhopper", "lacewing", "dragonfly", "damselfly", "admiral", "ringlet", "monarch", "cabbage butterfly", "sulphur butterfly", "lycaenid", "starfish", "sea urchin", "sea cucumber", "wood rabbit", "hare", "Angora", "hamster", "porcupine", "fox squirrel", "marmot", "beaver", "guinea pig", "sorrel", "zebra", "hog", "wild boar", "warthog", "hippopotamus", "ox", "water buffalo", "bison", "ram", "bighorn", "ibex", "hartebeest", "impala", "gazelle", "Arabian camel", "llama", "weasel", "mink", "polecat", "black-footed ferret", "otter", "skunk", "badger", "armadillo", "three-toed sloth", "orangutan", "gorilla", "chimpanzee", "gibbon", "siamang", "guenon", "patas", "baboon", "macaque", "langur", "colobus", "proboscis monkey", "marmoset", "capuchin", "howler monkey", "titi", "spider monkey", "squirrel monkey", "Madagascar cat", "indri", "Indian elephant", "African elephant", "lesser panda", "giant panda", "barracouta", "eel", "coho", "rock beauty", "anemone fish", "sturgeon", "gar", "lionfish", "puffer", "abacus", "abaya", "academic gown", "accordion", "acoustic guitar", "aircraft carrier", "airliner", "airship", "altar", "ambulance", "amphibian", "analog clock", "apiary", "apron", "ashcan", "assault rifle", "backpack", "bakery", "balance beam", "balloon", "ballpoint", "Band Aid", "banjo", "bannister", "barbell", "barber chair", "barbershop", "barn", "barometer", "barrel", "barrow", "baseball", "basketball", "bassinet", "bassoon", "bathing cap", "bath towel", "bathtub", "beach wagon", "beacon", "beaker", "bearskin", "beer bottle", "beer glass", "bell cote", "bib", "bicycle-built-for-two", "bikini", "binder", "binoculars", "birdhouse", "boathouse", "bobsled", "bolo tie", "bonnet", "bookcase", "bookshop", "bottlecap", "bow", "bow tie", "brass", "brassiere", "breakwater", "breastplate", "broom", "bucket", "buckle", "bulletproof vest", "bullet train", "butcher shop", "cab", "caldron", "candle", "cannon", "canoe", "can opener", "cardigan", "car mirror", "carousel", "carpenter's kit", "carton", "car wheel", "cash machine", "cassette", "cassette player", "castle", "catamaran", "CD player", "cello", "cellular telephone", "chain", "chainlink fence", "chain mail", "chain saw", "chest", "chiffonier", "chime", "china cabinet", "Christmas stocking", "church", "cinema", "cleaver", "cliff dwelling", "cloak", "clog", "cocktail shaker", "coffee mug", "coffeepot", "coil", "combination lock", "computer keyboard", "confectionery", "container ship", "convertible", "corkscrew", "cornet", "cowboy boot", "cowboy hat", "cradle", "crane", "crash helmet", "crate", "crib", "Crock Pot", "croquet ball", "crutch", "cuirass", "dam", "desk", "desktop computer", "dial telephone", "diaper", "digital clock", "digital watch", "dining table", "dishrag", "dishwasher", "disk brake", "dock", "dogsled", "dome", "doormat", "drilling platform", "drum", "drumstick", "dumbbell", "Dutch oven", "electric fan", "electric guitar", "electric locomotive", "entertainment center", "envelope", "espresso maker", "face powder", "feather boa", "file", "fireboat", "fire engine", "fire screen", "flagpole", "flute", "folding chair", "football helmet", "forklift", "fountain", "fountain pen", "four-poster", "freight car", "French horn", "frying pan", "fur coat", "garbage truck", "gasmask", "gas pump", "goblet", "go-kart", "golf ball", "golfcart", "gondola", "gong", "gown", "grand piano", "greenhouse", "grille", "grocery store", "guillotine", "hair slide", "hair spray", "half track", "hammer", "hamper", "hand blower", "hand-held computer", "handkerchief", "hard disc", "harmonica", "harp", "harvester", "hatchet", "holster", "home theater", "honeycomb", "hook", "hoopskirt", "horizontal bar", "horse cart", "hourglass", "iPod", "iron", "jack-o'-lantern", "jean", "jeep", "jersey", "jigsaw puzzle", "jinrikisha", "joystick", "kimono", "knee pad", "knot", "lab coat", "ladle", "lampshade", "laptop", "lawn mower", "lens cap", "letter opener", "library", "lifeboat", "lighter", "limousine", "liner", "lipstick", "Loafer", "lotion", "loudspeaker", "loupe", "lumbermill", "magnetic compass", "mailbag", "mailbox", "maillot", "maillot", "manhole cover", "maraca", "marimba", "mask", "matchstick", "maypole", "maze", "measuring cup", "medicine chest", "megalith", "microphone", "microwave", "military uniform", "milk can", "minibus", "miniskirt", "minivan", "missile", "mitten", "mixing bowl", "mobile home", "Model T", "modem", "monastery", "monitor", "moped", "mortar", "mortarboard", "mosque", "mosquito net", "motor scooter", "mountain bike", "mountain tent", "mouse", "mousetrap", "moving van", "muzzle", "nail", "neck brace", "necklace", "nipple", "notebook", "obelisk", "oboe", "ocarina", "odometer", "oil filter", "organ", "oscilloscope", "overskirt", "oxcart", "oxygen mask", "packet", "paddle", "paddlewheel", "padlock", "paintbrush", "pajama", "palace", "panpipe", "paper towel", "parachute", "parallel bars", "park bench", "parking meter", "passenger car", "patio", "pay-phone", "pedestal", "pencil box", "pencil sharpener", "perfume", "Petri dish", "photocopier", "pick", "pickelhaube", "picket fence", "pickup", "pier", "piggy bank", "pill bottle", "pillow", "ping-pong ball", "pinwheel", "pirate", "pitcher", "plane", "planetarium", "plastic bag", "plate rack", "plow", "plunger", "Polaroid camera", "pole", "police van", "poncho", "pool table", "pop bottle", "pot", "potter's wheel", "power drill", "prayer rug", "printer", "prison", "projectile", "projector", "puck", "punching bag", "purse", "quill", "quilt", "racer", "racket", "radiator", "radio", "radio telescope", "rain barrel", "recreational vehicle", "reel", "reflex camera", "refrigerator", "remote control", "restaurant", "revolver", "rifle", "rocking chair", "rotisserie", "rubber eraser", "rugby ball", "rule", "running shoe", "safe", "safety pin", "saltshaker", "sandal", "sarong", "sax", "scabbard", "scale", "school bus", "schooner", "scoreboard", "screen", "screw", "screwdriver", "seat belt", "sewing machine", "shield", "shoe shop", "shoji", "shopping basket", "shopping cart", "shovel", "shower cap", "shower curtain", "ski", "ski mask", "sleeping bag", "slide rule", "sliding door", "slot", "snorkel", "snowmobile", "snowplow", "soap dispenser", "soccer ball", "sock", "solar dish", "sombrero", "soup bowl", "space bar", "space heater", "space shuttle", "spatula", "speedboat", "spider web", "spindle", "sports car", "spotlight", "stage", "steam locomotive", "steel arch bridge", "steel drum", "stethoscope", "stole", "stone wall", "stopwatch", "stove", "strainer", "streetcar", "stretcher", "studio couch", "stupa", "submarine", "suit", "sundial", "sunglass", "sunglasses", "sunscreen", "suspension bridge", "swab", "sweatshirt", "swimming trunks", "swing", "switch", "syringe", "table lamp", "tank", "tape player", "teapot", "teddy", "television", "tennis ball", "thatch", "theater curtain", "thimble", "thresher", "throne", "tile roof", "toaster", "tobacco shop", "toilet seat", "torch", "totem pole", "tow truck", "toyshop", "tractor", "trailer truck", "tray", "trench coat", "tricycle", "trimaran", "tripod", "triumphal arch", "trolleybus", "trombone", "tub", "turnstile", "typewriter keyboard", "umbrella", "unicycle", "upright", "vacuum", "vase", "vault", "velvet", "vending machine", "vestment", "viaduct", "violin", "volleyball", "waffle iron", "wall clock", "wallet", "wardrobe", "warplane", "washbasin", "washer", "water bottle", "water jug", "water tower", "whiskey jug", "whistle", "wig", "window screen", "window shade", "Windsor tie", "wine bottle", "wing", "wok", "wooden spoon", "wool", "worm fence", "wreck", "yawl", "yurt", "web site", "comic book", "crossword puzzle", "street sign", "traffic light", "book jacket", "menu", "plate", "guacamole", "consomme", "hot pot", "trifle", "ice cream", "ice lolly", "French loaf", "bagel", "pretzel", "cheeseburger", "hotdog", "mashed potato", "head cabbage", "broccoli", "cauliflower", "zucchini", "spaghetti squash", "acorn squash", "butternut squash", "cucumber", "artichoke", "bell pepper", "cardoon", "mushroom", "Granny Smith", "strawberry", "orange", "lemon", "fig", "pineapple", "banana", "jackfruit", "custard apple", "pomegranate", "hay", "carbonara", "chocolate sauce", "dough", "meat loaf", "pizza", "potpie", "burrito", "red wine", "espresso", "cup", "eggnog", "alp", "bubble", "cliff", "coral reef", "geyser", "lakeside", "promontory", "sandbar", "seashore", "valley", "volcano", "ballplayer", "groom", "scuba diver", "rapeseed", "daisy", "yellow lady's slipper", "corn", "acorn", "hip", "buckeye", "coral fungus", "agaric", "gyromitra", "stinkhorn", "earthstar", "hen-of-the-woods", "bolete", "ear", "toilet tissue"] \ No newline at end of file diff --git a/notebooks/pytorch/MLA-CV-Lecture1-CNN.ipynb b/notebooks/pytorch/MLA-CV-Lecture1-CNN.ipynb new file mode 100644 index 0000000..2d4b7a0 --- /dev/null +++ b/notebooks/pytorch/MLA-CV-Lecture1-CNN.ipynb @@ -0,0 +1,661 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "![MLU Logo](../../data/MLU_Logo.png)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "-" + } + }, + "source": [ + "# Machine Learning Accelerator - Computer Vision - Lecture 1\n", + "\n", + "\n", + "## Convolutional Neural Networks with PyTorch\n", + "\n", + "In this notebook, we strengthen the fundamental understanding of Convolutional Neural Network (CNN) by using built-in CNN architectures in [PyTorch](https://pytorch.org/docs/stable/index.html) to train a multiclass classification model on a real-world dataset.\n", + "\n", + "1. A Toy Example\n", + " * Convolution 2D \n", + " * Padding and Stride\n", + " * Computing the Shape\n", + " * Pooling\n", + " \n", + " \n", + "2. A Real-world Example - MINC\n", + " * Loading the datasets\n", + " * Designing the Network Architectures" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "Notice that, if you are running on GPU, please ensure you are:\n", + "1. using library `torch` >= 1.6.0, and\n", + "1. using library `d2l` >= 0.15.0\n", + "\n", + "by running the following updating command." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "torch 1.6.0 \r\n", + "torchvision 0.7.0 \r\n" + ] + } + ], + "source": [ + "! pip list | egrep torch" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "d2l 0.15.0 \r\n" + ] + } + ], + "source": [ + "! pip list | egrep d2l" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "You should see `torch` and `d2l` are listing with its versions. If the above packages meet the requirements, feel free to skip the next line of installation code. If not, simply running the following code to install the packages:" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "# ! pip install torch torchvision\n", + "# ! pip install -q d2l==0.15.0" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "After the sanity check, let's import the packages for this notebook:" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "from d2l import torch as d2l\n", + "import numpy as np\n", + "import torch\n", + "import torchvision\n", + "from torch import nn\n", + "from torchvision import transforms\n", + "from torch.nn import BCELoss\n", + "from sklearn.metrics import accuracy_score" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "fragment" + } + }, + "source": [ + "## 1. A Toy Example\n", + "(Go to top)\n", + "\n", + "### 1.1 Convolution 2D\n", + "(Go to top)\n", + "\n", + "Firstly, let's use the built-in CNN classes in PyTorch with a toy example. PyTorch has a variety of convolutional layers such as \n", + "\n", + "\n", + "```python\n", + "nn.Conv1d()\n", + "nn.Conv2d()\n", + "nn.Conv3d()\n", + "```\n", + "\n", + "and more operators [here](https://pytorch.org/docs/stable/nn.html#convolution-layers). \n", + "\n", + "\n", + "### 1.2 Padding and Stride\n", + "(Go to top)\n", + "\n", + "In the built-in classes, we can also add padding and stride. Recall that:\n", + "\n", + "\"padding\" adds rows/columns around the input, \n", + "\n", + "![Padding.](https://d2l.ai/_images/conv-pad.svg)\n", + "\n", + "\n", + "while \"stride\" refers to the number of “unit” the kernel shifted per slide over rows/columns.\n", + "\n", + "![Stride.](https://d2l.ai/_images/conv-stride.svg)\n", + "\n", + "\n", + "\n", + "### 1.3 Computing the Shape\n", + "(Go to top)\n", + "\n", + "\n", + "Let's experiment an example with input shape of (3, 3), with a kernel size of 2, padding size of 1 on both sides and stride size of (2, 3). The output shape of the `Conv2d()` should be:\n", + "\n", + "\\begin{align}\n", + "\\text{ Output shape} & = \\lfloor(n_h-k_h+p_h+s_h)/s_h\\rfloor \\times \\lfloor(n_w-k_w+p_w+s_w)/s_w\\rfloor \\\\\n", + " & = \\lfloor(3 - 2 + 2*1 + 2) / 2\\rfloor \\times \\lfloor(3 - 2 + 2*1 + 3) / 3\\rfloor \\\\\n", + " & = (2, 2)\n", + "\\end{align}\n", + "\n", + "Let's validate in code! To check the output of the convolution layers, we define the `comp_conv2d` function as forward propogation." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "def comp_conv2d(conv2d, X):\n", + " # Add batch and channel dimension.\n", + " X = X.reshape((1, 1) + X.shape) # after reshape, shape=(1,1,3,3)\n", + " Y = conv2d(X)\n", + " # Exclude the first two dimensions\n", + " return Y.reshape(Y.shape[2:])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we can verify the output shape of the Conv2D layer." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "ExecuteTime": { + "end_time": "2019-07-03T22:12:43.364745Z", + "start_time": "2019-07-03T22:12:43.345529Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "torch.Size([2, 2])" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "X = torch.rand(size=(3, 3))\n", + "conv2d = nn.Conv2d(in_channels=1, out_channels=1, kernel_size=2, padding=1, stride=(2,3))\n", + "comp_conv2d(conv2d, X).shape" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 1.4 Pooling\n", + "(Go to top)\n", + "\n", + "Recall max pooling returns the maximal value in the pooling window, while average pooling summizes the means.\n", + "\n", + "![Pooling.](https://d2l.ai/_images/pooling.svg)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "We can also import built-in pooling layer from PyTorch with Padding and Stride, such as `MaxPool2d()` or `AvgPool1d()`. See full list of built-in pooling architectures [here](https://pytorch.org/docs/stable/nn.html#pooling-layers)." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "tensor([[[[ 0., 1., 2., 3.],\n", + " [ 4., 5., 6., 7.],\n", + " [ 8., 9., 10., 11.],\n", + " [12., 13., 14., 15.]]]])\n" + ] + }, + { + "data": { + "text/plain": [ + "tensor([[[[ 5., 7.],\n", + " [13., 15.]]]])" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "X = torch.arange(16, dtype=torch.float32).reshape((1, 1, 4, 4))\n", + "print(X)\n", + "pool2d = nn.MaxPool2d(kernel_size=3, padding=1, stride=2)\n", + "pool2d(X)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 2. A Real-world Example - MINC\n", + "(Go to top)\n", + "\n", + "[MINC](http://opensurfaces.cs.cornell.edu/publications/minc/) is short for Materials in Context Database, provided by Cornell. __We will use a subset of this dataset with the following classes: brick, carpet, food, mirror, sky, water.__ It is well labeled and has a moderate size thus is perfect to be our example.\n", + "\n", + "\n", + "![MINC 2500 Examples.](https://raw.githubusercontent.com/dmlc/web-data/master/gluoncv/datasets/MINC-2500.png)\n", + "\n", + "\n", + "### 2.1 Loading the datasets\n", + "(Go to top)\n", + "\n", + "First, let's define the paths for train, validation and test dataset. " + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "path = '../../data/minc-2500'\n", + "train_path = os.path.join(path, 'train')\n", + "val_path = os.path.join(path, 'val')\n", + "test_path = os.path.join(path, 'test')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "It is a good practice to visualize what does the dataset look like! Let's define the `show_images` function and see some sample images in MINC." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "def show_images(imgs, num_rows, num_cols, titles=None, scale=1.5):\n", + " \"\"\"Plot a list of images.\"\"\"\n", + " figsize = (num_cols * scale, num_rows * scale)\n", + " _, axes = d2l.plt.subplots(num_rows, num_cols, figsize=figsize)\n", + " axes = axes.flatten()\n", + " for i, (ax, img) in enumerate(zip(axes, imgs)):\n", + " ax.imshow(img.permute(1,2,0).numpy())\n", + " ax.axes.get_xaxis().set_visible(False)\n", + " ax.axes.get_yaxis().set_visible(False)\n", + " if titles:\n", + " ax.set_title(titles[i])\n", + " return axes" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "train_dataset = torchvision.datasets.ImageFolder(test_path, transform=transforms.ToTensor())\n", + "test_sample = torch.utils.data.DataLoader(train_dataset, batch_size=2*8, shuffle=True)\n", + "\n", + "for data, label in test_sample:\n", + " show_images(data, 2, 8);\n", + " break" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To load the dataset properly, we need to massage the image data a bit by some `transfom` functions. First, we load the image data and resize it to the given size (224,224). Next, we convert the image tensor of shape (C x H x W) in the range [0, 255] to a float32 torch tensor of shape (C x H x W) in the range (0, 1) using the `ToTensor` class. Last, we normalize an tensor of shape (C x H x W) with its mean and standard deviation by `Normalize`." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "transformation = transforms.Compose([\n", + " transforms.Resize(224),\n", + " transforms.ToTensor(),\n", + " transforms.Normalize(mean=(0,0,0), std=(1,1,1))\n", + "])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now apply the predefined transform functions and load the train, validation and test sets.\n", + "\n", + "In practice, reading data can be a significant performance bottleneck, especially when our model is simple or when our computer is fast. To make our life easier when reading from the datasets, we use a `DataLoader` of PyTorch, which reads a minibatch of data with size `batch_size` each time." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "batch_size = 16\n", + "\n", + "train_loader = torch.utils.data.DataLoader(\n", + " torchvision.datasets.ImageFolder(train_path, transform=transformation),\n", + " batch_size=batch_size, shuffle=True)\n", + "\n", + "validation_loader = torch.utils.data.DataLoader(\n", + " torchvision.datasets.ImageFolder(val_path, transform=transformation),\n", + " batch_size=batch_size, shuffle=False)\n", + "\n", + "test_data = torch.utils.data.DataLoader(\n", + " torchvision.datasets.ImageFolder(test_path, transform=transformation),\n", + " batch_size=batch_size, shuffle=False)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 2.2 Designing the Network Architectures\n", + "(Go to top)\n", + "\n", + "Now it's the time to design a Convolutional neural network! First, let's initailize a `Sequential` block. In PyTorch, `Sequential` defines a container for several layers that will be chained together. Given input data, a `Sequential` passes it through the first layer, in turn passing the output as the second layer’s input and so forth.\n", + "\n", + "We will build a neural netword with a 2D convolutional layer `Conv2D`, following by a 2D maxpooling layer `MaxPool2D`, a fully connected (or `Dense`) layer, and a final output `Dense` layer with output classes 23." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [], + "source": [ + "out_classes = 6\n", + "\n", + "net = nn.Sequential(\n", + " nn.Conv2d(in_channels=3, out_channels=20, kernel_size=5),\n", + " nn.ReLU(),\n", + " nn.MaxPool2d(kernel_size=2, stride=2),\n", + " # The Flatten layer collapses all axis, except the first one, into one axis.\n", + " nn.Flatten(),\n", + " nn.Linear(110*110*20, 128),\n", + " nn.ReLU(),\n", + " nn.Linear(128, out_classes))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Our network is almost ready to be trained! One last thing before the training is to set up the hyperparameters, such training device `device` (GPU or CPU), the number of epochs to train, the learning rate of optimization algorithms. Besides, we specify the loss function. Since this problem is a multiclass classification task, we will use `CrossEntropyLoss` as our loss funciton." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [], + "source": [ + "device = d2l.try_gpu() # Set this to CPU or GPU depending on your training instance\n", + "\n", + "epochs = 15\n", + "learning_rate = 0.01\n", + "criterion = nn.CrossEntropyLoss()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To infer the neural network \"how to optimize its weights\", we instantiate the `optim.`, which defines the parameters to optimize over (obtainable from our net via net.parameters()) and the hyperparameters required by our optimization algorithm." + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [], + "source": [ + "optimizer = torch.optim.SGD(net.parameters(), lr=learning_rate)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now it's the training time! Starting with the outer loop, we will have 15 epochs (15 full pass through our dataset)." + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 1: train loss 1.697, train acc 0.267, val loss 1.482, val acc 0.354\n", + "Epoch 2: train loss 1.469, train acc 0.391, val loss 1.486, val acc 0.354\n", + "Epoch 3: train loss 1.362, train acc 0.446, val loss 1.270, val acc 0.458\n", + "Epoch 4: train loss 1.247, train acc 0.508, val loss 1.198, val acc 0.547\n", + "Epoch 5: train loss 1.169, train acc 0.551, val loss 1.263, val acc 0.557\n", + "Epoch 6: train loss 1.059, train acc 0.603, val loss 1.302, val acc 0.505\n", + "Epoch 7: train loss 0.984, train acc 0.647, val loss 1.087, val acc 0.573\n", + "Epoch 8: train loss 0.919, train acc 0.665, val loss 1.119, val acc 0.589\n", + "Epoch 9: train loss 0.891, train acc 0.692, val loss 1.167, val acc 0.589\n", + "Epoch 10: train loss 0.772, train acc 0.722, val loss 1.118, val acc 0.583\n", + "Epoch 11: train loss 0.688, train acc 0.767, val loss 1.165, val acc 0.625\n", + "Epoch 12: train loss 0.608, train acc 0.792, val loss 1.143, val acc 0.651\n", + "Epoch 13: train loss 0.549, train acc 0.813, val loss 1.606, val acc 0.542\n", + "Epoch 14: train loss 0.502, train acc 0.830, val loss 1.099, val acc 0.651\n", + "Epoch 15: train loss 0.426, train acc 0.878, val loss 1.204, val acc 0.635\n" + ] + } + ], + "source": [ + "for epoch in range(epochs):\n", + " net = net.to(device)\n", + " \n", + " train_loss, val_loss, train_acc, valid_acc = 0., 0., 0., 0.\n", + " \n", + " # Training loop: (with autograd and trainer steps, etc.)\n", + " # This loop does the training of the neural network (weights are updated)\n", + " for i, (data, label) in enumerate(train_loader):\n", + " # zero the parameter gradients\n", + " optimizer.zero_grad()\n", + " data = data.to(device)\n", + " label = label.to(device)\n", + " output = net(data)\n", + " loss = criterion(output, label)\n", + " loss.backward()\n", + " train_acc += (output.argmax(axis=1) == label.float()).float().mean()\n", + " train_loss += loss\n", + " optimizer.step()\n", + " \n", + " # Validation loop:\n", + " # This loop tests the trained network on validation dataset\n", + " # No weight updates here\n", + " for i, (data, label) in enumerate(validation_loader):\n", + " data = data.to(device)\n", + " label = label.to(device)\n", + " output = net(data)\n", + " valid_acc += (output.argmax(axis=1) == label.float()).float().mean()\n", + " val_loss += criterion(output, label)\n", + " \n", + " # Take averages\n", + " train_loss /= len(train_loader)\n", + " train_acc /= len(train_loader)\n", + " val_loss /= len(validation_loader)\n", + " valid_acc /= len(validation_loader)\n", + " \n", + " print(\"Epoch %d: train loss %.3f, train acc %.3f, val loss %.3f, val acc %.3f\" % (\n", + " epoch+1, train_loss.detach().cpu().numpy(), train_acc.detach().cpu().numpy(),\n", + " val_loss.detach().cpu().numpy(), valid_acc.detach().cpu().numpy()))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "As you may notice that the training loss and accurcay keep improving, while the validation loss and accuracy are fluctuated. This is a signal of overfitting. As a result, in the following sessions, we will show you more advanced neural network architectures (such as AlexNet and ResNet) to stablize the training!" + ] + } + ], + "metadata": { + "celltoolbar": "Slideshow", + "kernelspec": { + "display_name": "Python [conda env:paperviz]", + "language": "python", + "name": "conda-env-paperviz-py" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.6" + }, + "latex_envs": { + "LaTeX_envs_menu_present": true, + "autoclose": false, + "autocomplete": true, + "bibliofile": "biblio.bib", + "cite_by": "apalike", + "current_citInitial": 1, + "eqLabelWithNumbers": true, + "eqNumInitial": 1, + "hotkeys": { + "equation": "Ctrl-E", + "itemize": "Ctrl-I" + }, + "labels_anchors": false, + "latex_user_defs": false, + "report_style_numbering": false, + "user_envs_cfg": false + }, + "toc": { + "base_numbering": 1, + "nav_menu": {}, + "number_sections": true, + "sideBar": true, + "skip_h1_title": false, + "title_cell": "Table of Contents", + "title_sidebar": "Contents", + "toc_cell": false, + "toc_position": {}, + "toc_section_display": true, + "toc_window_display": false + }, + "varInspector": { + "cols": { + "lenName": 16, + "lenType": 16, + "lenVar": 40 + }, + "kernels_config": { + "python": { + "delete_cmd_postfix": "", + "delete_cmd_prefix": "del ", + "library": "var_list.py", + "varRefreshCmd": "print(var_dic_list())" + }, + "r": { + "delete_cmd_postfix": ") ", + "delete_cmd_prefix": "rm(", + "library": "var_list.r", + "varRefreshCmd": "cat(var_dic_list()) " + } + }, + "types_to_exclude": [ + "module", + "function", + "builtin_function_or_method", + "instance", + "_Feature" + ], + "window_display": false + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/notebooks/pytorch/MLA-CV-Lecture1-Final-Project.ipynb b/notebooks/pytorch/MLA-CV-Lecture1-Final-Project.ipynb new file mode 100644 index 0000000..63f2ce3 --- /dev/null +++ b/notebooks/pytorch/MLA-CV-Lecture1-Final-Project.ipynb @@ -0,0 +1,231 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "![MLU Logo](../../data/MLU_Logo.png)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Machine Learning Accelerator - Computer Vision - Lecture 1\n", + "\n", + "\n", + "## Final Project\n", + "\n", + "The goal of the final project is to train a neural networks model for a binary classification between images of:\n", + "* software (sw), and\n", + "* video games (vg)\n", + "\n", + "You can use the __MLA-CV-Lecture1-CNN.ipynb__ notebook as a starting point to build your model. We will go through the following topics:\n", + "\n", + "1. Loading data \n", + "2. Training and Validation\n", + "3. Making predictions\n", + " " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's import all the libraries. Before that, make sure you have installed the required version mxnet and d2l library as below." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "# ! pip install -U torch==1.6.0 # updating torch to at least v1.6\n", + "# ! pip install -q d2l==0.15.0" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "import d2l\n", + "import torch\n", + "import torchvision\n", + "from torch import nn\n", + "from torchvision import transforms" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 1. Loading data\n", + "(Go to top)\n", + "\n", + "Your final project dataset is stored under the __\"final_project_dataset\"__ folder within the __\"data\"__ folder. Over there you will see the __train__, __val__ (for validation) and __test__ folders. Let's start creating the data transforms and loaders below. In this project, images come in different sizes and we will resize them to 224x224." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "transform_train = transforms.Compose([\n", + " transforms.Resize(224),\n", + " transforms.ToTensor(),\n", + " transforms.Normalize(mean=(0,0,0), std=(1,1,1))\n", + "])\n", + "\n", + "transform_test = transforms.Compose([\n", + " transforms.Resize(224),\n", + " transforms.ToTensor(),\n", + " transforms.Normalize(mean=(0,0,0), std=(1,1,1))\n", + "])\n", + "\n", + "batch_size = 16\n", + "\n", + "path = '../../data/final_project_dataset'\n", + "train_path = os.path.join(path, 'train')\n", + "val_path = os.path.join(path, 'val')\n", + "\n", + "train_loader = torch.utils.data.DataLoader(\n", + " torchvision.datasets.ImageFolder(train_path, transform=transform_train),\n", + " batch_size=batch_size, shuffle=True)\n", + "\n", + "validation_loader = torch.utils.data.DataLoader(\n", + " torchvision.datasets.ImageFolder(val_path, transform=transform_test),\n", + " batch_size=batch_size, shuffle=False)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's see some sample pictures." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "%matplotlib inline\n", + "import matplotlib.pyplot as plt\n", + "from matplotlib.image import imread\n", + "\n", + "im1 = imread(train_path + \"/sw/0.jpg\")\n", + "im2 = imread(train_path + \"/vg/0.jpg\")\n", + "\n", + "plt.imshow(im1)\n", + "plt.title(\"Software\")\n", + "plt.show()\n", + "plt.imshow(im2)\n", + "plt.title(\"Video Games\")\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 2. Training and Validation\n", + "(Go to top)\n", + "\n", + "Now, it's your turn! Free feel try experiment through the standard neural network, convolutional neural network, etc. \n", + "You can start with a network that we discuss in the class today. You will use the __train_loader__ and __validation_loader__ as above. \n", + "\n", + "Here are some ideas for you:\n", + "* Try differert numbers of layers, neurons size, kernel size, and see how does the network perform?!\n", + "* Experiment different combinations of batch_size (in the data loaders part) and learning rate in your code. " + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "## Build and train/validate your model here\n", + "## ...." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 3. Making predictions\n", + "(Go to top)\n", + "\n", + "After finishing training your model, it is time to read the test set and make predictions! You just need to load the test data and apply the similar `transforms` functions to the test set as below:" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "test_path = os.path.join(path, 'test')\n", + "\n", + "test_loader = torch.utils.data.DataLoader(\n", + " torchvision.datasets.ImageFolder(test_path, transform=transform_train),\n", + " batch_size=batch_size, shuffle=True)\n", + "\n", + "## Test your model here\n", + "## ...." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "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": "3.6.10" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/notebooks/pytorch/MLA-CV-Lecture1-Neural-Networks.ipynb b/notebooks/pytorch/MLA-CV-Lecture1-Neural-Networks.ipynb new file mode 100644 index 0000000..94dc318 --- /dev/null +++ b/notebooks/pytorch/MLA-CV-Lecture1-Neural-Networks.ipynb @@ -0,0 +1,481 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "![MLU Logo](../../data/MLU_Logo.png)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Machine Learning Accelerator - Computer Vision - Lecture 1\n", + "\n", + "## Neural Networks with PyTorch\n", + "\n", + "In this notebook, we build, train and validate a Neural Network in [PyTorch](https://pytorch.org/docs/stable/index.html), an open source machine learning framework that accelerates the path from research prototyping to production deployment with a clear, concise, and simple API. \n", + "\n", + "1. Implementing a neural network with PyTorch \n", + "2. Loss Functions\n", + "3. Training\n", + "4. Example - Binary Classification" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 1. Implementing a neural network with PyTorch\n", + "(Go to top)\n", + "\n", + "Let's implement a simple neural network with two hidden layers of size 64 using the sequential container (Adding things in sequence). We will have 3 inputs, 2 hidden layers and 1 output layer. Some drop-outs attached to the hidden layers." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Sequential(\n", + " (0): Linear(in_features=3, out_features=64, bias=True)\n", + " (1): Tanh()\n", + " (2): Dropout(p=0.4, inplace=False)\n", + " (3): Linear(in_features=64, out_features=64, bias=True)\n", + " (4): Tanh()\n", + " (5): Dropout(p=0.3, inplace=False)\n", + " (6): Linear(in_features=64, out_features=1, bias=True)\n", + ")\n" + ] + } + ], + "source": [ + "import torch\n", + "from torch import nn\n", + "\n", + "net = nn.Sequential(\n", + " nn.Linear(3, 64), # Linear layer-1 with 64 out_features and input size 3\n", + " nn.Tanh(), # Tanh activation is applied\n", + " nn.Dropout(p=0.4), # Apply random 40% drop-out to layer_1\n", + " nn.Linear(64, 64), # Linear layer-2 with 64 units and input size 64 \n", + " nn.Tanh(), # Tanh activation is applied\n", + " nn.Dropout(p=0.3), # Apply random 30% drop-out to layer_2\n", + " nn.Linear(64, 1)) # Output layer with single unit\n", + "\n", + "print(net)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The weight parameters of the `Linear` layer in pytorch are initialized with a modified form of the Xavier Initialization. Using these weights as a start, we can later apply optimization such as SGD to train the weights. As a result, using a strategic technique to initialize the weights is crucial. \n", + "\n", + "Here is a full list of [Initializers](https://pytorch.org/docs/stable/nn.init.html). The commonly used one is called *Xavier initilaization*, which can keep the scale of gradients roughly the same in all the layers. (Here are more technical details of [Xavier initilaization](https://d2l.ai/chapter_multilayer-perceptrons/numerical-stability-and-init.html#xavier-initialization).)" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Sequential(\n", + " (0): Linear(in_features=3, out_features=64, bias=True)\n", + " (1): Tanh()\n", + " (2): Dropout(p=0.4, inplace=False)\n", + " (3): Linear(in_features=64, out_features=64, bias=True)\n", + " (4): Tanh()\n", + " (5): Dropout(p=0.3, inplace=False)\n", + " (6): Linear(in_features=64, out_features=1, bias=True)\n", + ")" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "def xavier_init_weights(m):\n", + " if type(m) == nn.Linear:\n", + " torch.nn.init.xavier_uniform_(m.weight)\n", + "\n", + "net.apply(xavier_init_weights)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can easily access them with `net[layer_index]`:" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Linear(in_features=3, out_features=64, bias=True)\n", + "Tanh()\n", + "Dropout(p=0.4, inplace=False)\n", + "Linear(in_features=64, out_features=64, bias=True)\n", + "Tanh()\n", + "Dropout(p=0.3, inplace=False)\n", + "Linear(in_features=64, out_features=1, bias=True)\n" + ] + } + ], + "source": [ + "print(net[0])\n", + "print(net[1])\n", + "print(net[2])\n", + "print(net[3])\n", + "print(net[4])\n", + "print(net[5])\n", + "print(net[6])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 2. Loss Functions\n", + "(Go to top)\n", + "\n", + "We can select [loss functions](https://d2l.ai/chapter_linear-networks/linear-regression.html#loss-function) according to our problem. A full list of supported `Loss` functions in PyTorch are available [here](https://pytorch.org/docs/stable/nn.html#loss-functions). \n", + "\n", + "Let's go over some popular loss functions and see how to call a built-in loss function:\n", + "\n", + "\n", + "__Binary Cross-entropy Loss:__ A common used loss function for binary classification. \n", + "\n", + "```python\n", + "loss = nn.BCELoss()\n", + "```\n", + "\n", + "__Categorical Cross-entropy Loss:__ A common used loss function for multi-class classification. \n", + "\n", + "```python\n", + "loss = nn.CrossEntropyLoss()\n", + "```\n", + "\n", + "__MSE Loss:__ One of the most common loss functions for regression problems. \n", + "\n", + "```python\n", + "loss = nn.MSELoss()\n", + "```" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 3. Training\n", + "(Go to top)\n", + "\n", + "`torch.optim` module provides necessary optimization algorithms for neural networks. We can use the following `Optimizers` to train a network using [Stochastic Gradient Descent (SGD)](https://d2l.ai/chapter_optimization/sgd.html) method and learning rate of 0.001.\n", + "\n", + "```python\n", + "from torch import optim\n", + "optimizer = optim.SGD(net.parameters(), lr=0.001)\n", + "```" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 4. Example - Binary Classification\n", + "(Go to top)\n", + "\n", + "In this example, we will train a neural network on a dataset that we randomly generated. We will have two classes and train a neural network to classify them." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.datasets import make_circles\n", + "X, y = make_circles(n_samples=750, shuffle=True, random_state=42, noise=0.05, factor=0.3)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "First let's plot the simulated dataset." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "%matplotlib inline\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", + "\n", + "def plot_dataset(X, y, title):\n", + " \n", + " # Activate Seaborn visualization\n", + " sns.set()\n", + " \n", + " # Plot both classes: Class1->Blue, Class2->Red\n", + " plt.scatter(X[y==1, 0], X[y==1, 1], c='blue', label=\"class 1\")\n", + " plt.scatter(X[y==0, 0], X[y==0, 1], c='red', label=\"class 2\")\n", + " plt.legend(loc='upper right')\n", + " plt.xlabel('x1')\n", + " plt.ylabel('x2')\n", + " plt.xlim(-2, 2)\n", + " plt.ylim(-2, 2)\n", + " plt.title(title)\n", + " plt.show()\n", + " \n", + "plot_dataset(X, y, title=\"Dataset\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Next, we import the necessary libraries and classes." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "import time\n", + "from torch.nn import BCELoss" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Then, we create the network as below. It will have two hidden layers. Since the data seems easily seperable, we can have a small network (2 hidden layers) with 10 units at each layer." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "# Use GPU resource if available, otherwise wil use CPU\n", + "device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n", + "\n", + "net = nn.Sequential(nn.Linear(in_features=2, out_features=10),\n", + " nn.ReLU(),\n", + " nn.Linear(10, 10),\n", + " nn.ReLU(),\n", + " nn.Linear(10, 1),\n", + " nn.Sigmoid())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now let's prepare the training set and validation set, and load each of them to a `DataLoader`, respectively." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "# Split the dataset into two parts: 80%-20% split\n", + "X_train, X_val = X[0:int(len(X)*0.8), :], X[int(len(X)*0.8):, :]\n", + "y_train, y_val = y[:int(len(X)*0.8)], y[int(len(X)*0.8):]\n", + "\n", + "# Use PyTorch DataLoaders to load the data in batches\n", + "batch_size = 4 # How many samples to use for each weight update \n", + "train_dataset = torch.utils.data.TensorDataset(torch.tensor(X_train, dtype=torch.float32),\n", + " torch.tensor(y_train, dtype=torch.float32))\n", + "train_loader = torch.utils.data.DataLoader(train_dataset, batch_size=batch_size)\n", + "\n", + "# Move validation dataset on CPU/GPU device\n", + "X_val = torch.tensor(X_val, dtype=torch.float32).to(device)\n", + "y_val = torch.tensor(y_val, dtype=torch.float32).to(device)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Before the training, one last thing is to define the hyper-parameters for training." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "epochs = 50 # Total number of iterations\n", + "lr = 0.01 # Learning rate\n", + "\n", + "# Define the loss. As we used sigmoid in the last layer, we use `nn.BCELoss`.\n", + "# Otherwise we could have made use of `nn.BCEWithLogitsLoss`.\n", + "loss = BCELoss(reduction='none')\n", + "\n", + "# Define the optimizer, SGD with learning rate\n", + "optimizer = torch.optim.SGD(net.parameters(), lr=lr)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Finally, it is the time for training! We will run through the training set 50 times (i.e., epochs) and print training and validation losses at each epoch." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 0. Train_loss 0.691067 Validation_loss 0.680190 Seconds 0.074899\n", + "Epoch 9. Train_loss 0.046372 Validation_loss 0.038246 Seconds 0.058565\n", + "Epoch 19. Train_loss 0.005466 Validation_loss 0.005107 Seconds 0.057234\n", + "Epoch 29. Train_loss 0.002521 Validation_loss 0.002381 Seconds 0.066555\n", + "Epoch 39. Train_loss 0.001571 Validation_loss 0.001485 Seconds 0.057680\n", + "Epoch 49. Train_loss 0.001119 Validation_loss 0.001055 Seconds 0.058494\n" + ] + } + ], + "source": [ + "train_losses = []\n", + "val_losses = []\n", + "for epoch in range(epochs):\n", + " start = time.time()\n", + " training_loss = 0\n", + " # Build a training loop, to train the network\n", + " for idx, (data, target) in enumerate(train_loader):\n", + " # zero the parameter gradients\n", + " optimizer.zero_grad()\n", + " \n", + " data = data.to(device)\n", + " target = target.to(device).view(-1, 1)\n", + " \n", + " output = net(data)\n", + " L = loss(output, target).sum()\n", + " training_loss += L.item()\n", + " L.backward()\n", + " optimizer.step()\n", + " \n", + " # Get validation predictions\n", + " val_predictions = net(X_val)\n", + " # Calculate the validation loss\n", + " val_loss = torch.sum(loss(val_predictions, y_val.view(-1, 1))).item()\n", + " \n", + " # Take the average losses\n", + " training_loss = training_loss / len(y_train)\n", + " val_loss = val_loss / len(y_val)\n", + " \n", + " train_losses.append(training_loss)\n", + " val_losses.append(val_loss)\n", + " \n", + " end = time.time()\n", + " # Print the losses every 10 epochs\n", + " if (epoch == 0) or ((epoch+1)%10 == 0):\n", + " print(\"Epoch %s. Train_loss %f Validation_loss %f Seconds %f\" % \\\n", + " (epoch, training_loss, val_loss, end-start))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's see the training and validation loss plots below. Losses go down as the training process continues as expected." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "%matplotlib inline\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", + "\n", + "plt.plot(train_losses, label=\"Training Loss\")\n", + "plt.plot(val_losses, label=\"Validation Loss\")\n", + "plt.title(\"Loss values\")\n", + "plt.xlabel(\"Epoch\")\n", + "plt.ylabel(\"Loss\")\n", + "plt.legend()\n", + "plt.show()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "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": "3.7.9" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/notebooks/pytorch/MLA-CV-Lecture2-AlexNet.ipynb b/notebooks/pytorch/MLA-CV-Lecture2-AlexNet.ipynb new file mode 100644 index 0000000..edb8d09 --- /dev/null +++ b/notebooks/pytorch/MLA-CV-Lecture2-AlexNet.ipynb @@ -0,0 +1,519 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "![MLU Logo](../../data/MLU_Logo.png)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Machine Learning Accelerator - Computer Vision - Lecture 2\n", + "\n", + "\n", + "## Fine-Tuning with Pre-trained AlexNet \n", + "\n", + "In this notebook, we use a pre-trained [AlexNet](https://d2l.ai/chapter_convolutional-modern/alexnet.html) on the [MINC](http://opensurfaces.cs.cornell.edu/publications/minc/) dataset. This notebook is similar to our previous notebook `MLA-CV-Lecture1-CNN.ipynb`, so we may skip some details to be concise. We will cover the following topics:\n", + "\n", + "1. Loading and Transforming Dataset \n", + "2. Fine-tuning Pretrained AlexNet\n", + "3. Testing and Visualizations\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "First, let's update torch at least to v1.6.0 and d2l to v0.15.0" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "# ! pip install -U torch==1.6.0 # updating torch to at least v1.6\n", + "# ! pip install -q d2l==0.15.0" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now let's import the necessary libraries." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "from d2l import torch as d2l\n", + "import torch\n", + "import torchvision\n", + "from torch import nn\n", + "from torchvision import transforms\n", + "import numpy as np" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 1. Loading and Transforming Dataset\n", + "(Go to top)\n", + "\n", + "To load the dataset properly, we need to massage the image data a bit by some `transforms` functions. PyTorch provides a full list of [transforms functions](https://pytorch.org/docs/stable/torchvision/transforms.html) to enable a wide variety of data augmentation. \n", + "\n", + "We will process some simple data transformations in this example. First, we load the image data and resize it to the given size (224,224). Next, we convert the image tensor of shape (H x W x C) in the range [0, 255] to a float32 tensor of shape (C x H x W) in the range (0, 1) using the `ToTensor` class. Last, we normalize the tensor of shape (C x H x W) with its mean and standard deviation by `Normalize`." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "transform_train = transforms.Compose([\n", + " transforms.Resize(224),\n", + " transforms.ToTensor(),\n", + " transforms.Normalize(mean=(0,0,0), std=(1,1,1))\n", + "])\n", + "\n", + "transform_test = transforms.Compose([\n", + " transforms.Resize(224),\n", + " transforms.ToTensor(),\n", + " transforms.Normalize(mean=(0,0,0), std=(1,1,1))\n", + "])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now apply the predefined transform functions and load the train, validation and test sets.\n", + "\n", + "In practice, reading data can be a significant performance bottleneck, especially when our model is simple or when our computer is fast. To make our life easier when reading from the datasets, we use a `DataLoader` of Gluon, which reads a minibatch of data with size `batch_size` each time." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "batch_size = 16\n", + "\n", + "path = '../../data/minc-2500'\n", + "train_path = os.path.join(path, 'train')\n", + "val_path = os.path.join(path, 'val')\n", + "test_path = os.path.join(path, 'test')\n", + "\n", + "train_loader = torch.utils.data.DataLoader(\n", + " torchvision.datasets.ImageFolder(train_path, transform=transform_train),\n", + " batch_size=batch_size, shuffle=True)\n", + "\n", + "validation_loader = torch.utils.data.DataLoader(\n", + " torchvision.datasets.ImageFolder(val_path, transform=transform_test),\n", + " batch_size=batch_size, shuffle=False)\n", + "\n", + "test_loader = torch.utils.data.DataLoader(\n", + " torchvision.datasets.ImageFolder(test_path, transform=transform_test),\n", + " batch_size=batch_size, shuffle=False)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 2. Fine-tuning Pretrained AlexNet\n", + "(Go to top)\n", + "\n", + "To fine-tune a pretrained model, we need the following 4 steps:\n", + "1. Define a neural network **finetune_net** with AlexNet architecture, and later reshape for given number of output classes. Note that for `torchvision.models.alexnet` the default parameter `pretrained` is False, which means it will only return us an AlexNet architecture rather than an AlexNet architecture with the pretrained weights.\n", + "\n", + "1. Initialize the **finetune_net** with [Xavier initialization](https://d2l.ai/chapter_multilayer-perceptrons/numerical-stability-and-init.html#xavier-initialization) to make sure our random initialized weights are neither too small nor too huge.\n", + "\n", + "1. Define another neural network, **pretrained_net**, and load the pretrained AlexNet model (which was trained on ImageNet) on it. Here, by specifying ``pretrained=True``, it will automatically download the model from torchvision model zoo if necessary. For more pretrained models, please refer to [torchvision Models](https://pytorch.org/docs/stable/torchvision/models.html).\n", + "\n", + "1. Transfer the trained weights (except last layer) from **pretrained_net** to **finetune_net**, and output **finetune_net**." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "feature_extract = True\n", + "def set_parameter_requires_grad(model, feature_extracting):\n", + " if feature_extracting:\n", + " for param in model.parameters():\n", + " param.requires_grad = False\n", + "\n", + "def FineTuneAlexnet(classes, device):\n", + " '''\n", + " classes: number of the output classes \n", + " device: training context (CPU or GPU)\n", + " '''\n", + " finetune_net = torchvision.models.alexnet(pretrained=True).to(device)\n", + " set_parameter_requires_grad(finetune_net, feature_extract)\n", + " num_ftrs = finetune_net.classifier[6].in_features\n", + " finetune_net.classifier[6] = nn.Linear(num_ftrs, classes)\n", + " \n", + " return finetune_net" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's create a `net` using the `FineTuneAlexnet` on available GPUs (or CPUs) by defining the training context `ctx`. Since the MINC dataset has 6 categories, the output classes will be 6." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Downloading: \"https://download.pytorch.org/models/alexnet-owt-4df8aa71.pth\" to /home/ubuntu/.cache/torch/hub/checkpoints/alexnet-owt-4df8aa71.pth\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "e1f9d87d00044e3f83fbafb090d8decb", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "HBox(children=(FloatProgress(value=0.0, max=244418560.0), HTML(value='')))" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n" + ] + }, + { + "data": { + "text/plain": [ + "AlexNet(\n", + " (features): Sequential(\n", + " (0): Conv2d(3, 64, kernel_size=(11, 11), stride=(4, 4), padding=(2, 2))\n", + " (1): ReLU(inplace=True)\n", + " (2): MaxPool2d(kernel_size=3, stride=2, padding=0, dilation=1, ceil_mode=False)\n", + " (3): Conv2d(64, 192, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2))\n", + " (4): ReLU(inplace=True)\n", + " (5): MaxPool2d(kernel_size=3, stride=2, padding=0, dilation=1, ceil_mode=False)\n", + " (6): Conv2d(192, 384, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n", + " (7): ReLU(inplace=True)\n", + " (8): Conv2d(384, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n", + " (9): ReLU(inplace=True)\n", + " (10): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n", + " (11): ReLU(inplace=True)\n", + " (12): MaxPool2d(kernel_size=3, stride=2, padding=0, dilation=1, ceil_mode=False)\n", + " )\n", + " (avgpool): AdaptiveAvgPool2d(output_size=(6, 6))\n", + " (classifier): Sequential(\n", + " (0): Dropout(p=0.5, inplace=False)\n", + " (1): Linear(in_features=9216, out_features=4096, bias=True)\n", + " (2): ReLU(inplace=True)\n", + " (3): Dropout(p=0.5, inplace=False)\n", + " (4): Linear(in_features=4096, out_features=4096, bias=True)\n", + " (5): ReLU(inplace=True)\n", + " (6): Linear(in_features=4096, out_features=6, bias=True)\n", + " )\n", + ")" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "device = d2l.try_gpu() # Create neural net on CPU or GPU depending on your training instances\n", + "num_outputs = 6 # 6 output classes\n", + "net = FineTuneAlexnet(num_outputs, device)\n", + "\n", + "net" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Next, we set up the hyperparameters for training, such as the learning rate of optimization algorithms. With the defined learning rate, we are able to create an `Optimizer` to infer the neural network \"how to optimize its weights\"." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Params to learn:\n", + "\t classifier.6.weight\n", + "\t classifier.6.bias\n" + ] + } + ], + "source": [ + "learning_rate = 0.001\n", + "\n", + "# We will only update the parameters that we have just initialized,\n", + "# i.e. the parameters with requires_grad is True.\n", + "params_to_update = net.parameters()\n", + "print(\"Params to learn:\")\n", + "if feature_extract:\n", + " params_to_update = []\n", + " for name,param in net.named_parameters():\n", + " if param.requires_grad == True:\n", + " params_to_update.append(param)\n", + " print(\"\\t\",name)\n", + "else:\n", + " for name,param in model_ft.named_parameters():\n", + " if param.requires_grad == True:\n", + " print(\"\\t\",name)\n", + "\n", + "optimizer = torch.optim.SGD(params_to_update, lr=learning_rate)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Besides, we need to specify the loss function. Since this problem is a multiclass classification task, we will use softmax as our loss funciton." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "criterion = nn.CrossEntropyLoss()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Our network is almost ready to be finetuned! One last thing before the finetuning is to define the `accuracy` function for evulating our model." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "def finetune_accuracy(output, label):\n", + " # output: (batch, num_output) float32 tensor\n", + " # label: (batch, ) int32 tensor\n", + " return (output.argmax(axis=1) == label.float()).float().mean()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now it's the training time! Starting with the outer loop, we will have 10 epochs (10 full pass through our dataset). Within the inner loop, we yield each mini-batch from the `train_loader`, and update the weights based on the average statistics of this mini-batch." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 1: train loss 1.185, train acc 0.611, val loss 0.849, val acc 0.776\n", + "Epoch 2: train loss 0.815, train acc 0.750, val loss 0.688, val acc 0.802\n", + "Epoch 3: train loss 0.691, train acc 0.782, val loss 0.625, val acc 0.823\n", + "Epoch 4: train loss 0.645, train acc 0.791, val loss 0.575, val acc 0.828\n", + "Epoch 5: train loss 0.593, train acc 0.806, val loss 0.558, val acc 0.802\n", + "Epoch 6: train loss 0.569, train acc 0.812, val loss 0.554, val acc 0.812\n", + "Epoch 7: train loss 0.551, train acc 0.812, val loss 0.527, val acc 0.833\n", + "Epoch 8: train loss 0.530, train acc 0.815, val loss 0.510, val acc 0.833\n", + "Epoch 9: train loss 0.508, train acc 0.825, val loss 0.503, val acc 0.844\n", + "Epoch 10: train loss 0.507, train acc 0.826, val loss 0.491, val acc 0.854\n" + ] + } + ], + "source": [ + "epochs = 10\n", + "\n", + "for epoch in range(epochs):\n", + " net = net.to(device)\n", + " train_loss, val_loss, train_acc, valid_acc = 0., 0., 0., 0.\n", + " \n", + " # Training loop: (with autograd and trainer steps, etc.)\n", + " # This loop does the training of the neural network (weights are updated)\n", + " net.train()\n", + " for i, (data, label) in enumerate(train_loader):\n", + " # zero the parameter gradients\n", + " optimizer.zero_grad()\n", + " data = data.to(device)\n", + " label = label.to(device)\n", + " output = net(data)\n", + " loss = criterion(output, label)\n", + " loss.backward()\n", + " train_acc += finetune_accuracy(output, label)\n", + " train_loss += loss\n", + " optimizer.step()\n", + " \n", + " # Validation loop:\n", + " # This loop tests the trained network on validation dataset\n", + " # No weight updates here\n", + " net.eval()\n", + " for i, (data, label) in enumerate(validation_loader):\n", + " data = data.to(device)\n", + " label = label.to(device)\n", + " output = net(data)\n", + " valid_acc += finetune_accuracy(output, label)\n", + " val_loss += criterion(output, label)\n", + " \n", + " # Take averages\n", + " train_loss /= len(train_loader)\n", + " train_acc /= len(train_loader)\n", + " val_loss /= len(validation_loader)\n", + " valid_acc /= len(validation_loader)\n", + " \n", + " print(\"Epoch %d: train loss %.3f, train acc %.3f, val loss %.3f, val acc %.3f\" % (\n", + " epoch+1, train_loss.detach().cpu().numpy(), train_acc.detach().cpu().numpy(),\n", + " val_loss.detach().cpu().numpy(), valid_acc.detach().cpu().numpy()))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "If you would like to save the trained model, no matter using it for inference or to retrain it later. You can call `save_parameters` function to save the model architecture and its weights." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "torch.save(net.state_dict(), \"my_model\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 3. Testing and Visualizations\n", + "(Go to top)\n", + "\n", + "Let's validate our model predictions. Meanwhile, we use the `show_images` function and show the sample images and its prediction together." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "def show_images(imgs, num_rows, num_cols, titles=None, scale=1.5):\n", + " \"\"\"Plot a list of images.\"\"\"\n", + " figsize = (num_cols * scale, num_rows * scale)\n", + " _, axes = d2l.plt.subplots(num_rows, num_cols, figsize=figsize)\n", + " axes = axes.flatten()\n", + " for i, (ax, img) in enumerate(zip(axes, imgs)):\n", + " ax.imshow(img.permute(1,2,0).numpy())\n", + " ax.axes.get_xaxis().set_visible(False)\n", + " ax.axes.get_yaxis().set_visible(False)\n", + " if titles:\n", + " ax.set_title(titles[i])\n", + " return axes" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "tensor([3, 0, 3, 4, 2, 3, 1, 4, 1, 4, 2, 4, 2, 2, 0, 0], device='cuda:0')\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "random_test_dataset = torchvision.datasets.ImageFolder(test_path,\n", + " transform=transform_test)\n", + "random_test_sample = torch.utils.data.DataLoader(random_test_dataset,\n", + " batch_size=2*8, shuffle=True)\n", + "\n", + "net.eval()\n", + "for data, label in random_test_sample:\n", + " show_images(data, 2, 8);\n", + " data = data.to(device)\n", + " pred = net(data)\n", + " print(pred.argmax(axis=1))\n", + " break" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "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": "3.7.6" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/notebooks/pytorch/MLA-CV-Lecture3-ResNet.ipynb b/notebooks/pytorch/MLA-CV-Lecture3-ResNet.ipynb new file mode 100644 index 0000000..c2285bb --- /dev/null +++ b/notebooks/pytorch/MLA-CV-Lecture3-ResNet.ipynb @@ -0,0 +1,309 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "![MLU Logo](../../data/MLU_Logo.png)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Machine Learning Accelerator - Computer Vision - Lecture 3\n", + "\n", + "\n", + "## Inference with Pre-trained ResNet Model\n", + "\n", + "In this notebook, we use pre-trained [ResNet](https://d2l.ai/chapter_convolutional-modern/resnet.html) with only a few lines of code.\n", + "\n", + "1. Downloading a Pretrained Model \n", + "2. Preprocessing an Image\n", + "3. Using ResNet50 for Inference\n", + " " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "First of all, we install torch and torchvision using `pip install`." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "# ! pip install -U torch==1.6.0 # updating torch to at least v1.6\n", + "# ! pip install -q d2l==0.15.0" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's import the necessary libraries:" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "%matplotlib inline\n", + "import matplotlib.pyplot as plt\n", + "from PIL import Image\n", + "from matplotlib.image import imread\n", + "import torch\n", + "import torchvision\n", + "from torchvision import transforms" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 1. Downloading a Pretrained Model\n", + "(Go to top)\n", + "\n", + "With torchvision, we will start with a ResNet 50 neural net trained on ImageNet dataset as our base model. By specifying\n", + "`pretrained=True`, it will automatically download the model from the model\n", + "zoo if necessary. For more pretrained models, please refer to [torchvision Model Zoo](https://pytorch.org/docs/stable/torchvision/models.html)." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Downloading: \"https://download.pytorch.org/models/resnet50-19c8e357.pth\" to /home/ubuntu/.cache/torch/hub/checkpoints/resnet50-19c8e357.pth\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "34657540d77e46efbd59b324c09da426", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "HBox(children=(FloatProgress(value=0.0, max=102502400.0), HTML(value='')))" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n" + ] + } + ], + "source": [ + "net = torchvision.models.resnet50(pretrained=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 2. Pre-processing an Image\n", + "(Go to top)\n", + "\n", + "Next we read a sample image, and pre-process it with preset data transforms `transform_eval`." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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0zWRucxv6QIGukd+Pyzmse/bTrHt2OiFO7qFWgJ8A7npY2Tskbv0y1boDpSzMPjiLUEHm/jxoLmX/iQTBxCv9vzHA6hPTQAc0F9S87B49A5kstKyFhYuBT53UNbyRMdizCZdRZgCebpBSyyh4zNA1IorEbgatIjgxcivuAquTpqsXvbYnfZKYEEJgangqF1zYTjQ+i5ipMyMFjfXyW3kEXFsl1+vhlorsKhQpFiysAYvCSGGcz2Q3FXaPy55WAas+D1d9nOpk7wBxz08qCoa5CQzCSAbsAtE/Bv0pyVsAGeSav1Z0Ejg6ZPaA6kGxH8rLoc3aKQxF3c9LSA3CtC8ffko1HAdcK8cFZzQSMWNYtkdEgZZUgmKhCJ6YpYqqUsZlONOFprugu5BaADNPzxTxCSEEpps6172v9Yi/OTkobs+QS3eTS6fZ3LWJQr/FMC4qYaoVByceevvhqiepNieKTwY8cG2oD7ojW/LXc0DxaHrvm7m+IYOVH8IpQDQMWhnKltCIt+7x6xlrwk9yXejeBGYW4tm9EKuD2HK4LQLTahrBiWLznp3MARoUm2HXA00jl8uhKgrusIR0jYjBoG1jxAwc10XLF1HUHkLaIsknOc0wIYTApIPOooLkJw/81mWwkMe2Cjh2kcF8D5png+vgADksyux/jc74+LARKKyGmAc8A3Tsl2rFigPTAn6x381oSkRSjzUwLp6FoUQod/0G15KM5V1FMKZBwx7QZ0AkAVvSUBzxqUf9cG0B5scaCKV2gnmnhBhqpsEJIahc7ezZjV4/FbwyqqZh2zaappFIJimXy7iuS2HAJqJH0Ge5hBXkQZ2kOflaYEIIgQqQ3wOZTIGNXV24eYuHvvkA1kgGBY+WM+IU9/2eKI0ok5rIYU84ARD0Kz4cD/fDrV1IKC+PzyIcBaMA9XEkShBsaYOZhD+7AZ7LoA7mcb0d2Ba4GvTu8vsc6aCG67joigY2dg3Ru0v0iTV7oLhniDkF8DJDzMl+GvVfFiBpTzUcE3vgnXPPZdgaQNENLMfFK0ufSUVVaY7HsQYsHKeEpmnkszYtqRZ03R/4ZRtecOHMcSu6/argpIVAKBTajkxGBwCvUqnMD4VCBpKKPwtJsn3Hy1UcDgEzpkHDghix+DLcYpl8psjDa4axeZp1+8T5l2UIDjx/sqc87ggalgS9k10kO+BG/y8Gcoe6qbKGY7vFlpwGY9xAQ5MPMZgdg5viKHfehafsRtHBmASZA5B/Dix3FLwhHt4l4cNB/+MAW/aIxpDJwnXhW+Cj/a/WrTitMdCbJxGLyjNQGtAcB7fkYQ/ahHWdYXcYd9jFdV3Knkc8PgvNMHBdFy+bQ7Mt1FTKb29/+oRrx0sTuKRSqRzMqfoYsLZSqdwZCoU+5n//x6NtXBqG+x8qs623F13XmdeapKXzKjQzwc9WLMfe96txOs3xRyPwd9OE7rNlD7RMggvaIRGrw1iWAOv3Umy4gaoLwG+kijaEuAAHqjvUgwqmTeAWaOxYSnHtt1i/tVrO8CFgXj9cgpRaD5gNeX9L0z8cI/Do53dyaXwhvHPjqbwNrwvs2p6nmM1gGhqaoTDD0BnWwHFKNGgaiqqQSCRwXZdhd5iW1nnkCwW0sE40GqPs2KjDFtV+lKcHTpU5cA3w5/7/3wF+zcsIge25nXz8A59kyLZoXdBBd9cGBu0CpmFgJlqxn+piojoAbwHYAy2z6+g0Ryk50D4LuCoKrRoU6kT9PziPICg7aAbpxYNABNzdkO+BZCfgwcw2oMi3N4sAsJACZSAD/3BaUwlYA8xBggUxoLAHMp97glTHu+GcWj2Cl0M+swl0Q8K0roJT9tDCBtF4HE3TaNBUPLdMbNYsABQ9jK5p2KVBFB2i8VmAAi+WYMobSwhUgDV+YZBv+qXEm/wORVQqlf5QKPSSOxIKhW4Dbgu+l/v/HwA967/jL5lMuPk6EokkUoXniXE41fFBEnhnPZgGoEr8Pm4qxBckpZRY1IFZrk/0d6AjD+6ojOKgi7kLeGF4+nkZsXWXinsg83vQMnCOH1KIRZhxBqT3Hd+5lRCrI+d/bwaKW8H5ix/S3n0DsGQc78TrC7pjYbkalgtmk4HjOvTZeRQVLmhNYVmW9LNMJca2GbRtcukcF4U7CYVNKGvgqjDltbuOE8V4CIELK5VK0R/ovwyFQseVgnfsvgP7KfX9kHTfuRzZ5fbaIGg/mBuBqAq7+sTkV9gP5QFQVGjSIerzy0su2KOHmogakDrXtx3LkgdwnlVttZTtgXNaZN3tObYcpwA4GDbCVZiHFDNjM7Tf80m4oyYEjobenl7cYRev7KFpGq7rglfGGXZRFBXTlLlM13WisRhbe3qxLAs9rGOaJhXbxi0UaHRd/9meHjkGJy0EKpVK0f9rhUKh/0Lq6wyEQqGZvhYwk0OrZ5wgJpYjMOhEFAc29olAmAHkMxBr2i2u0JYWCKtSQyDlispgZYH9ciccZOBTFhpxYTeQF9dAGTA8GMmBV2Qgnx6rQdSKzOzrOXoT1YNhIz4DkNBX73d/R8sd43EXXofod8jlsgzaNroexnVd9LBONB7Hj8kwJ5UaEw59eWltozVozGgyKXsemzd1o2kacxQV9ak0nH96dKI+2Q5EU4A6vyHpFKRJ8D8DPwf+ErjT//uzkz3RiYIwos2vR8ZyFD9hUPW/RBE63xQTaIFpBkzbDuEGMPNCCnL3gzEdrCLoqkwYz+WrVURSWbAM0EyaDIU4UkIkh9CI7zj/zViOw8PP/Z6fHON8XX+7YSC10acZ1/ASlLu7cEqS8GUYEUqOg9lkkkgmicejeJ6Hruu4rivsQSAai9I8S+pMbnsmgx7WaWmbx0CxSBMJRKJP/CjBydYTaAL+JxQKPYUU3l5VqVQeQQb/ZaFQ6PfAZf73CY2jUTyCxgqNyOwfDPoo0vpcRySpZyOeuNapviq4wF8QBcJiJmiGtCdzkdlfUSQbyEUalmTwq47thp7VYGdh8QI+en0dSUTr0ID5s2K851Of5cEf/ReVr3+LBy5521Gvy0ZCiL1IW3d++e5XeIde39jw+DoAnH0Oti0hW6/sMlwqMWgP0qBpGLEYjuOIb8Ark8vmcEolhn2tYU4qRdkdRmvQwPM4HQQA1KoNHxFNHBK0I4TY1hZwKyLfgzYhC4ELzoNQB3AF0FEHsU6ouw5xIZaoNiPNwJ68DHyvBHhSKsj1oHsvfBDK/aBeDrwXSE6F1FWgLaD06RX8+b/+mhwiVq4FPnTLX2K8/TrovBicEpkHV/HxD3+QFUeJpCSBD86Gv/mfD8LMWm7BGF60+PLb3k13VxeapqGgEotGWXhxB6apYTQZxBMJKp6H63p4rksun6VYsNB1DatokUglUBXxAcxJJUFVcMpgXHvDa3xxVdSqDZ8ABpABsxhYSnW2B9HYr5wNlzf7TreZEEri04EBa1QI/QQU0ib89B/5eBFQTVAMoQEOquBoYEG6X+ym/BqEW1DYC9kVYK8j/PYot8yuqvcW4KoapFr9vocxUh+6jXffcXSacA7JPmT52vG9Yac7BrLYBRvnwBCm1oSu6ETCBrgwaLmUyxplV8WxPYadMo4DVsFBQSeXsSgOlNi8KYPrQEtbJ2pTAkXR0VtPDwrxhKANT0RowLtnwI93CRtwXj0oI1LvLxaHBlcaEDUtQJL9Df+TOhumJKiWDyshQsAAcmIWKIqoE2YMigOgaGMBEAtR3ZtX+MJFGQJjO0RTfOD2s+n9xx30Ae3T6oiZ+ksc0Nd9/WP8S/c6/mnzL15yTRXgsX64cvXvaPqHB6Fu2XjfttMTs5MMWhZ9QNS2icfjOCWXbT0ZIrMMFE0jl8nilYU96JQcP5tQYdh1yWVzRAwDXZMogaKqxDraUM+LHvPQEwE1IXAEhBBSztZdQrwpAozAvLmQioFVgLlxaGpFXPZBSYB4nV9a/GCGdBAYcWDUkgIJjgMxE9xhSCTBLYO5AxPxQ29AuAfXfA6MqwDvSWjxCLXA9QuhkIHOjjgYLtg9cI7OwV6NT/6fj/H9y39xxHrEa4B5a+BDX7wTPloTAgIT3TAo7NvBYwd2knhuJ9H6s1BQcJ6yUeslb8DzPCxrAF0Po6gKsWiMaDJGoVBEURWKhSLr1q0jrOu4uCT1Njhv4ucR1ITAEVBB+DtBaO6a2XD10jpwR2k0gJsmCxegx689oAGz6kCLgKrhZwwg0/tY8B960mBEwDTBLopGEDwBB2LnwUXPSmhlM9C3Cy77NnQMA52/g7bpXHrFVO7buJd1Dz/PNfbXCJMHIw/ndDDW3/CyNh689a+4/b5vcXCdpIDjYAG9332Slo+6nC6x7FMN23UYREy/DJAb2UkzsAuwRuDKYrV2hWUN+CQ2GHZd5rW1sbW3h1Ubf02YOubNnUfeynOlqZE0EzBtYguCmk/gKAh6rc+thys7oNGI0JhshKY6wAN7N7RNho6zYcG5oOhS5mtKCokjBK+TH1R8zufwaZp8dJ2xJgOOAy6ULBEfVyORh9lAYhoiKGwgvxsMheZJ4pTs2wis+29Y+3UYWY44EsQf0fKNv+en//jXh/inS1TzlwpZ4Of/ckru3emHMg/vel6yWf1PFhHEc5jMPMAreyiKSiKR5IIOif9HY1EihoHnlVm1awfbgBnTTCEWGSa5ngyr7rqL7MMrXqsLOy7UNIGjYAj4HtA5AonvwwULdxNbMhViTZDSQVfAKYLlq/apBNRDtWdQUBTMlk9Yh2G/2YiqQr0BqiuMQteFHrD2SDw/qCSk4Tvygj4lWSC7m20HxEyJTkJyNLueFr/B1Q5iyMSgzqXpszfwxECBN3/7v8euK+2foTkC8+6+k6ar//WU3sfTAi/mKRz0NU5w/ycTmWHSYprY9iDxeJy5rS2YpkkulyMej7Ohq4ttGTG8Lj9jOu0L2jFNkwZd4+ffexCr7PEBI0ZylAk75daEwDHgIRZ+70aIaHtp9Bww4oAGmg4tKfD8JiIjLtR7yKgtInNKGBj2+QAuWJbs9UwDKMl3xwWnOktHEF9jAxDWEKsiD3SJP2CLv556AOY+BU1FIDsEhW/AHVngZiAO9RHaPnUbf7f8v/nKSPWahuXIbFwzyjVjaY1vXBS6Noz93wS0zzib5ngcQw/jOSUu67yYYrGAqiii+vf0igNQUVAUlWg0xq3RGNcuuw7HcdA0jXQ6TaZvB5dedCEtqRSUbCGOTUDUhMAx0EU18W9wHVzOKMbA89DRCDEN2lqlIhCexP7rG/DZQMjI1eV7oVfWiyeh7MCIIk5EVYPMTohDrBnoE5MgVI84JloRhWIAsr2wfp/8HkQRFMDZBWbaFxjqrySueeZSIAmz4cb3/wVf+fJPD7muLYh4uubhL8KVx9d27fUKNzPArTPORVM0dF1nTmsLnusSjc9CMyK0tHUQdyQiYMSTzNFMzFiMbdksiUUGLa2taJpsu6Gri7xlsXnAZt78C0m0dbAl3YNplyg5DvPa22k8/8il9I4bL/TAmSlE4wxSUwPmyomjJgSOA2NzpYbcZw1R6WMxoQPX+eFAVYERB1Q/OuAOiP1f1wTJGGSeh2wGDL84YFPUJwv5B4pDtE++NgYdywLKQRjiCdjwVFU7sajSlxN74JKHgV4Y/PSvaL/9V/CZTwA3MWdWhEaqydg9cihSACuWv+GFQDLVxq23/Q2KqtK3PU/zrDiqotDS1kbFiBFqjRPGbyg1pdpYquWytpfQsDsXpqC/TKKlDcfKUiwU6e3JELUdtvb0kuvJcNNdJ3O/M37lIsldoD8PM+PwYs6nqsdPeI81IXAMNCKDzgOcoHR4x5/Ckjax6/EzAbGgLg+OJR6+4RK8qQ1eKIrqf05CogJdT4hpEDYgm4NYVEKL2VFQZEC7AIovCNJQdkVhsP3nHhwxaE6SQ15MF5jfJ23Myt+Ejo5H4LLrCethmjm0hWkeMViym3bUOhXEk0RbHXTTpO2GpSKkNQVQCJW8E08LnqnSduNiYDFDv82wtaeH3p5eZsQTJFrbXvl5jnT7JelADBcdZvpNL6d4MFKC+hOP+NSEwDEwRPUmlYFIpFF8AQULZgfthwMx4cr9PzMOoyUYdSUkOGKJwKjXIHkuOCWI6GDZ4hPQFDD2Q/hMh9QAACAASURBVBzCs6GYl5let6QTcdCtMCg/0ut/ug86z0EkY/B9Z0DHEmRCyBfhq3eQeXwQk0OFQKP//aGn4B/f6H6BZJzYeUeYQf+QJ2+Vic88DjG5B7GvDnP+Nb4lRftbUrT/YYlUjbJtGMF3Ip8AXlwtWqW8hRyq+huA5u/Tkv9fss7RURMCx0CQNThnEuhN4Glh1LAJsxcx5vnHgZE02AX8umFQciAchSlhGfzPpmWGMSJQLos5oGvgqf7ynbAJyh4oJhQt+PFI1e4HaU3WzZGrK1SQQb1xH1yalZUyK3fy/a072YaEuw7GEFVHJD+/E65+A+cSuIBSBmeQAd/TH1QYztllyppKIhEXP80IMrbqOHQwH2u8Bf0LX5FzcB1MKcukQufLrGcjLt+CnJujwZnH1jxqQuAY8NN8WHMA4jaof9wGqRR+HyAk6OaCU5APnh89KEM+L+vWKTLQ89shPgtmp2BP0VceyuBWSUNKgygaxX64Dw4JXR0L65Ft1m8GZbOo/A8h0YDDU4qSSFpxHGDTaiEnvBHxVIGKbeG4Drl8FqtQwIzFMAwDq1Bg0IE+FwazeRRFQUWlIdyAroVpMk0R5MEEXSpLHQmFE5/pXw4v9oi9X3ekAZ2Bp9LIe9cgDSryvaJtKkm4Mom8q0dHTQgcB4aASydBxATKChhNyJP300X7V0JuBxjCKsSxQfMg70ImI9RgrUEEgG3DmVGYZkpIEQdcv6pQWPIFIrbQe0+0v1IZGfSXUu10GFRAPhwu1chjOff0aZL0Oo54sUz6mz/C88oUC3kaXBdN0zA0DTdbwA47UCoRCTehOC4KKo5ts6G7G13XaW9ro+niTsqOg6IohBSVilcml86hh3UMM4Z63ssPvuM7z3uhkPfpx4FZ4kD/Jnm3bBvCYSoDBUKeJT0tVBsoS57K/6yUEvYvg5oQOA60AREDrlzyp+BZ0LUa/iyBGArbpU6Au0MKioaB7G6ZYmNnSXKQ4whF2IiIMPhDHs6JAYqQhYLU8whQgo1paUT6ShDY/ruozv5HSiwODukCvY+PEY5f33ghC6hwZpyBlT8Bz8axbax8lmYzjKJAb08aXdNpX9CGq6g4TpaWeCflcpltBYuL2lLEzDiO49L1yGo2p9Momsrliy5FN8IM2wVUT8fBBdckohsMDloYLfFX4Af4ngx0RYF+B2bmkRerxNCqR/HcEkrZw3MyWIUijptH9wok2S8kE2OQjKGROlVCIBQKzUF6CwSYDXwaqaR/K/IeAnyiUqm80nd6QiAOXNAymaakBnEFEjq8kJFGoE5B1H4byUGOUe06rllCJx50ocEQe3CKBgPD8HReogVoYPsxwrD4BDaMVJ2BJ4Ig8el4EKFa98Y7ieJvpwU2rgbDoNyTwbIGiC1bRl9PN45Tkuw/3cEpD6ISQTc9DAMaTA9dU8BVMZJgb7eYkzJoiqUYssGysuSeyTCYzzO3JYWVz+FYUCzkURQwm0xcF0zTRFUUIq5FSPc1A91nk3rI5H5EJuE6KBQk3Rwd7AGwNsH5cSBG46VLYfVKStkMxXSabT2/h7ap6Oxnw2MQmwbzl5bQtWPX53zFQqBSqWxDUuoJhUKTkPfvv4D3AP9WqVS++Er3PdFwy0XTuejiNnHmuWUYdCT8N9INtiV5/2VE9w4erAI4o6Ds9fkcO8GIwrRZ0ORCxhZVTlHFrjT3Q0F8hgpCCMqe4HmeSGWWHFKA1AHyB6C9vxtmtp/gESc4Rmzo7qaU6cEtuazp2kAqlSLW04PjlJjb2sqgbRM1YziujW05xJMJ3JKLikZEN4jEYuB6lF3QwzolewDXVTFNncwzLhFTI5fLYA3mKRYKaJqYicXtBaKz4sxrayMai1EoFlEVhbInRUw9JYKiasxzWwmd70cm9hSqyUa/XQloYEYhnYf2Tpi9RCYfIyb57B2dOOk0vdkcuT0w3L2XGYrMvuoecDzozefHeA1Hw3iZA4uAXKVSyYdCoWOufLrBdR3CiSRoJbg46VN+M1CfAnMA1u2olg5wET9ME37R0DqZbu1R0RgA1LBoAa4LaoM4DS0HzFE8129J4O/GZowWcsJoRB5wYP8fjMAcKOOXJ+96HK59nQiB0TI8/iirvvtd5sSa0NUyRdshrOvMX9AOrsvc1lZM0xyrC6Bj4Do2+WyRQdumtzeDrhvscm0UTVhipmniOGXKHrS0tKJqLnHD5Ct3fYnhA6OYZ5xNbt8OopPO4tEDO7nWcdB1HWvAolgs4JUlYyxiRFB0g8SsGE5UI6yVwMrDn10s5//sctju88g3dVFxNEJ6DnLfg0S7lJ0zJCzoKhpBuxN3H+j11WK4luNhtqeOebvGSwhcDyw/6PvfhkKhm5HI1IdfrgXZREcj0L15P9cDLFjgp+0HjQOyMrO39ELv/upIC0K1KjDg+wmMyVJ3zh4UD/LB2YSKBvkB8IYIN8kM3efvquDv5kS1ghhCBqpw5OhVkDEX8J+G0mkarz3Bg0xQ5O++C6tQhOFBVDR0w8DKZpi3YBGhlhZKXRtoSqUobZfZO5PJoaLhlFy+v/HXzJnUiHdgGJdRcojAVBChHJ/RyJVXLcWysqxZvQpN01AVgEbS+6Tgi3vAogys73+e3EPPc9H8C8eSjHQ9jKoqzGlJMqi5bF5XYL6TIvzOm+UIz66GdDd2Tw9G2wIGshaDtodpu+RtD8/YTnM8xpZ0F2rZITErTvuSq0hYFgZQzD5BNA/NCTBTKcxFi455v8ajF+FkJMD0cX/RPcDnkPfsc8CXkIp5h293SPORiQgVuHYamCqUMhnCF6dEhTcsqEsBFvxhg8zq0R3ijClSpQEHSYRlRAAY/sAPoGk+FdERZ4ALQyUw62HuiBw/gQiCZuDREzh33f8EzPLD6yZCtUFJO9BoNJ3A3icwnkqTeTIN6jAt7QkUFRoTBsaAia5LurfjlGB7nsfWriWXy2LbDsW8S37fDtLAlgPiSm1GBGUIeQ6p5rO5bHEnYSNMqWTzgY+8F88rUy5rbO7q5f6HfooCZBkF5Lm5gJnJ4JU9ciN7MfcNMm/uPKIxDU1zmZOMEX7n++Xc96SFVp4vgKeSX5emN51h/fqdRM4AI/5HRFpNent6mZNKooVjZApZBh2XaDROZvU6Nj8nod9E61mohom6cOkxb9l4aAJvA56sVCoDAMFfgFAodB+w8kgbHbv5yGuPi4BLOs7iPYsWg+nfqrIncf9gdHc9LTN/CzKlGvjdhaiOOg1JJbZt8fTGfFNA8dOOPU9Yg/p+NA2iTaD0SXzfRWTLLk4M26j6CEK81NEYZozjSB4oZDLHtB0nLF7IS4XnF2TYxVtj2K5FfOkinxFl0950BZjt4Dhsy2SIGAb2oE33pifZNiKyO3hcgfY07H/XAZM6rr1uGc2zIrjlYRo0l7YlnZQyWQZtDyeV4KK5F9K79TeHnJoNbNi3m85pZxEdcTFnmHzwI/9A8pZLqWYhFKh84ZNsWLmChGHStGQRhqbT+8ijgII5DRLJP6FU1tDQSLS209KWoqy4tLSlePiRlfx41QqsviFs5FVsSrWQLx9fo/TxEAI3cJApEDQd8b++nWofzdMO7bMnc2lHB1zcAQkTVEckdS4jdpniyFN+hmqqYZwqcSsg9JcRmrCuyOAv2YDiG+VlGeWeBzaEVJgTh7gOxQyUDlTLCZwIKof9f3iY0EOapvQhiUTrH18tJs/phqczoLhwpgaFPEPZnHQFMnURtvUGQ9ZKGiNJiJkMfPd7FKwiG7rWoWs6fSOH9nQMZn0FkR8JJqMAH7jj/eiqwobH13H9DUuxBl0ol8hlehm0y/TlpRR5hJc+qzsueRuf+PjHaIyZ8KbDbPT+tXx54VtpjkwGZz/D9l6ctTpbC0UyPTsxzEZcJtNgGFxzw23Quggog66hJg0K332ATCZH1Ixh9v0eF2ieCRgm8QXHNgXg5JuPNCJ9BW4/aPEXQqHQPOTd237Yb6cNDOCdi9qJL+uUEVmPOJxAnvLaHQxloXEB8rbkkQEfNKxxkatv8tenKBwBz4PBIJA3LLUEbBus/bKPEjSWfNqAAYoN0QNCADIRMtB4qE1DiHCZg5/s7IyOw15fAzSUIZeHTJrMyrU47jBRUydpxqQDVH+GYleOaBwaZ7fx2Ka1/PKRX5DZI5sXD9tdFHkMJpCaNpWo6jInEWdeU5ktPZsoptO4HW1Ewzo4oLsumq4RvbiFBlNnTi7J1uwA89ovRgmbfOD2mwi/6Qg61lPfo9K1iX/74tdYuPitdHzkU5DJ8ugjq/nBipVETQM9/ibmL7qUtkVLINUmLMBWE0qQ7eoht2I1fZu6uCrVxprl9+IioV/dhHzPOuJ3fPW4buFJCYFKpTIETD9s2U2vdH9xppJn78mc0rjhxmnQtsAAJw31bdBfgCYD1q2A7TtBh8YksJZqC+AsMvhj+La+vzMNYRLaQ34R4t2gTAbdzyVwStXpIwwYQvqK6bLvxADMycH6A1KE9ESZhEeDQ7UrmnuMdScsBh0yj68jl95E1Gyi7LmYTQlp8vKiS2ltF9syWXJZm3hR5SP3/PeYw/RIuGDaVHJ79hKZBHNSCeY1aeiaSt/2HHgenjvMlu5urrrhBjj/CpL2oBwr2ULK9SR57KhZfGXsf7qGBsoMeioPrVzLo8/BvA6PR794F1+976fo0+qY19rCtTfcgLGgA9raoS7YnwkjUHEcoskkxXye9z/0LeYBt85oZMa0IUwDEq1nQ1Oc4zMGJghjsA6YzXSy7H6tTwWQyfyjH3kzXGpA2EE4eBY8shyyO8mugGQb4lFToLQawp3+hnnGsjzxSYGogDFVfACOI47EMuA6kknoulVzIignpjNWg1A1REXd9uz4DNagtoBKtXxhYsH0l91momIg3c3DK1cSDevEO+JEDJ1QPA6eh/3gCv71i3ezJbMbTZsMrDuiAA2Gig3gOcyfOZVBx8EMa5jRJjLpNH35PPPa5mHbo0SjUXmWe7aD6teKHLBgVpKXCIAXCwz8+500xQyyj6+kmNlCPBaloJps2bqfzUCuYNPd24U7Cf5i6XWEDYM8YLyl/aX7q4fQOTrDT9nksjk+ev5bWfPUr9iya4hLpoGm1+FpBrHbPnLc93CCCIGJxS344rWNxJa1SntxStCflbS+omgAyU4obfIH/sV+RZ8M1bdpEzKIFyEzO0hWoRGR/2MxqDPEoZXPSC2BIBs5YO8F+/Krg7rD1a7mx+sfCHHkGU/xfxtCHI4GYKYWHOdeJxCeS/PjH36XXC7P/Ouuw/FcjFiCbDrN5nSGR7vTPNS3W+7Xvv3AzpfsIowI2CJyHyKaMCsURvnZmt/gnDcVXdMoe6Nsy2TQNNiS3oKmaaQMg+ymDMm2Fgqb1hG7OXlQ7YEyPHQvP7vrS3x7/fN88JKzGczvQAF+tHEHj7GDKPC+2VNxUWjvXMQdF1+KV4Z5izpQj9HM1HVdNndvwrWKfObyt7FhzS/YugeirRFii5bAzGNHBQJMCCHgUSE3QbSAW4Hrbl4grL+yJenBxcOqeSRBC5qHGoj7cy1SiyyF34kIYXAEday1UXB2+47CbmidJ/sMPFC6v++g+UCQe+JrEo3A5WXI9UO8HvIj8LFjXMvRVN4S1VKonn/4tvf+/XHdnwmB51ZD0WYgk0EB5nd00HnFEro3rSD/yCryRZthRWdrIACOgibEJxvIXbHc94MHW/eJXL/IK9OgGYT1RhRFRVGlO7HjOGC7JK+4DhIxvO4M5f9agRqLiN9nwCK94kHyGemqvbl7B7l98niDisZ3XP9WrrnhZvK2TXzxUqkQdDBetOl+vJt5HYtRwuCVyri5DC4asYVJvvaNb7Bl5XJ4ZhMts6fjPLebsN4EbS+XbvxSTAghAHIiZTikDNarjauAOz85HTqjUOeI085VpYtwwASMAT2gtsBQDzTG/OUdVPuVhZGR1eDvWEPetIBMVBgF+0kI1/k040YY8K/a5tAR6vnbKcJW7gOu1+D6r03lH5/cy4X3cEhvgeNFmbGqZTQDnLf4FezlNcBoN/bqFWzZ1IPjwB23305IiZDNZOju7UY3dC5YsoS+gkvDii44cOS3qZHq4J9DlRD08C7RDHqR+7LtuSFc53l27YLE7EY6Oy9klzVAyXFo/8ISqNfhRZdH1z6KtT2DroKhK6zZuGOMp9ENpPdJ6G49cNkkuNyoQ9ENMtntJNoC77KPP1hUrAEGS8PonobiQmgaqNNU1FSK3CPruP9r3yNqGMQpcenFF9N2cSv2g/dKb8rzTlMhUKaqObdyFj1HUN1OJVqBa88DIwm4eSHylKg2EypTLS6gAU1Q7IWkX2OU7VTDgxJYrhrdQT+SoLRPkIWsR+T/wm5h7gxQbV/YgLxBJX+fFmzwZydrD8TLe+FTb+M3f6WR/uuf8ubDq4YcJ0rABTNf2bavPvLY/34n99z9UxLJc5nT0kbIjFDKD9KXz3P54qso2kWG3TKuW+bRIwiAOBL/V6hONhpy+wNWpodoUTGEoKXtEmds63NDlL1uXNfj0kWdUh/CDUMhj64bKIbJtvTv8MIwpx4eG6mWnE0Bq/z9uwcgsmuU3H3/ySD/ybzz38T8jsUULRsPhVuWLSW+eDEGKobtwsHPZ4pK27WLaLt2EfwB1j1wF71dXczWyxgdF8D73s+JlhebEJXQJ1HNuZkDzG3WaZ/x6jmqYsDn5sJ7bgeSYRi0JOxkFaBUAKZWw34OYzxcM+g5Gvd3YiAdyYNipEHzAOrAmC7rBb8pdaJllBx5S0qMRQbGBEfA93WBAZmdPHziRRbpW/CWFtq6/53KlnfxyFxponoiUIG/+93EMMWOiRfSfPXun9L7HCRa2mlbchWZ9Do2Zx8H0yN5wzIu6riU/PYCP/uvasOPv1n4LkbXb+L3//ev+NBFZ3FL82SumTZ57F5t49CyGwFpKI/I38CZ2AN8u28/0IA14PK9z9/JTx68F2ZFuO7++7jp3+7nQ5/6PHNa3objTWYDsM7fNtA6wsij60aEQhfwo6eeZs0Dy1EKFnNUDaeQh9wWyced/TID2oTO99/G7KVL2egArRfDY2nse+49ods6ITQBMzKDD1xzC5Zl0ZJM0FvI4eULdO/61aty/CuBuW1AbKqQd0pIt1HPkaxBwlDeW+0YpACG1Aod0wyCis8Kok/m8b38jaCF/XbkQGKq9CO0h8DZWe1S5sg+x4REoAX4psFAXkyBgA4sxm7Q5kyD89/P4t6PsHh0LXzwf/PZr8BnOTan4O6F+AlRExwvdjPw4ApKDtx441uZ09oCYR3Hc5nXsQCjowOmtZBbvpbPfvmHdAPXN5/NnV+9n/jVMtyTcZv3GU388pEuXBeasyW2PPskl9NIhiEMZKCrCC/jSDTtKLBl1xAx26G3dxXbdu3lunfdJOngC9sxjChxp0zRdVE2/npsOxuZ6ILHGqATCfsOjlh4rku5ZNPS1ioRpD2OlJk6Wh2CeqBeo/HKxVy6oAUef5SvvPPd/N39PzihWzshNIGpUw3mX7GMC97+Xpo7l+ISY0vPq5Pkvhi4/hJIppABr2qMTcWKLgPYO4KsjFClCptIezINsfPBDw9Olu0NQyIC8bOkh6HuHTrjB0WLg+ko0DgCB2EYtu2RxYHcoQtIdzPGMMKTg9a9H+7awWcq/8nofW/mgWli6gRoRV7yJPAh4NYNE5Kx/RJkP//vPPbd1XR2/ilX3bBM7kHZo33pMowrl8K0BPy2l/vv+slYAdbvfvueMQHA06tZ97m7+MbXHqBQKNAcTxCfZdAMzD+/lTjCoAR5FI9y5FqO3cBGpB+h5gqjcNXnvigZoi+W4bwYqY+9n812gQTiZwrcfVlemr9xwXlv5nPz30qRUcwmk+tvvhnbdslvypBe8Qj5tdVyspV+YATyv+zC/uU6eLoHnuqGF/L87IN/x99fdzMuHlx9YvrghNAEtPoGOha3j3XzLuaLbO3ugYJG9sATp+y4rcAtM2Fh21RYEPOr/xi+Q8+PyA87MsaU6aDtrvYCCMyCIH04aECSHwWzDpoiMOCKALFLEkeMRaE0KFmDhf2iAQS6pup/And14KlSAbuaThy8qPkcxDP7odOC+oDuE9Q9jAHL4H3LeM/7CrznDw/Av9/Lqi/voBeY13w2i+/5OVx5mtQT2riSx5avpjkW49IrlsKsBBGvLH6bWFBr36b7hyu5r+9pAB688R2olywBbHh6Ffd9/m5+9P0nGAYi7ETTTIqFIhdNm8qcP45RzIO3R9wxGlXZHAzaIO0axJfw7f7dY3Lbsx0qhQK5B9Ikb1oGYZ27f/VzSo938fGPfpJV/Uf2bxnAo88+ya3X/yXfWfY1NE0jZxVQdINk5yL0/HYatDDl3xb40YM/4f5v3g1ung0j+0kBV59/Lnfc/h5iS5fw2e//FBt45KaXryJ0JEwIIfBC8Xkyyx8kdfsymALX3bAYrdBDby7Hv/2smwHGn9IaA66th4sWQeOyRdAWF+aeBiieFAvRFKnYWnaFvudqkN0hb0R4ujj0ZgGzpovw0A15Ma0C2I4/kMO+cLOllNiwBXlfAATMH7++ICCOyIBp6Dcvwj7UxwhQPoDorr1peEsKmWc0qgkMB13pOZ+CL32Kq75U4KrTKE2o8lQXIdVhYPlyioXdzO9cghoxYHseq1CkyQzUKeDpdXz/gbuxgfcwmeu+8VUgT/b+L9I3kCfT08M15/8JRavA3FQru2yLLc8+zy3XvxVwmRM/i2bTpfDs3rHsTYtqEujhGPA/KtCd7qasKRDRiLakKNoWyWtvIHxjiotWrePrP/rOIdsGLqSG+qkUR/by6OPruPK661BUlaaWFjJdaZb82WWs6pcJsBGZGw4Od/YA83PPE1E9yKXREVMl9al/OuH7PCGEQHlfiXf8zTuY8zdw+flvYv6CFE62B9V2MBl9iQp1smgDrpkJt9wwldiSdlh4AWD4fQQ1wPbruQHhEtg5P4U4LHUDCyWh8bUaUPg96DFx8Entaki0gm7D9jxkB2BWGMolYFjow1DlHAThRI1qNQgYKyWPDXh+KiuiCTQD0Xr/cO5expwUlKj6uI/Uieb0EQDpb69m24Of4fpFbRQLOS5Z9g6MN3dSyOdwHRvN0KnYNt7j61Bn5aiUHC5f1MrgQ7/mm1//JynOaW0n15Onzypw683/QMSL45VLuO4glr2dMjbg4pVt4jED1y6RYq80bEVs9cDVc7R3UAfu3/U88372PJFJjWxJp/n/qXv/+Djq6977vWLG1sjMYg0wC7vBa9gNXkBKvInlgFJMwSlOA0nIAzRJn6RN0vxsmt42zdObprdPkie3r9z2tk1y26RtektSkhRaSAsNoYXGkNhPDNguMkjAGnaNx3jX1hjP2jugkTVj7f3jfL87K5CNHWjrfF8vvaQdzc7P7znfcz7nc87Z/sgOvvDfm4xdex1BDOtYwibmAFjHAJdfehX5SplwtsPf3Xor9+57msLoKJSL1G67jT/9yq2AzXsv/HnWjFawooDa1AT/e8+jvby0q4BPfuDnGbpuAzNf/WPawAff/hpVmUgXjjuxcUoogUJlmG0/t5Tt/7IfwsfxtzyObcAKEz5xCQxasKsJt+5b2EBjsZFD2nofII2ygbjvw8DbrwQskdvix6+DC4qIh2yTNqXWjn4oK3i5At6ksPuCg2AMqfZvDhSGxN/XqcFhCM1pWFmE9RWobZVsQ8eBWKWrqIZFPTAQxC2ok1Ylckh5BjbCZT8qHxOgfQSGeqR/zTNWvgPNvnv46Ry/8P43cz3wrrUuqypF9uSq7IhCVsURU5MTOK7Lzt0NLqmuJreyQMYZ5pqPXMPqio1Bm3jjbeyoeTy4cRsNz6f1cEI+W+T6d1+Hm8thZw2sLNi2yb133EHJsLls7Vrue3AvhdNhzdirCe5/ChcYXjrA3Ufme8lG/SiKXp2ngObRGQqPPMzlwHe/8nW2TTRwnAKf+NinWb1pEzsem2DkwhEsy+HtN15Hw/N4cOs21rgu002PPVs2EScxKyoVHpjYyPf3PcS3nvxnCV9fcDFvBwaXQqV8Bm/bsI7yOzZAp8mOrT9iBfDeD3wADnmAkZYpO4GR6Xb/84GhNWvO7W7f+l7J1Q87ELREoCw7Dc31zGsHrKKgp+cWpGx3kkjM9uI3I6KuA/GaGa9adj+5Q/nZsVT0Wa7TOjXZP1Tf0WEA3QhQE3bbyJoQwb6GhBJ318T/H6moaEIoVgORkI30cTohmo224NC6Zn2I0I19hHjkqG27gY0w8aR81L3vhpdCZlTt+5Ez4eIbgfeqDT/d45eMDLWj8I8fexPDlgVZl8emQ/78lrsgsiiViwy7Fu/6xetw1o1CUSo9xbfdzkc++CW+ceTYx84BX3zPb9LYsgVCn6JtceXaEdodnyAMaDWnsJ1B/OYMl42fz84E7t74NOFzIugnW+FJjzHgmtdezPW/eA0XlMoMrRyDKIFSCYD/9rNX8vtPPrrodyvAX1//VgoFi7NHC4RRh+/cfAu+P8Ote2Tp2v+/PoplO+ys72bslz6yKGEok8n8W7fbXfPC7aeEJQCGZEotNaRbj2VJ5x69FB6aBUsV+SxWYKlO1MgLKLbUUXnaGq6HtLIHalsE1rBs85tSyvnnyqS13LUDbpM279ad6kNSgzAn+52rSk5Ekeo8ZErRUEPVDCABW4UGNfUvVJeiepT0eASaRzqF2HraktPAoClXooiDctoEhmK1cfIglCZg6Tgy3X76uwh84e1vpPCOG+n+6Edsn5xiW92nfWgGe6lJqVLh8vVrccbGxAqb2ArJLHdurB1XAYC8xQ98+0t89fL/CycycI0Ef3edHVMP49gDOA7kHZOoCbYVY4VSBqKGEIp+UkZrC9jxyOPc+sjj5IGr17wBMKhUKqyqVPjfx1AAIL6+6zg0mx6DxTK/8Ftf6vEPAG6++jXkilVuMovjYQAAIABJREFUvfkvGXZzav6d+DhFlIAG/tTkXTZIujoDyxMp8mlaMKCdaS0hmo6nHWs9DNLbywJtcAtyDMOA3Q2pQ39WoW8/h7TXm3YmNBfYZmESbx7OLcG5DXhki2QDxpFYA6ZSOhOPK6ARWfUbpJwCV22fRpYXnYCUJw0fah0Wg3262vQcmGcrkzRL2jzAa8KFEwh68NNtDXz0v3yU8Tevg2aNPZ5HrVajHcLw6UtIDIN8uUDhinXS3SmOmPFn+btbbuYDty7GKxmARYDluzffwdsvKAEJjV1PqySjeQpAbB2mOjpAu76X4ZWv4vLxc4ju3s/ly2H16Pn80+anuY+TK/TSJJ09NeC+7Q+RA1Zs/3GvYuWxRmn5ObQDqWR89x0/6imAAvCFK3+WazZcwze+fDMRbS5fvx5ee3JRnxNSAplM5ibgWsDvdrsjapuD9B1YiRitv9DtdtsZKTf8FYSDMwO8r9vtPnzcE3QTabGs6+9ZRViaV//MAjYsDegV7TviKQKF9n3bsr0nrHol1P3bAVzVsdUQ/XJRqJ58kxQw06u+putpTF4/pv7tiuVzJJbmI9aguv6s9LVLIljvwuQUbDycEvX7dZe+RN2zoKqepk4m0rpupbqjrJAEMSDzQqaioVHEJqny+ukc47/zafBqTP/jNmq1KfZ4h7FLr2J1qcJwscS6666Vku8AjTrfuW0jH15UAYAY08MIzJcqg+8zz/d3PUURuJ4ltJjDBNr7pF1EqTTP2QVw3SyWDe97j4OBwR4vYMVpYB59+fepIwxw/Mz/Ytll28QWXNdhZyPp3dXH1rwOJ+tw5x3fJ4ojVo9XKVSrnCwWdKJkoW8Cb37Btk8DG7vd7quRHDqd1PbzwKvVz4eRwqPHHxlVfVfHfpdqoXSQlU0vnRX5vdRh4TJYUz8vzBaPSbNwHHqdWjsxDBchNqVYiGbd9eh/w6QMnmzf9nLfcYeBorSKdvJSC97WS7gpoUFbE4UQxaOjd646lGYGahKAjhRoHoIG/vLAFcgCP0oq+P2GT4LaucXJFyM7xUanw9Rf38T9GzeKArAhiRLyhSKFi1bCqMJy5uFr//PrfPjbf3Ocgz2OPKzFw8we8CfMcSvwB6iOz0ehEcKBZAkWg0R+EyuJKDg2tpHg2nA9aVT3lRjHe2NuwcF2LErlApetG8FB8RPiNhgdnIJFpbKS1dURqJ489+OELIFut7spk8msfMHmtwM/q/7+G+CHwH9V22/uCuL4YCaTWf6CuoOLjAEwDQEF4wTMphSO7AlhXzyYHAv5uz7QEYBwqQ6ya78eFoZL1GuLEJPdR1qDZ31YFpGu/AkpNJ+w0DbX2IEP8wEMKCxgwFLAYKLcgggC1SuuQGpsFJaA66g05XmpTzit/q/jP9p40QlMWnFk1b4KAO49lggBIY+EymLyWTxEeAqPeXpLknfHHUxNTWFnbYL2QYadM8iWS6weH2PF+Fppzgnc/oUv8PFv/tkJHPx7wBLkmTx13D3vR9Q7PjSjOQxjCstMsCybKGgT+r682p/0Pn+C4Tc9SkbC6rUVnMjgfZtkNq4o26woZ7Ftm1K5Qmb9OumFcZLj5WACOS3Y3W53XyaT0TZIAXimb7+9attxlIASnHhWqHCGAUYBltukM10Lss7n1cKtwLylesXXfFudLKuVgQIFSRR8YEnnH9MRRt8yXVai35XQf2uOvm4ooLy4gUgUgaWu0wol6uBH4HcgUnReZ0gE383KNn+vXFpL/ZikrctWInPV6jttvw7Sl4P6Tq9oSSCtqs5tIt6Z7pz8UzL6bNLi+isolnN0J7ey+Z57aEdwtp0l8DxwcxRfG3HPn3ydGz/3/57ECeY4tgJIcQNtoteOAofg/vvnuHoplAp7JbNcBal0JuLLHQXkdetM88UazbSaHu/5wDVkxqoULYf3BC12TkxgJiHt6d3kC6Nk1o3AWT9Z85h/D2BwsTJBL4pD9vcdWHHeMojaMDsNcSBOWVCD5frydF6uNoT6J3cfM/7QRlg+SNoTTKdqJKSuQiQlw2KVG2yb0JwCAjivQ8oZ0Eu37gNkquvop/o5ELVE+M6zYCAviktHDIxEVROKZD8SsOIFuos8qZAHpDSFYVLvQ9cX0KfNkWIGg2p/c0adYyWCQE7yUxkp2FcDN4L6NHdvvAc/CNlTP0xz+14aQOHs81nx1Tv4/fv/4RU64RuQZ7U45j8BTByBoV3CPzGRV7Odn8waKKC6BaH6A5y2BNt2uMSyOOBP4xydIWRhKNIP5jFWFqE8ApZLdaRJe9Jj2DBwcg65alEVJPnJ3vXLUQLT2szPZDLnkmZL7gXO69vvVby4qOuCvgNr1pzdxVT+u2XKqmYHMO+rFaIBz3YE/FpeRaRjkBfd9LZtMD4GsYrXRwHkXPlsxbDUhOc7ktQTRRCqYgFhoHj8Hly6gbSrqFY2uh6MQWq3g8T/O1CbkjZSRiJuzXBWeAxTjwKhVBK2AX//wieujZd+OkOALOS6OEm//utPD1CnXxAFNQ3SAgQN0nXmVB8hPDsNzbYgn65Bt+UTJCb3PnaY7fQJxYGn4f6nX8FzN4GZHlbbOcZeM8C2Y/zvZM+mRwOIjs6x+pDP2dkiiW1hHJrFXb6a+qEUS4+OwOYfbeJd69bBhethrc+q2iRGkuCUXSjpSrc/2Xg5SuCfgF8G/of6fWff9l/LZDK3Imr28PHxAICjEHiwuy7En8iFbA4CWywCLREJsFxaM6cVOPqC6nGkGnwk0J5W1FFf/PO8Bc4ghLNwnqOUQEd+R5F8d2oSslm4eIzUGtChxwICPkakgKML545KWzFiaDbAn4bpVqpY4jlJKMrOpz2/bOR/NdIkwKbcYg8QhB5lmFk5Va9oic4udIZkP3+mj+CuAc5IHVy7VKfo2FeH2gQzDY/7N27jmndfB3WPf9q4hW+qUNxLjfeueA3f3vPoCZViH0XW/XTs5dcveSNGWGPHnoNkkac2sdiXX+ERIcptBfOM2AY79xykDZSyCR/iNfzVIeEOJEAYJtKv8sIQCkUK1bUyr4sunFvhZAuJ9I8TDRHegoCAZ2Uymb3AZxHh//tMJvMrSKr7jWr3u5HwYB1RoO9/yRPMJ+KfO5YAa7YpTLy7/gGsJTAyJmG4KIFKUZVg7jPJSYCOrISBr4C3RBTCtA+ODU6sWHqBdHrVrb86oQhs5Mt3n5gQTMINlNWhXRAdxNdwvg5JBmKhLB2GC7NwYQzP++DVgf1KX8wvLOznLAFzLpVV7df36xw9SqSC75NGMROgOQO5JeAqZRBFcMRX0ZO+kkSnshIIfbpNj7/721uwnZUEfsitf3sTX7r/qRNm512St3n0vZ8Bp8Q7futDvTZgi42FCmCAm973afLhVtpJjiQ+yCc+8ss8Vqsx9cQk9z4ys4CU81LjZIhE/VHiLDD12FO9ehFh1GRs7TgH7n2UB4BVFyzBdV2C6RAHA5aVoVCBJFBsyX7z8OTHiUYHjpWf+KIWJyoq8PGTu4x5uaGsKdz8mmrnbQ2IcohUbm3WEf/7rDzyGPtybo/4ss1xlDWhlkYT+RwnskqbllgH2gJoh2I5RJGcK4nEvN+6DUY9eO04EsDXzrpeypHz72tKmvDSQWmFbRqwzAE3J1/REILmJJkDcg3NubQUWT/EEZMyS6qkRQS0e6D0HYUBUS7RnCIkGQpbiGCpdl10Qmx/vbMTGQH/YYrDsYjCkCSB+zdtwU/gn7Y0Toqe++kHf8zvPPhjfvvCN/BHH/s0f/LnX2bTccTRBP72k1/k6hvfS+O2P8YOWySVEiOlEuVCgXatxn2PzLAGuOzsIbwDM3yXxbMJ+8eJKAC9DoyR2pP6TV15Otj2EizXAsPn0ssvJl+vcfWG9bg5l0FLE9cUN8VW1PmXmSNyajAGM9CLEOSGxCSPLAHvEhOaTVV7z1bgV1mBubEg9FjKDUCQ+qAlmICdVfXANRgXiZXQ8iU6oAUfUHFDlRgEcauJmajjXaj9ao9UETTluDlXzvXkDijmlWsRClYAKQkR9bVoHjxVpaiMKIoJROnp96mt+P5yZJo7oEOF4XxqAViW3CtAxxeL6SwH0UBaIUSkSOPxhi6U8B9EOOr4WCZE8SzfPbCfr915YoBff34/CPL8B08+xDVPbmPk3DzGPqkUdPsLvvej97yJdZ/6PTpNg3tv+xbRPTdj1g/SKtZ45y++l+CJOtvueoidwGrg6pGVBFHEas+jtW++F3/axksnsy02bCQDMH8acBTs5WCaAwzbNm7BxXULhGaEF/iUSi75whhXrr+KoUIRqqp23bzqXXGuXgwXGc80mfYCcsUyHT9gZ/PYDe5PDSWQzAmaHs4pK3Ympe8nQOlV0v0nCMGOhF2IBVYiQJJhiSCCrPKuDZFaAU0Upz8SwM5StGHDkiahbg5qE6p+gC1FRLyQOA7BbxLfcStDY20YXyd5DUcaYvrLhQtPYFkRLjTh8a2S8mgpi6RwprQiZz510TWNWEcx+xOKdNRTESN7jUx0qTLNI1g7JOFUECvHMEXZDdiwPIZ9bXh+GpblSaueaObRS5mNfdyLBQSpV350vvtV/vBjv8YDB47fcbmAeEUPkAp+zOJ9Fe5mnuv37WVk+RlYFrx332E2IY/18rPPxKt7/MH//T7+9LGniYCPIXUtV+RcLMNmx5YtkmaCOH+XFIcxs0XeckWF7ROTTE3sxbJgVRO+ffTkFYED2KfBZWPnky+4hGHIsDOMaZhYlo1tWwRmQmzHRFGINWgztLIIxXIfi9ZQte2OY9nFCe2gzWxUwzAMYi0fi4xTRAnEqZvdHw+3AGtIJnonUoSiUAR6NhRgr1gWBVCwYdYQ1+GsqmyLVZDdskRIOr5U+rEcwQGcLNARreo3RU6GhbM7ZFrg2ET1Gp3bvkSWmkQeElQOA4CtCDoRMAilojQr9Xx6VYR1PKhJKsRlVOIPKSag5VO78jrLEFJAUKNW5RmwB2T1DztQ269Km8WiCHq4gkYaPfVgF2OTaU3UbxFFiBbWE+ffQRE867Hjhl/jAWDHcXa7BlEAf8qLBV5XBG6+YFsAcCikdWh+QTn2+oGDfOPAwqKqv48wKt63eT9FvsaK3DArNpyBu+0wpgGbt/2YYuXVXDI6ykh1FNvJsmf3bnbumaGChL2OFVFYbPiIclm9tsKqcoUw6mBZFkEQMtsJwUxI4phiYZSYGLe4El4/ipiASvEPJLDsOFbasyGdlo/v1UjimFK5QtY4tqifGkpAv7lJUhyrCLhLkKw/5cM7Km8/Vll7lRH5rAE224GoKY1DdR6CbcuKP2AKSIjaz2/Kim1loaBChonCBmZjSfFsB9iWRdubgS0/AmKolCGIxZJwlPQ+Xoe8Kwqg0wLv6TSmpUN7muGnqcF1hKOq3e8C6YKtXJJef0NtzWvr3gFG5iHZL3ULwxlRBOVYuvMutwVsZRqZcnrFWMzE14KuTZN+uqLWyhqxfAWH77MZeIxjx9szwFsugXag6ustMl5IFAcJve1h/kXt2I812qgqQtPzrB4RLKQdHGZqSgB5txww5AwyVKrgRBWqrSbDzkYuSyJW12e4d9eJ936wkbWs5dVZUSxiKzcuVyrKP+KEfAI4DnEcYzk64QT1+wTuKorwm02SKGJ1tYpTyDM12Tjm7qeGEhhAFhu9KmqkZFa5Bz5S7dc5LMph5bCyWj1BSS1Lsuh0eS8Q4TcMFTsHnleWhOWoh10XJWDYYhXkXDGrPU++B9AJyVgW9jAE3gxOpLDiJJIUViI4NyfRjIltYo00A5GbHPAw9GpV9RMeQXAA3clYY3BayF3SegLaItAMwmFEtjUhcu1+Wcp8YOIguAdFMVg6YapFmrK4GLTVH1LU/pcmLbTV5xeWLHsFxsVj7OTYFXsKwFuWwmXr38Sv/K9jJQeloz/0N8tC+HaY45vterq5OTAvqhA/USNUdWAuv+pVlK8Yl39WijK/Wi4j0x6B3+RsC4aTGdgjOMHxwEMTeYqDWWV/JTFJYhBFEVYSYxaL4GYxc0VpPe5olqtOMtEJbP2+4wvGPExt3MjU5BT5Qp5arYbleaw6TleiU0MJzJOG0LRAaCKMfptaYJLH5S2XKlIeyEpgcgKCBAqmmMgDWTA7ECZpZCBEFSRxheOfdSUqkBjCuy8VxOIIAknCqNXEgjAczBji+l7qtTlK3EemWlGEJEfcD8eS6EatKeHIOIR4XhSaJi9qJa7xgDop6w8UcwSaR2F4n+p4HJNKiY5SriR18yNEmUSkVOMIwTeyBiQN1RtPh5C0JaAxaQ0ABqRuQdR3Qr1PXZ20v27xyx+rLjwTnjz4ou1fvfAXuGx9kXZ7ipGxKi1+wChw6YpzuKo6hm1ZXPPud/P9W27h7279ewCuvPpNfOneH/SMSRdRf7rEzAvdhiGkTFsCrAFGzobq+OuYuO17mAbkCwNY1iB2zgHLkOeZVYo0DjEs2Dmxl6gDq1z4jNKzDR8273oxIKlHHoGeLMui1WyRxDHDjkPWzanCM0DZgvO04l0MxzmGK3AopLlpC54nIGCj3sCyLFZXVzOUzy/+HU4VJTCACIxOqdWJff34QAidaciOI2W7KhUR7i0/ojvZILP+mtQKIBbTP4ok0rDcTQst+IoXgCUKIlBRgUgRjfQxoghKZbXNJZndCwm0vXkM73GyZaS02LAFF1UED9i2DabbasIgYcApdUkOqeBbiDCb9HhP8R4wT0uzInrC3wDvQXkclSuRTMImqbLUFgHInMmiJmsC0035fFa/v38FaacTHWPW6cda6GPSuGSLNHTxyg73iuvgyb9esO0rV76JX/2NT0GnDivXwXiF/781jRmb5HN5TLcAls2mv/wqrVqNL3zyV2g0Gqy/8wdcA1yOYAzagwrUXZbU/xrqbirABaCqDwssVJ+cYnX1YpI4ZsfEU9x77wyNOx9lzWsf5fob38RQ3RcrMZsl6xRZXfUYNmIwLYZcFyyHOIm4/IkGV0/t54Ea3Plc+npipNFJyQdjsoZp1AnaM9j2EorFIiOjoxQrFcllOU+/s5NzwzzPw9vt0Wo1aQcBl6+7ghUrV6bs2EXGqaEEXji0H60VgYqlZ0vA+Dmimf0m1D3wO2QqFQh9KI9KBOGQr6IESvifbUGkVjbfFyUQh4or0IGLHJhVSsBxZB/HkZ8gggiMwSHy9ixDlTzNe/YytQXGb5yBqgn1KdVXwIHmfgikqGQv7VcnJ64ltbrb6X3Gj/Xdtw2mh8xOtUDrNbmH72ngT1MlcqTLXwQMzgk5qpeBFJISFnLqZHrF15xlnRzVzynQBQ1CxKC2eCXdgsvWfwD+KlUCt7z9rbzrhmthvARnjdG9+ya+9LO/zr2bn6YEXHIatI7CVVdezLfvf5xVp0Px2vXs+G1p8HU38Gfnwjurr+KBTXtpPSdC30aEz2chLaOt7nTV6eI9uq5LxhnGn5zivgelWMYEkH0EvvPID8gDa1ZAqXQ+hUIeIovEhmErC7kCjFQwazWY9lg1cg6rR4qMbJrk93fN9BTBJDD8HHiPzWGr4qPO6XOE4VO0mk0uaTYZYxDTtuACTVY7wRF2mI0iDvg+ti13ubNW4zs330xp9Nhdp08NJTBAau4myHzTmJSe9FqYvP1gedDyietNzMqIhA+nA/kBBfIpO7uuEbqC5P37HVntcw7UJ+WgUSKuAUhxkGg2bSNOQhxCbrRK15sCyyLvwuZ7IXsLjBiHYfxMmHxcgMyo97XU39ezTlcp09Z1ADymAP/TSZevo+r/MUwdkj9HII0eaBdDC32ELHVJ3/9M7UNCqnkiZBr2s490qElrFodeCTVs0vCGbojgcrKr07HGyDvH4V3y99df+1betX6dRHG8bXDWGJlymbMLRWqIEuAouKcJtePLv/5WsjfeCJ2AwN/PrwJfA/5wH+z+jbWsWz/OTBBw/8b72L5tngeOpl60hbgCa06Hy6pLKJUKZHLDggkBw47D2CWHeewxUQId4B51zd/YA0N7nuZSdU2l5XD9u12yw64sGuvXUx0tEz/hYVJk2HSo7fpn7kcUkmaMe6RE0bOfE/JQsZilHQR4nke5URCcahmckCI44tPxPPL5PPlCHtvO4vvTGIZJvlBgVWXkmF89NZQApO6pBj/1E7JJfWuQ+XjXQ8yEMFQEKjmYnlJC54lGbgYC+rV9ZhoNTMMgSCJyli137LpgG8SBj+k4ijAUiGKwTLESjEEBAJMI0wYqZTJJCH5AZED+Ati2C9gII2WdiDS3kMWi3RmN5WiloN3sFnA2lAPgOYifk6/uASqHYPqQmKq64QgGcBGiSDqIXPbLpMYEptVzSyKYbYoJu1wjlFqzaq07iMqgJ4XItB/az3KaRdyGAFFJr5x78P7lb+Tbj3yPz//69/gYcP3Vr6Py6U9BFPLeG67DmpzgqrERatu2keBghAbZsfWAzcRNf8l394ivXQWuBKisg/Exhoi45uOf4pptW6lv2YbZSbBzttylZRCTMOQ6UC5BviAVogOPId/nyvJdNP7yBwS7hJ/QH8GYQXgN9wG5Q9D++qN8MHIpXzQuJgUdTNuG4nrKV3T4anUEz2uyp9Yg7Pg0vafZsUuskwcQtRw+ByuaASuqeZLIpzM9RXbagQv6gbK+8exGcFwYUDjN0pio40MSsqZawfcD4siiUChgZ23azakXH0ONU0MJzJNWCiuRZuw2ScFqbdmqMeSiSDX1vky6BJo16QtQrIDfZmh4GEyLxlYPK18g69gy/2s7CJMExzLEJaCPPejoCEIiTyintJBhQARDhfMprX0aw4VGC0Ym58XH14trPwjok7ramtIPKRtFC3AHzCyYB+TyuopRlj8NMhr9N0nRLV2yvL8Ois5vAslNMIDkoNxHBJyrLyRSJ8+RZkb2a6qQXhWmXl6zj+DfWutoq+HljY23Psr6d72m9/lewL33YezOf2fzxONcfu0b+OZjhwnjB5h6cp6E/axeup/qhw1+6/J38Md/9UXuuvHdTE1s47s3/Q0PHIB7br6NDW/7AD0T7G1XUX7bLBxSYC8xDDiYvYffV5LuLAeMGplrr+VDpSLXb5rggS07uHv7PA/w4sSiaeAPjsKt3/wBt3kNxj71blibl7DzgA3n5TE/VqD8yBTliUnipsfUNoPZ4CkeO5TaWj6wY9ccpWKIFQVMTfyI8deNsGDS94ZHXGsQxzWGrkCA8GebWMCBKCIMArZv2cTOWo3V1SpvufZaVhWHFzmOjNM+97nPncw7+3cZX//y5z/34dWkgr4XmWNnk8bH54AjyLw11ed5wMyo1OA5Qf69Z2DlhTA3D0cSeNV5xJNP0Go/T3n0tXBWFp49BE2Pjv8Mp68swGlzYnaZBixdCu22CE6SgLlUIghLl8Lzz8vvXI7s8ixnZmfZvvUISQiFsxAFcERd1/OkIb7D6vNy9XNY3dOZSEvmI+qejgB5yA5DZg7m5mDoHGAUuo9AZpk6zry6fx0RWKN+T6pjh+q4qGOGR8DfB6fPgb0MWdUPqZ0s4Cx1Ac8Dz6r/LSWNKuj/7Ud8FZ1mbSK+nD7ZyY/zR3Ksy17CzffeBuqIrwb8AwcojJ7Pa9/+Ni4amud1b/wZXv/6i4h2Pc37f/f/gdERVg8PcfqHPkDnhz/g6XqdWx7cxX3AzU88Q2XnfYzc8EF1ltPkfgYLkDkLMjkWdn3pv/55aD0ubmTDw1qa4cLRCle+JsdrBrsc2t1ZNNx4GNi8+xCvPvRvlMvnics5YMCSOWA5nFOFM+c57VCbswaWMpeZJWocYlbdbw44Azj3jKOct9Ilf+FKTl9ZhjPPhcwLescfeYbT2ocxBkwyDEiNzmf28/iOOo8+MsXup3ezfPkZlEpl2u2Avc/sZSZsc/PmB/d97nOfe1HL4lPDEuiysLCGdmH7c+yzpAtSP3fCsgQotBUIWKlIyC9MxFyKIjxvN5aRo2cyBC1hHVtIPWmQ79abkMul/IJoVn4bsRCDnKqE38IQBgsMWQalysOiq2MgHgCjr8OQxgUs0o5DIAvvetLqQvp+dVzLAK4DZwsi6OoSuw2IjsLQa5FFuIaAj/2ukiYf9R8zizAv2w3xMwc0M0kLgo47t0hjsdoi0Iogh1gPujlXP+bw8gqXXPXJG5nMfo83fuithMBm4JIjMDJSheo6qp/4fSk2cs89vP+6G6BYpvnVr1B4x3WwcSN/+MUv0ToEv/mhnyf+q39m7JIhvGaT5rc/S+E9n5frfb4Jy3KkFs6xhgsrK3D7Xdxz2/eYDeDtN57P0Np1rMtl8Zt78Z5cPNW4Bvze3TN8d+OfMTIK11/7RgrFsszPd34EzivBqIfRqkui6nJYcShNCRk5F+ysQeD7JEZCrhKAG4qO1uPQJDQaxBFEUUwy2cApl8BxWV21cd0COyYmiKKIVrNFEASEnZAkOTav8dRQAhlk4vqkqIlm2WlAOiJFViooS3Yg5cDEIRQcmE2EOFRWQMjuFq6bIwlioQ07lmQiJqGUAehFCpoi3CuLIvyaPRgjNOGsLf6ezkqMAjBM8mVoeao0IjZEh1W5sAEozqdVzfpdbD00UL8bOk9C9jRSsK8pzyLeBeaVkLGgGym8IETKvm5BCEm6wKzJwgJMXt+2ogWu1VfGS+EgPYBBgxn9yQ164uiHrHOYtXbT+0S83Ey2kQ9ey+EPdmGXz+2f/U0cx2Hs878n4d2772LLPfcwfuON8DNVpn7ro3z39tv57Pg49YltfOb3PsOBZpPi+nX8xV/9M43HZrj8Uoc7v/ot1k3VGfnwx5m+4w4cR8hkZrkIr6+ykE3ZNwbG4IZ3c7Vh0KrdxwwGQwUXKhVusG1Kt9zO/ZvneykdANZSqB0RBuT2I9DdDn++/cdcxo+57AL4UBjCBz8MxGRcm3xxGKf+NPlD6WkNC1ZVili2RStog9eCXABnxUjGqg/tUNjwWCRRRKs5TZwY5KpVMga0g4DEI/w0AAAgAElEQVRWU3zGUrnEqkqFVrOJbZ3qtGE9MQMWWgL9GXSzyDsbRYp0+PPAvAhugpCEajVhBDoqlTcGLJ9ssUhiKj5/FICZSO6BbUhUoBPIcSoVSfgxDBH6ZjNNNNJVj4azKllJuL2WcwaGfxjPh7IbgzUA0byQhTTlXgu/BuMN0oiBwhGyl5Cu4h6wTYDCncAlm0QJZBywD5BaQ+sQRaDlU8e/dJTFRqoUTQPhQdn2M6OIVtW8ZJt0Zdej3w/VoIO+Z20lBOp/g6Qkj1cg/fgClxu+9Z308xFgdC3j4xsE3HxyIyPXrmfFaBnesYGyCbgu9l0BFAr8461fZqbehCRhNghoTdSJv3ITuQ3XUrvtNlZcVMaMLTA8icUahgDFy1+QH3HBNWQ+WaFw6DphkRYKahGwqYYRjrsNP+ywanyc7IZ1QEI38Nm+cQvf/pNHe3yEFuDtgvqPN1K+boOwS40E00pYsXIJMCdrTgyl8hJsx8TExM3azPjTDD08ARePwzMtqO2mGYS0g5BOGNCo1wGTbL1F3vOxDItarU6j3sDNuTTqDUZGR3BzLqXisYHcU0MJzJOWBrBZ2JVXE9w0wSYCJhQbrzQkJr+vkoospdVHRuQ7rWaP/OMM2xL+C1S8PO5AUSX8DCITAfU7iUUZ2LaED3uZh0o5+IbKScgy7BRpGI9i2UuIQzCzthCIiMQqKJwhv3eTLqwh4r9r0s96RFDvIgVFO+kj2HMUVkSQsaFwLqIINaVfF5fV/B4toz5yTm35A0Qq9ZgSaekxE9E6GvnXq7zWwFoB6JeirQKtqft9kVe4BsG8Oux5qZXR8Rpkx6pkq1UYKMKGDRAEONUx4XQYNtvvuodSrkBhw3U4dpGpr9/E2RMew4Uif/pf/wer14xhuznG37FB3mM4Jfm9F/QrAhNwYfkVsFy/uAiMNpTKFC2bIrMwUoJLrwBKZEgYe0uTsRu3wFSNeOP32dncj2ENUC6Xe0V04zAQrNgwKJUhXyhgGAalcplMucTM1hpRDAd2t7AjcO7fCFFCJ4xoNTsEQRvLGqRQKONPN1l1UZWw0+H7/3IPjjPMqkqFnbUa+UIBb7dHNBth6RD4IuMllcAxGo/8T+CtCDzVAN7f7XYPqbLkTyALGMCD3W73oy/5siHFaBIUhYsUXdch6ydeuG8CjQYwmArv2hHltycwPS3NREM7NYujSHibbgG8FtMPT5J7nco/iBKJU4eh7BMFUMiLO5CohxjH6d8YmIZN3j0TQMKNw4pC7BbBrMHuwyndt0mK7q9FZG8aEdYICf9NQvOAfCxfAEUfOs9BRstiHpFhLYMOqfWkn0tOPb+t6lxldd6KCc8HsMxXGzXAp2iLPRxA+y66FoHKtuyVxNBYAqRaRyuNV4hMNA/d6ZCMqX1CaH7zLnb6HpcEMbn1G+Qaaz58+Y+obdrC5l17ufrSN/GLDz7KKh7lB/WIHY9t5TeYITmwl1VIgdBPb/+xnOPuf2ADcPXpr8LO38Ka9eNUv/ZbpMqsfzICBHBhoupLaAqXjpLo77hwaRUurWFWy4w0m0r4Y3iiDmurhGGCtbLIlaNVMmoxwVIl7g2LoXeMMhTFsLsF5iDdpkeUxESJRRRF7KzVcF2Xy69Yx4qiPO8oiigW8rSaPlNTUwzbNjsnJ1lRKFKqlMnnXh5t+JvAnwE39237V+B3ut1ukslk/gD4HaTnAECj2+2uPoHjpkNbnP1ktf7SMhpkmyCtAVpAmHkBUC1AZEic32+pFcFSBUUS8fmLrjABjbjXRXjL7XdQyBfALcsFBG3JBTANcQUKBSUXYmoStIVZqDsQRyGmaeJkHfHDdvsUKIiFEUSpnASAtQQKBnRmUqFdPwSDCXhzEn2bln1dD7yj+nvAc6TpxwX1LLQFXmBh6vHr1PMKEEWjlU4J4LAkTvUEOUKEtk0qxP1uQQfmt0nqak8QNEDTYiHimaiTvTJKYKZeY0+tRuVt1/W2PbBlE8XRKo2tPrmxMnzhJtiykfu2/z01hOE3+KAw+7LAdx/7IZvVHe1EPKdrWFhi7B7gnuf2wpN74ckfcsOmu/izL32eXHVEXMyl/ZhBv7Dre4aF5Cntvzoqq9WD2CKObUzLBWsMpwJsWK/qA2gFqqyr51XcN0boGN5u2pNTOMUCVmLQikKSJMb3fbZv3cbqapUVK4vMhiEhJsOYrC6WSOJEJSZB4nfwopeRRbhY45Fut3tv38cHgRte6jjHHboknJ6bT5ByPPUE1/UG9Mq3FRGMta8SINBwwFkpMf84ESVgmLKqZ22FYSmzzjRgsoE1aLFiZRE6IV2vScaywHSlDBkoQnlDFR9xRTGAJHpYloQmHQdj2scwTdrtw0BMwYqlLoFlq/yEw1I4xUIwg4K66fqM3IOuF6AWY7MI7i71TIYh20HkaxSh/jeh1x1Nk/gcRJH0A6olUou+PKS6FvVrJu17dRDQRZsT0PPBBhzVs9FW76km8e9eS6V+YTi2yXlS44j4L1oBbPrDrzBSLOEHPi6w5qKKEP9v+hY857NDXflOJG+ggjACQ9KsdB8h+Xz/JU59+2OPsuqLf8Rnf+MjmPYgjFdVA9wXDhNowPOeyu2v0AN0jtRlwdFs1dERzGJFrNKBKpx3rOrAISxTmZzzsSw2mIRhhB1GmE4RCBm0LHY+UcPzduP706xZu5Yw6BBFMWNrxzAMA8/zaNQbJElMGIb4zRcV/O6NVwIT+ACihPU4P5PJaLblf+t2u5sX+9KCvgPnIMLgky5KmpPiATUB9rP9LmdO/TRbEMyrWoQq1JWYIuidjgTdXUdeSBgqRhd02wHVN47D8LCkJAMzQcCQYRAHbczqauU6WAowVMlFoVIApiGhn8FB9ni7RXkr9zjuhJh5jVUYYA9BMisrvhbK3AAUDZhWeQbajB8BtkF2KambrfGSSQQM1IpQh/B16QDV4qAHEmrQ3gDclZJYZOovaXaWDvtpv0uThvT/RlTT1qa4WcG0KNgL9MET0jiuzSuVdjykzFye9GhP+/z5XfeQL5XY/vA2Ln/jOvjmFrrP/ZAMqT7UMY3LSdGNQfWzGOXmWKMxNUXL202xmJNclHOPtachUaVOAueawBQ8WYeJKZkbrgPVMTi3jDwrLfjHAukicdcMU97Tsiy4s4DJzlqdQbvD5h/tYGpyEjtrk8QJ9228D8/zWFWqMFIuKhxgN77v0w4CDNPAMMxeZuEx7uInH5lM5neRZ63h3H3Aim63ezCTybweuCOTyVzS7XZfFKRc0HdgJNPt5a4MkrpbAdARaz6rVzutHHQkwZhXbWMOwvq8NAAtFMUayBXkOJ2OrOgg/heIL2bbdFstMsUCGd8jnPYZsm3J49Y1CLO2/PZ9MfM1HmCr7Y5DvlAgCHxs+wwsy8AwDaVAHBGc9nxfYt4SaM9JBKEwJ4I/jQI6EXxAA/dNem3Jph+D3HLEJSqTMgR1UpJ28zWugvrbWgKtOZiqi0tkuWrlckjDg1qIQ1mBBrR/ptmEetL6Kjzqw64ILtDgQ0BK79SA48sYS8UamXlkkn+65XZ2bNlCq+GxamQEd2WZzLr18NHf7nW5KSLAFOr3ZxDfX1+Zbld7osOMYgZtC9Zd9uKowYKRg7MqQARHdkvfhMkJqNXFcqxU4NxxTpxi7aoWazE860HTo7ltC36zQ5REtJ6YolGvMzsbUSgUGHZkVWwHbR4MHmD7fZtwhx1WV6uMjIwwMjLCHs8jCNqsrlZh3+L9Gn5iJZDJZH4ZAQzXqwrDdLvdI0hQh263+2+ZTKYBXIi8k2OP01S1OB9xNRNSIK8ApgmdmnLx9RWPovpFybPjiteIArAsKTnmT6fFQWo14naAWVFQ+hM1BSRKKSfTsJgJAnKVivj8dpZepaEwlCXeshYWHKk3FLW4gG3bhJ2QZnM/q6vnkFGJKNKo1JUyZ14DvHmw1cofIBZ4QiqP/WF47YIrm9YB4kNSUrFXiLSJKI0rSFOPS33PNUEeWFUpxSgW8HOZZmX1Z2hpMyuAQ8rqWdqfd+dKUlUwrUKkCRyZgKUaE9DH0ySPfj/55Ef8jEer2cLzPOyszaXjl3H/xo3UWrdzjVskvv8vehH+DpKxrYMtmhKkH+EL6RkvNd557XXkqmuVAjjefWjbI5FnYsawtqoyUG24cB0nn2MRwbMB1Fs0JyYIw4hWM+CBLQ+QX1kiSRIGBy0ajTqrzAqO47BjYkLE4lDEjj1P4Xkel1+xjkK+0Ktc9IpbAplM5s0IEHhFt9ud6dt+NhB0u92jmUzmAoQRuesYh+k7IAtrW+jnrt9iU1kCIGq9QhrRKiN5/aGamKMVqbvnOGJWzcZ0gzBVAM2mrOLlMvgqgSgKGbJtYdPpOgSWqhwMij5sym/fFwuiHZDJFyCKMXMFVpgmQbif2SiCMGbIkYIk0nswAtOW9lpmAtPzKTCvrR6ftKq5xkJ0eM8C8xLo1kg5ALq+h5ZjnXAVIU1PG3NyvHEEm8g7ogBsl9QV0DltmhWYFQxgefSCgyuQYSAHrnIdskj6tllTfSB05ZQAMVfGX/K1H2949To7a5O0mh4WBq7tUHuyxiwm1D180nYwm4Hvqu+JwJ+Bw2FA1pQHT+K8G4BV+ZUKOzmeAoiBCXhGwYyWJQDyxVU47zLSmnEnOwzwfLbdsZFavUbb93ELeS4ZHWM2ibBtm80PPkQMTO3ZK6dG3qgLFFlC+1DA/Rs3YttZSuUSURTJvDz2GY8/jtF45HcQcvm/ZjIZSEOB64D/L5PJJAjJ/KPdbvelLbEjXbkL7VLqULXm3ufkd9wEU1sA/clVwQw4kfj3CRLaw5FQ3e4mURgyZCYShnEcGKmKWWHZ4uvHkZRz9hXwl8TiNoRhL5LAsCP+n+PAtE9GcwbCBIYtMoZDvnAOTjnfF16PRBEZ+lGr0JvT05up6a5lRstnFnmrdXrWdUZnIEbIim+o/w+Szjctt6UzmPnxYYYmZ2DEEteESJVb04QCSIGFHplArnFBGqfWPMrXSCJo+lLjsVdzYJA003AbKVB28qO7r0nLa7F50wPs8ZpcUiqzbWKCx5jhE1f/MphWTzz/HGl5pf1NyeeMe1nZOzjx+n8A77zgjZSrq+HC0kvs6cMzDTH/TQtGKlBaqZq/qAl7MmM+hnqD6clJtm/cxNTEBI1Gg9aB/eSXn0OpUsF2bIYdh+GlS/CPzOliVD1LxwMeY04KmUYRbj5HFIWCDWjrdJGRUZb8f+pYc1Gmu/3b6oNGwrUjN404ertJw4O6II6euwbgDAgIYxpg54BBEVBN+409WFkCilAclzLh/3YPGIoJ56oJ25iSv4OmrHRBR2L+2ay4ApYFmzZJ6mnU9wqyWeElgOQfWIPQ3CFmYeDLfv7e9K3paJyByEubtGrQFtLaijpi4qn9DFIXqMrCEmwAyYBwG+wsbHlclEQBuOLVEgqdrMkzqlQUrjEL4W6hU/dz649EogwjRZ92bGmqQgSPb4LJp+S8G94ASzUYqKitWReWrUMw459gPOIzsW0r3/zrbwBgWYNMTU2RLxZ5z+c/xZ2f+SP+5JHvcRWLlyqf5wwaHOZe4NdYpBvuIiMD/Cbwx5t/AD+zjmPnFwTw/BbYukkyVtseM9MhQ7kijF8Fr38zUh3gREcEh5rQ8Jj6l/u49eZb2P7k0736Mf2R8nVIVmn7qFg4k4seT4aGIlevGMIeNAkTm6/t2vtv3W53zQv3PTUYgxrh0QpAFQXuXZ2NMvtJO/lAWhXLISVbODnxfTsR5IpSaMS2mal3GKoWIVsBsioMZaVUYhCT37AUYzBQ88CU7dYLTEPTkOxCyxIXwzRF+HUY0s5CwVX9DNR38+dIVWB1WEqkrVo7wEZE0LUb0E/RdxFFOKruv6Huu0JaiyECrHm59mGHXtsAm7RnQ7kkLs3EhEKhDbBCMFylBBQouLQkmMB0A7ZshdZB2PAacbcGHbAV4Firw2sduYD5SKHaLilUd3Jj5tmIzXfdRaPeYI+3mzhJeODAozgsYc+BJqvvuIcDj9S4AakdsJgSiIjZAXyJE1MAv875FJdHXL5+HMbHObYCiOS+vJpS7IDtMGQVoDgCpVGkZcnJDENCzYZJnMS4zjBrXmuzIoq4+8mnFuy5GXjLURg+bQm1o3OL9l3ov9IICDszEC/BD1+GO/AfMnTJ8WlEKDSSDilmFZMWi2uo7QVkhSwiq3ehIALcCUUB6BBfs8XQ68YldNhJFDdjVlZ4dEgGcQeyluQSJImEeSxLgEHbFvcgCMQiCBSBSD/BTkf20RTjJIZKCQJH+hJ2YjlHZQi8mZRxa5FG6GL1UyTl4OioXRVxtQPEPdJh/QSxIqbV9iZQzAqI6Z6RVlgykOdhO6qoijIdLEvOtbQ/9GIi5kcOLhiVrLqNG6WYazOQg5k2GAdFuegxYIOdwJNT4HXg505+Kuyqeeys1bj3nnu4/8CjrGBIGYVzfPLSKzC9JjZNbn7f39L55lf4Jg+9qFvwY8xwP5xQKzMHGDnN4ENf+sO+PpeLjRDYLZyJ6ZaAgIkJVhEKJRgdV9bPsc3uY44wgCDANExsx6FRa/GdXU+9qK1ZF3FvnKNzOMgUmH7RwWRokDSOIIzmCI9D4Tg1lMBRVHkVUuorpDkq/eB1izS9dhaoqvLani++6nRHNKttS7JGMxDAb9ABw4bEUgBgRzFiTdm/01QTOl4oMI4SCh0lAHotv7RiiKIUP8ja6cofhRJW9HfTyzcu5AAvDRtqd0ZHBzSC4qrPK/u26ZaIOmql3QDNCjTU89L1FZv7YeWrxCrQ13mhypx7tqnuUVGCn2/CsojUkHQQ18CRrLqfW4f4IU0JX3UidV1zsMuDXAyxJS7YlgmoBzAWSg+EkxiNyUm2b93G9gOPMgg0mOmtdmEQENTqfOyC9UBCg4coqCmhH4GJ4ATfOIFzVYEPnnYGV797HNZfBucdLxw4DftUC/pEzR1nGApVyBdh2VpOTgHEyASOwfeZ2DbB/ff8C5s3P4zH8fsa6ojwi+LufWMIQWlaR6B0OljmADy3eKPWgUW3/kcPHWHSq6M2b3W4ukPaFcsBimdCdonU18+5oknLyvQ3EZ+4UJDVWpN8sioZyHZgwIVwVohEy1xBxLXJ32ypWgLKfNIr+/T0QregXBKQMArp9Tbw/bRsdJzIahwGgh8UXBG4IJBr6U/k0yH2OrLaT5DW/dSKok668muKtUOKLWgFOoxMzqwSvmlfEql0k5Z9LVEAlmJUtpqSstoJFWU1IBUrHbO0EBbTOLAezhqXoq7o3adF2bZ8VfzVZ6Yxx51fv4n64yceoAse95nauoMdT070dGL/t1ePjpAc8rCDALZs4QHEJfgj4FdJSyuciALYAPz28jP50KffS/E3roPzXojka7NMZ1hG0gVlclIa1vqeYE8Xj8NyKXV20uNIALsaNJ+o0Q58gqAt7SOOsXuGlJR8PAUAUo9HG4nec2Cc8h2IjpCC0X0RKUDmou4m4SDqrb+BX7OpBCUEc1gEzTBlu+fJ6u+6YGSls9DyksTBbVfAQR2X1JWH41i+n8SQHRThiKBXvjyOVVQgVOcyVNpykrofHaUYgkB8R2cYkqzciGlDc69aSUgxAQ10xqRdh/TCpHkEmh+gwUQLqVvgzKcNSRLk+ocdcM6Q62weVG+6I9uxhIJpWWKpxCrNeqmmF2qNC2nlfp/UXAGKJXC2QW1emp6MOzBcFHwlmmdPHe5t3kUtchkZGeOa64+3ysqYeniSsB1w9ukuuecOLzB11y09n1+9/U/hvxRpfvVbcGiSy4AqbwQchvkeX3vJM8gK+RngstPP55I3j2COl+H1ml/dP/pX9WlpN08CsyEz002GIguqGqw6WU5EBPOBgM5JLOQyJKvweCpTi4U2+BbbVysK3UROO3irR6vcvvmhYx73P390SZPUdGwnBKahMwnZtciz1iBgFElNfzcRoUIJ3GhJhH42Vun+Ragorvbjdak7SARWBMv1i1O822Yg5806AvgNFxR+MCiJQ6Yhq6o296cVg9ApQqDrGBhKVfvgB7LNj9UcsWW77QpA5x8EcwDs+dQasGCmA0MFRIXX1f2Ok7Jyc+qyNTPQMsCYS+U0AexASEqOI8rMMWH6MDAL234IhXMEAA2VWVvQ7k0Hzh0lRV+1iaJLOkWIth4RZboe8P5W9VN4Cq5IJIoA3FeDO5/7Ae5kC/Mjv3pCSsDJmtiWwWwUMcgAaVIJbDryNOyL4Zeuo33ztygcalPlTGAbW5hbwFs/3ngnMHLJ68hXR6FcgOo1HL/XonJ/gjY84YHhMFS9VnpNXHwNJ2cBxPDMA3TqdSI/Yo/XwrIGWeEWWFUsEa3t4Ld8dhw4vKiAx4jXPMSxCVAlYGTpAPGR+V5B6uHlS9g5cexYwqmhBCAFwHQyWgDdKYVfaWXbUxBz8uxXFmHyaXDOlPDfsA1WDlod2fmKdbBsPTy7VTCBZS4c8sSs78VyA+FrRwi24LrQ9ESIirk0TKZXemdYkYcMWWVNSymiQCUtGQo7iKBchWlPYuqOpUqgBQJaBpEAhO6AvKncPLRg1pSX3CsdniXtJBYhi3GZlFHZnEsBUp1n0FaofWVlGvo0DLkPT5KcMA2pxNRRYGcSC68BU3HldSEHHcdcSdrdZFgu7rzRNIfIB+pPg30mOEPMxjMS6T30OLXJYxm4C0fTqxNMt4iORhSXFvGOpDTXMc7hzReuYkXcoXgkIsAi4SBXcg4m+3mpqD5IyYDL17yKfHUlq8bHyYyvU7z+F44Q5jvyXLwHBF0zVd6FrZSrW+LkG7XOMlOvsWPbj0iaJr4v8fvAbWFnbYKgjWEZXHMa3Hn02Mj/8fCCOmAdmWfVaZB3hnDdHK7rCslz+6lsCcxBr2Z+QK/JRrsDzhjpfNRKYhgonw+1p1X4rKASiBIRsukAiqOwbBQObREhLFdhV00VjtBOdVPIHXEiK3wcQaIThLRfEsuq7ziidCwLdntpVmGhIu5ApyWClMQSYTAderULogC8QFqdRR1ZVQorof04TM5DZUCEqS7RzV62pFb3HdKKQ/30sOwSeGIOLloCG+YUl2BATPTBGbkW1xUugOOIkG85LO5BEcgNqVCmWvkNQ64XWEg+0JmGg6TE3IJ8Hr8Dmg+pUgNL5DyNJmfngD0wfIKw09S/TnD/xk3sbNTJn11g6sDCEtnFFUW+u+chSsAXr/4oVjMmeGyKzH0PMtb0aHz2E7Dre8c8vgP87vVv4PL1GzDXjktI76xFGH1HPNi2hZnddcK2j2MhjUAWDG2Qn+yIGCoWGQlH8eKAsBPheR57vN2c7eakN2GcMGjAlUd7sGGvQtFiOIDGzTukFJphIDoKKxRjMIkTbPPYLsupoQT0nWjquXI7nYsQZaujBBFQGoDcsPj7NWDdq8QkDkIpBhoGYpKuLKYpnaMjKmqgH4QivTwzpXIBEtH6OhOx2YKLFE0vCCSfANRqGkleQiEvwtxsihIJI3ElDAAVUpz2ZbWNElmR20E6d8IIKhfD9OMitAC2VC9bkMinqcQJaeavg4RJxxzI7ofJOVj3amg+JZYKB4VM73iQV8SVdijXc8WZsPugzLD6DLgzEu4admSVy2rh11Ri/bz6gZoIpYnhwo9DtQ53HARrTnbNFwgDiXE3macdHLsFlh5nOy75fIH77rqLnUcPvmjCN/bU6arH8Wv3/kWvyejvvu8tuNUK/+U4CgDgprf/PFd95AaojMJ5xyDzHPLY8oXfZvumjdiGVPyxXYfCyiK2beMU1SKQc+GsnyRJygbHwcYgCkM8z+Pe+x/qUT5swD4NwqNpNT0DcC44h6ld+9nBixVBgkC2AWlgLb8U3NyZ/4e6949u47rufT+gZiQM7YGEUTSwAVeQDdhCLLIVbyzXZmu5sW8sL8ercV+TJulzu1Lfxm1e+nKz+uvmNbm9eUnbm/a26Up/vOQtt0na5iZuE/fZreNUaSU30orkWmqohJQM2oQtsAIlDKOBiFEwJGdIvD/2ORhQJiXZTlLdsxYXCRAYDGbO2Wfv7/7u78Y0DKZP1klbFsXc2l7LlWEEUiQoOCSsVo3X6JqUYYQUFMXQWFZk6SJ4LSEJgSDxuRIMZKV8t1js0/jLkCBydbrVKqk3PQAnDolbbJgJep91oVWHMJQS44pSH6pW6QYBKV1N2FaceUtB+aF6PlLudaD+FyMGpFkXt9JHNA6Hr4F9ikAUq8OEJEzdJsmdzpDIAraAqTNgqdi5Xhcps8ZZ8QamllX14EQit6ZxjGJRDJivco/tQM57Ppbf1+p87Dyy+7fVB/d42up3Fngr3OdD/f0KSI9lkfRxU770zN9y4Kkpdt+39kTMvaHA9kMlSpUyk8fPrvifA7zlvgf4k3v38CPv+ykpGlX/++3pr8D0V9Y8rgn85Rt/jDt27xIj76wSwy8EtPfu43N/+Wk++9jfEyCOUmndWbYObeSujEPacRUGkxHP85UIqy77wj2pVyEOidohzxw6xOcOfqsnk9EbS/JLmxgbqL14hharewJdhGCqiUMOoqVgpS2yjkNxW5GtxSKBvzZR4MpIEUKS/iqSSGgpIlqP9ZFG4vn6XOIyx6o6rlgULwAU4SNOdm5fcQjabZQlgW/XSRWLcpAoVAVCyloUi+JdtNsQzktxUakku3sUE0fq2FZaUmxxnGAHkKQXI+Vh+G35DEOxDxszKqWm7Lf2dhSluO1Dt0GiJOSzsrhKdwYDCJalgMpfpGfTx5cTYxGGIsFWr8ku76msQKUiHlKxqPooFiCflwUMJDTFYfXjkHSKNUhcYgt4GN7zP4SzocaFBMun963G7Vs5du3aRan88uj+vz34aximiZt12PfL/4PRy5i2ZQQD+OJ9P8I73rYHp+iKcb9q5Yl1v3mE337Xz7PxLT/Bex/7e44gHsZe4NElePqbcxx8dozayQbtUIWIRj+Z5RJjOYRqDSkvIgIAACAASURBVOp1mkeOcOTzX2Jy7BhbXJfSupeXOJsIJqQdRt3y5WLEp0FkuWjDUVuAUHmvURSLFNnuK701uUlSI6+zBD69VHU3hJQLOIPKHVevsdYLiDc8Kgtdk3kIJaXjA9hy0+oz8KN9AkhRBDdVgGbCemu3lKqspXTfrIQEZJqygADTSstj0xQvwS3Igndy8gXakRgR01CgWR3CjKoj8MHOCvsOE4JINAbsRfxD4BQUB6mlRFQ0NKFZkiaywJ8j8Ri8DpQ2ymvL10PtJdkeRhGAU0Eboqq8COOnoLwZhoYVn8CS72xayoBarHTD9NAnpG+aJnVYwC/AfcCJx4EMD74THnokeef+fft41+mHKa4p0AEGJq1ma4XAGcATjz/O/vMv8NknPsVX/stH+PqXn+Z33/Me/uf0CcaRXdAlkWV4+6ZB3vcLD9Fu1hn96QfEwFmGEpO10ajrxF8/zm//5sd49PnV6+x9xBgET7+EH7SJY9geRrh+QOpOQzCnVUcEC/MSNjoOtNt0pqq4tkMtjNm/90mO1hcJl+DnNq3niXOLK1pr6GjwUoVPDonTnAd2bt2M6+bwfZ95VTk4Wa0yWa1yx+iei1z3K2FowE8xUns5cbX5pDQWlcnAyTOqgGi9rJgQYa85efCn4LY8fCdUpB1LLcKGuHG9UrspuFZvlaoqzs1CmJYJU52QHdFRXPrAp9e63OyjEvuaTKOIJVZavUYx5yxLPABbhSoNH0JL0oPGehEhLawHb7FXFTlVlypnLZxcH4fiAySekt46Xo8YAj2n63NQuU7O8+7r4GunVO/RGdntM7Z4K7sq0iihcRZO/rPiGmxM6MQFCzYYiAumnVC962kj6yP75QxJ6acNjMLNIZyrYu65GfORE713HPjm3/DQz5fZ9+XfXnMaZMoFSreO8K4QPvPNxMU/eF7whRrw/t/9Tf6m8Cf8+n/9Fd7TbPD0k49jA1szGQ76DdyhCvfcuwezUlYcDTsxABtychG/XaOx7wAPveMXXkY5Xm0cAryjZ5msP8pQqcTWbUXeZVqkdueSrBaohrgRzM9DGFEdGxPxKWImx58l9D2cjEW+6BK+eIpjwK5zi9yzDo4tJXyv1TocrTZ8VEqQ9dhXW4TtFmQzFAsutm3T8jy8oE0QBPzxI59a8zhXhhHox5w0MNgvsNGjx6rYmfWymEIL/AhKmtBiwblAgYR5CRNsieMZvpteSnBhBjYolO3bDTBiacxhqZjXUluupfJyYSwegWHINq08Ato+g5YBhlqhTU8+M5eD+kkF08YqzCnKeeBAew5YVN9zsRcC+KYII081RI6uo9keyNtWQME6ddoACso9rs+AkxbEvzwguMn4MrRfEm5AQS2CH90jlWv1uvpuhvAZiCBnS2YkUkVRugLSssBQz2lSlO+LXci5MFBGpuSIcDCGWzzIiRXsvf1P/SUf+sAQv/Wxd64+D15nEzk2cR/wXmA9DdXCu4toBP7S7/8/3HPXLt7xsw9wX9kkCjwsI8T2KzhOHrNSECDWjOj1YZyoQ7tOp1rjsSef5B/3HbgsA6DHFDA1u0x99gV21RvYBtzXbDOYK+A3Gth2hjAMCYI2cRyDAcfGx5isTpAmwmvUqJSL1Go+x8fnsJBawwbgLgnko6e9w+UpIRVQjuLVi6Rtg9hbJmw1KBWG2DlSIeuMMtNocPTIEer+Cz284cJxZRiBJZISWx0SRCRFNAUSEdw2gkKzqEqELZmQnqdSXWoH3lSA2JPnHQd+QJW6LtdlcmCTyBhFEtdvcuDf6qJLEIYJZ6CQF9qwZckiSPfFg7b6/DCkp0ZkGBL/hzq/bIhr2JMdk1Pp+MJbAiE8tpqCaxKKA2FZSKqtX/dD/24g3pEDxMuQ3QitOaAjKUF/OTEaNSA+o2bYRiEJqXAGOyMGyrSTUmnfT8RUJ8bkseuKiwKKoGSoFJvmL2sOgXLbbsrw0d+Cz3yoH7Q7xR/+7p9Sssv83AdXR+grlSH+50c/cdHpMtP0OXzoCDuHSxDOkM9ZpApFcgVb5gOGiM+GIZ1GncmJcSbGqhz82jeYXpAinOZFP2HtcQzwTneY+dw/Efgx24eHOD4+QdqyyNh2Eosb0hz02NgJPNVq7PjzL3H7TQMYptwS3f/IIyGPVrm4ElKFJHGruXWEkpF23AHiKO6pCLluju2vr/DjDzwA7hhffuyfVj3mJY3AGn0HPgy8G5hVL/uNbrf7lPrf/wX8J2Rpv6/b7e592UEvHF1kwfdX1ukae83p0Sh5b2fcqAp3Qmkg2miItmAYqbA1TNJ5+SIsBGBWhTNQyAOBkIS8hrx3pCLlxWEodQGNGXrNRowQQlOMTFV1tvU8SR3GMT3dwVgBgeVSUlyk6wqmauA4RCdPYSIGII7h6LOwdZtkD3VdkrZprguDIyTVMQHiKRVIWgLqsFSnFWYWwVhOQge9Pp31SvhnTr2+r9BJcxuusmE5ToxaS9VduAWZcfWG7LCWK+HOBgcpndUZam0QHGCIwgd389YPfYUv9d3qNBYPfegXGbr3X9n1hpdPhYnxGvWlM73H2gvoH3sXzuA/f4ahsWNkjZCDh6bYUSlTLA/h+QF21ebokSNMPDdOrXaWg+eTS/haR4Tcggxw9MgYh488y8xsBw948w/dyBbV+efoi2dwtgzinZPPnkUWfvD8MrddKz6Tbs6h2eAzXLr0uQjsuGE9pmEQtDr4bbk1WdukVChDFBMEber1uigO16ZwXZet7topzcvxBD7Ly/sOAPxht9v9/f4nUqnUzcA7gB2IofunVCp1U7fbXcMR0W8kwZt09VyNRGIrIkFNnEHw+lB4tyDxuQYFAxUOGAoPsCwxEpaXEHciW1SHml7Cod9UhnNTsngHHBEhAUjbinvgJErDGv3POnSbDVEZUqKPfqOB01T55Lrfyzh0vDkGCwVMaz3N2iI5W5rJ2gp/nI3EeQBIm+CUoTGlUAzlHdCAyFcERQfxUevqmlm+MCbDs3LdtFHVWYW6YhaWNkOzBTmF7reV4QuqcjI5V+EdLTGgWqHZVaFEQxUJbSvKNc6kVSjgkkhF68qmCl/sPscf7X4D//mg8Nx8/gm4nltvSTF+vMPQzStRdrdQIssg7Yvy4sC6+joOHxpjaDjP9mKF2cBncu8BJp6rE8cRf/bi2Usc4bUND5iYnUNLaXeBSq1G1nGYfPEMBwBmOy/j+B8DotOJjdbG6VIFQZCg/4G3SKXkYJsWrh3ieR1mqnM4hscdd+7Gtm0mq1VqUzUMw8QwTSwNMq0yLplr6Xa7B7h8sda3AI92u92Fbrf7EjJNb73ku7okoQDIZB8joQqj/nYGYKYjFXnenELfI7XwW30pOV8Rc/rcdMuAmSmF1AcKg4gReq8CDEOVKlxQqrqWpei5ZqIu3A5km1aufspxkpADSOuMgird7fotOo2zTFYRjyPnMqt80ZQr6xYS9q52HhrKHvVYggXAlK+4YviolmOLAjg6yIyqIkxfTSXu96YKRamLaDZlUVuqyEpfT8sS0pHjCGAYhKJStO8EjJ2gJ9pSrcLAvDqRKfVTVT9aGabC+w6co/vEL/WdtKDxwzsGXzYVdgyP8tEP/g6jm25cdaqYDJBjkMPnTxEbWbaWhvFj8PyYo89N8XfPn+Hg99gAgFzKA8jU1bv3kfPL0iGo73UXuvZpEj3dy/FOdIA1SgK/2gaELY/AO0vod8hasHNoI2HQ5uDXDnBsbIy0ZXHb6Cj5Qp7J56ocPXRgzc94LTyBX0qlUt9KpVKfTqVSWfVcAfi3vtecYg21xVQq9XAqlTqaSqWOzp4jqRtVwmmmDn76JfGPKGZdFCu17E5SzBMrlp7m9WdzEs9Xqyrvr1aQoRa2zukHoVptyhZrsY16PakU1GpB7bYqutGWKZYUnN4t40gEHRUJJ/LnCMNlZpXNGDswB+2ALTmojsl31p3VC678Rtm0oK2ijEidmj2QhELKdvUqCg3EGEzRw1C6AZId0MpfobqO42fFGMWRaDMGi3KN6qq1ctz3o10TwxSK8baNULxertnYmPxeaMLCPvj2l2D5AFIIuxdprDiFTPU6/Ph76X71p3siUno88vHHVzwe/VGHn3n/f+aWW3f1Ckv7RwQ06VDiGvLlMkEYMR+BFwR4XrhCp/b7PVrAzEzjojl9zezTzlz/MJHF/mZk8WcQd/rdG+DHr4WfvGk92zdJGcr2Up6t7npsxVFzHZtSucyWnEsQBExWqxwbGyOOYxEb9dfex18tMPhJ4KOIEfwo8AeIoNyF9xjWCHNW9B24LtWlCd0DEqLnXAT5HheXOTVKojUYIVmCEJVTMeHACcl7m4g2oGELR9/zknLfRkM8ANTfRAKghYHs9FgJL6A/vIijJDOgxUejWLbulg+uLdu5iv3tXuuzkMBPivXiECaqMDISykuVn2ioQqB8QTZlwgTrDBTHKGfR0/owp0iydhoe1vyBkB6ylLKg60PqWcQXK5Ns1OMKWLVQfQvV3/2KtNqDMpSVaQeSNQgbcnJacKV6RAyI7UAxDzfovc8h0TZT2ZM3PcRyd4R7i7/G3ml51cO/8hN87gu/yNeOfLL30Z1GQ9qIrzpkIwiJCMOYVhgStQLCCLaWt0G9yf7Zs2u893s7AqA2fenP1vQOkKyApgNbwC1Xw1BpkHsC8WUMQzmi1gBby0XAYqbpYZpQqpTxGk0OPnOWiYlTOPkYz/OZPL2Ih3gdlYmX2Dlys0inPb86s/JVGYFut9sDV1Op1COI2QfZ+X+g76XXkVTMrz1UijA1ArlR9VgRglKK4Ndr2hmSQKsWcOgE3SqkCmelpNevSrFQ7Ev8XxwS3rwu5tG9BKy0yq8opFsDiZCw6nwVULd9ya1bilTjNSBt0fE9Bm1kC49a4GYwT4bCX7AMHEv4R3lL8ZV8aI4v4rrgK0MQgxzDkJR2ra7UwZWXEITg+pDyBewzXRJPwCTRFoReAVanDoNFSAXiEaRmkNlWHhC5c71V6sR04TrlLal/GIqtFcViifyzQk9uLMrGbi7CSACHziSz150DswmtCTEY5RL8gCbgKmYmNnA///BSwEPrPtJLHx44+ilSqU+RAz76X34Rg7w0c2HtWLnOWZ54+m958I3/kZ3DJQLfYeLQs4SzZ19Ro5FXMwaR6acLrDWd6h5g59Uwff7iIqAOAgzawC03wK+XB/nqVztMA9vzcMtIAbfgYjsZDAyePrCP+skO0ckXKOUG2VnZRqCIsq2cxZsf/EGq1Tq+F+G6ZTzvBP4STAPz56F+8ATBwRNrns+rMgKpVOrabrd7Wj38CaRcBUT9+fOpVOrj6jrdSNIbd+2hJ7UGAXU6cIhEN6+K+EoxdMchlZfXdo8oNmGEcLNtWzHzWkm+P0IMgKb7+r7SA0R5AWoEQUId1s9rIDCKIVIrM4rBzTBYUPLilimGwFKMujgU9Fzt9mlTQPViEY6NwZ775fDdEDJD0KnCoAP5bTBZTVLxhiE2q+WDM4GYdkVK7PEFGiQVh/OAJzjmoMqwpLQOVR2wlmF4c/Kd7IxkSvo5vjrD4beEEu0vqkapy4nRMJGipwaqmhHxMmqdpOt5owF2Q+k2aHBHWbuB+/n0Xx3gMz/zzyumQRN4+HeF1FJmI9u3/iCN6W+tOW1MwFL32POaTL546mVsw9c6+qkZkBiAW9Tzx5DFdjvwpq1w++h/IHz0G2xHdj8P+eZ54Om+95a2yBQslddTqZR5426XyWqVOI4xTchvK6i+FiFvvPtujo9P4HtN5sN5iCMcxyXCohUb5Itlsk6Rw/tkGQ4N3YzrNTl2WgziBCt5nxeOy0kRrtZ34MdSqdROxNU/CfwCQLfbPZ5Kpf4GOIEs7fdeMjMACU9ghiTG7QfAdCrMAfapqmINr/YxhWWxZyTe9wJF9iEB+cL5JHUXR8njOIbvtOR5SJ6zbbXAlVeQc2WXtG1VrYewFDGFuJSxZGakgXkf7AEy7jK+J0zhoRGYGIdO2AdfWOsZtKX6zgqUIFBB0vPFolASzH6IWYcSqvtG24NMDGQhasp1yBUFc6iA7kghC9gDqmehOCigX4/fECuxSwXM+IFkEzRYqwxz20vgENNUEVCNpCbr9YhBiNULei3ctSEI6aUzHnyYZ48c4tY/enkKEGCKOaamv7Wmgo68Bj771N+zc8tG3KxN6drNBEHAgfOrH/PVjCLJAtIwlQvsugHqLybT9RagkN9IGLUo3QQlZUuDEFoLkN0Au025v9srG7Fti1g1E0lbFoM5l5GREajXmZqqcvTZI+waNcDOMDg0zK58gXZ1ilbdw7AMZhotDBtsy6FWa5DPFzENg69+80SvudUOEk6Bw9qyZZc0At1udzV6159f5PW/DazNDV1tLJI0sNHpMD1vNHtCkamjBpgFeoSZOAYzQCafrpk+2ZBFaVviM4EcvBfjIovZQopmQJDytiLRWOkkJiZO6gZ0VyO9c0ah7PqasRFG0vQzY4q6kZm43qYFWVPo+sQiSNQNEWAuJ9/HLMIto4CqV3K2kdDz+0JrDIg8uQ5xBM2GVFcTq6RFASqj0FS6oLlhxHfVqFmsMI1ep1pDMRuXEwOsFn6nDq2T0PIUkUnmJaHKZtj03YuqUnS/FSjNCcPQcuEqSFouBcgUhV2/9174oz+86NS4GHEGxBC4s3OUigXyuQIT41VYhVvwasYgKwt3TMQQ5K+GrcXriIJTFBRTZugGyDo2QdDEzkC+MIhhmZgGzNTnCCMobdvI1lIJ13FImRZ+0yeKY3KFAv7JOk4UQ7FI2ZRai6lqlfLoqHhrpoFRqwMGhpEhDOd5et/XaceQyW7EawaEYdwLT3xWhiQXK3y+MhiD/WehJ2qIlE9ZiA+lRNMMC5GJNSCakrlr6gnuKRpraEBmm/zT92C4KAu4OiGAX1tjBFYC+rVasiistNIkNFS+zpb3xJEAYEXViCScl89zM8JAtIzku1iGGCC1aztqx275UNwFum9naorElVdokdb0KLxefW+tKtRWQJ/KE5kOUJWdxfMECnGGwRinV/mbuxuYUoYzQmZwE4gWIdO/0y+KQIljywmHbTjZ6WmJFLZBoQxdTzCLmlJ8yrtgzssltm0VDelqyPL1cNOo+lAN4GjEVxEYNlQumyK72jCBezZt5o7REYhDgmaAaRkMnru4+s7ljguPEQJbNkH9HGRrpzANAfLuc2FreTOOa9EOTLnPpW1sLeRJWSbtso/X9HBzLpliQaVfCzgnG7RrNchmcWybyPMwgwBKFe4yDJqNGbqeJ70tgzZBGHLs2Speq8P2kR8kbQ7Q8JeZnp3Drs+xvXwjRVbf8WdXeU6PK8MIdJGdKiCZEXWS2FeloqMmmGX13AEgVj0KTZQa7yKdAAbvtGQR10+ClZP4tqgylXoXt6yVxJ92WykDGfLbshJ3VlOFg7aKpSWkiLwGZlGheKF2XFVDADeTFEG1BVRPqzXGtgFh9VlAfkAalapysFSGJMzRpdQh0JKPHtQcU0MckcmqfFXXFaNoFoTnZEypYxWUg6QNTYakSjNU1zgGoWKfXcnXyCrQ0gUmoNVQeOsSxOfEA9viKC/EYiUG6LeVkhNysj1UVyukSjulj90EDz//CuZK34iA6XNnJS0bh3heg/rp7x1HIAT+TFGA56elbv/2Hxpk50gFw4qxMxZh7LG1PEDx9UWIhGuRiW0yriqRNjJiLSMglyMTx8I8rVQwdUrIsqBQIGdn6Mw0CMbGhPwzfoagLkao3vwWdm4jMwtzTACzC2Aff4H71kFxKQlhdBbiYrIuV4aeQITseBrx1umvERKv4CSYr0dczSNCuzUVjz5qIOJAdQHxpXDIUTTjvEzImQbRlHLu+luP94JzU3Zv3ZpLVwqijuc49CTDFSfBa56VWn3LgMZJ2ZJNkuo1jdpb0iU91wPJ7US6z7HVeQ7I67Xfpqt2laegOTyaOQhg5hNuVNZRcgqmhBWeB10NCDZYSeTTHAObxEsHwQxq6vUNaI+Bvw8an4CpJ9WOb8lbPODIEszMwtTzKvTw1fufA47oVJnOiGfQWX45mSIwyrs//99fsVJf/xgHnnj662xxC9xx553cdccPr5qn/m6MBmIzu8ATSKttP+hQq00wM1NnttnA9+cEK4kALLp1n+ZJX0K/2CKq+xCqcFM3rIli4boYhmw27XaPazJYLDJZneKre8/wd9+Ex85Jh9/DCzA9PSeS4ojX0gQ+s/RyOM3n4nTkK8MT0KKyHonArfYeNRC2TT1+Uio2HQ14oTCBEKIelNuBjAL+UKm9RohXr1Eol1VhkC++t+3I9kZb4RAxhE3IGohqcKCqeZRN1cU1OZd84RraJ8+QKVrin4dBwiwMVMBsIndHM0TczUl9/8hmqJ+V/2VcMM+s/O46NMqAPyMZhkhBFRmVKjQcCTPIKDX2NJBX3AJVhRjVwdQEPs1ADKF5kh6RyVZpTM1/8jyYmE1sBSgf53ySYdREYR+oLEDnkFJKLqmfnOYt+yQlx8qNAfn9BpevPDjAjZ9bvTHG5QwPOHZogsBrYtsWW7k4Gv7dGFsR53WmDnG4SBAsEkZzeAtQ2LBMGIzhunUCv029cYrStusIwpBSsUx+KiZVLsmNS1u0PY+MKUVnnUYDy7GY8Txs2yHjZIXeEkkvAZ0hH0dasFVWObcLmYgXA1fhSjECi9AZk3A8ZcquZ+o6lD40vLsPUjbkyur5eWRu5YGWxL22RZKXmfehLq4Wnkcc+pJGdBXLr1BUmIAlraXKpQQriEPIOXCyTndmRujBOpQIhGCUqlTI1ENFvVVpsDCEeFEpFg9Co5Ps6MWNQu11N8tPjNyx4c2qanIAvGW6kWJdOQOiHBTKwgwVkc925HQdR/BHNw+TU1AqkgjOC68EfOEW+HXI+vK1NH7qOpL8iCNxckxLjIymR2i4ImDlRGogu1GbRM6qBdgL8GYHyeQU1sNVJvIlPRJmUl86UiHA5b/4OB/e934+fJpXNe7YciO7bt1FbXyCen0Kl++9EdB0je2VAUzDYPL0Ys/xayzAHz9xhp2bzkglqAuP/X+n8BbgPQ9axIQUNYZkm4RhSDhVw3VdZj2P0Ivx/KaQNrFo+ZI2dDZIpqGPa7Zi119rXApcvTKMgBpxTA/hjqZgUOEDXbX7DaqMX2NKRH57OAKABcFJKOoQwveg6dH1l0nZNswb2AaKpK+shG1JLUGkPkiX8emSYCUqmaoaSfrQVFbK95WcdxECD7wzULpO9A4tJRQSLiYps9x6dfJAdBaKN0ooAUIRDMNeHj5oQSYHeMvQFllC25ZdGyCTh2BKUf1jOe2so04pAykd/5fpgZCOixgEhbt0fMFCtTeqmxTpviv666pkRI91PKl+Goi92aqeHwJu2QL8LLDnOrhW71EhCdBjqqO5JOkfAwYs/tvffoQP3/6blztVVozA99n/D/uZXjiDz+UtjNc6isDWdVAo5PF9n9KWRbJZMEwxCoe/KRmKUlmuq5sDbxpqU6egDFnPI2i3yRcKuK5UHgbtgOK2In67zYzn4/s+cRgQhTa2NUDWThPVO8RLinWxQVTxp6Zf23e5IozA8pLU0uvOWGEEs6eh/WICEey4SV47cQTqSzLptVHQZKPi69XjEIGx/WW8OuSsKbAyGOGiuAv1qjQisQyV61Jc5aAtK0qLhkxNiRehPAkgCc61tmGhAFOedBzWs8+2JPWX3wgzc5KX1yQjXQ/hhBAuCyqfcZTAxzIdnZOKoDEmk0fDFloyEcQL0GX+cUSv4VHgiSAJMUTPql3flpRiUFdqVwrW0MfTZQKuLSHprKccJJKSDh17HkfV1KvTzCJL+o5rofyrwEM3SklyD4XsNwL6b+0aWfQqnIYzPPmOa7j/0aSM+HKGCeSLBWwMnMDgidlT37Wy4QtHAXpNsCzkmgftANfNYdshpmng+z6um+MWXlJ1Z4NkHZOsE5LNLpIvbMZxHAzDII5jJqtVwjDEVhuQ7/vMRxFgEIcGQTsmaAVEkYFhWFRGtrGrnAfXwS2KoMlnPv43r+l7XRFGII5kkml+ThAkGGF2nVzsOAZvAmpLyg3StQNqtKcgcz9JYrchKbCZKciVY4gjMtuukbyW15LVssGURqS6qYGn1Hhr0BMQCQKp7plpQMMnCgLMQj4xFlrP30RwA29ZWD/a6/WBUmZlKBEuy3t66dAQ4efLv3MlScd5nhiBQRca1aROiVhKjdMNOS3tCWQzguDroWuZ3FxSCNn2E6a0LZmnXuViiCRStMK6tQ6sJbkck8jiV/VdPaDJBd6+Bcp/DNx7vXhV3/bAdmFDQEJw0Jo5GijRVEfVFco4zJvfWYFXaARu33Add+zezdC2MrXqOMce/fNXZQQ0PeVi6coYgTp2Xgs3FMFkPYZpYJgGtmnT8n1mGh3iqIHjbAQCVcZr4bourhsShiFpy8KyLLYWi0yruv+J8VMUt23EjV3CyCRsx/j+PEGwzHx7gHawjONC1s5y+933U9w9Ao5Le3yc3Mf/5lWLpMAVYgQClErWkuh3sNTLiuEtQXZJ8U4ycIsBblkQ8J7KTh0yw0BVCHyOoh9HMzBTg5G2chWGiorS69N5tsHg8Ii0DS+XpaGItjZ6m40itdBtWWXhPGYhT+dkncFcTk6+rtiHcSwyZ82XJP03vBGm5ugp/mrWobcs1Xg+YA5KT/sggGZAuw45pXdybExV99qAsx7XWewdohuKyz/oQtkWfACEQ+Ao0hAIeJhpk3SxUKeiF7l+3INBgr4M6FLCi5+EngrvheM3gPIHUKrISrCzMAgDAQnKqUdDPdZBhkmPAbXBB2uKdwOPXOa8MYF3PfQQlW0Fjh06xFNPfPGS4pwXDpU4ZifgXD3A/vPLqxoDXfQ8BtinxQhkMzaWkcE0JK4P2oHo2HiLWJaB7aSpn5zDNOfYXjEoFov4fkvV+k9RLBalt4FtU6vV8JoeLX+OF1YumwAAIABJREFULcWbyTpFVRF/Bjvjks1YbB8aoji6i+Lu3XDTiDqjKQGEX8O4IozAHHAQlQxYYIWqtsb94gUotaVC2LSUJDeQytFzG6b2qQ13BIih9hzE52HqyDKloQ6pURMOjRH5i7xYh6F9/yBvBJgPadc9MlqCG8Cy6Hgegw1FH7ZtsG3pBdeYwSzmBVOwLBHu1CFDA6hEYt1GroOpU/J8fU6J7gzB42NQ3AZOge6Tf8tBVe69vayiEksIOoTQrS1i2nItah60HCEGUbwGfJ9cbjHRHdQbr1YeMiT+n28LOBj4Sdily5WjJVVXtaToA+ckY9tCeOeHWX2H/DQw8i7khmmORxEkYaXAiGVPJaI1oSNCTla/QW0B356C9ine8w545NHLmzcR8P5PfoTb120mb2ewNpiYC5cGwvpHA4FOWsAdQyX8Z15g547N/PXxs9gkxJsSgs4fBB4Fgmfgli1n2VkBwzSIVWNRy1pUhaYxhmlhWQL9+P4pTMMkq+jmM42zTNfPYtsDuDmXUMV5UQSzjSa2Y1DI5SG2sKw8xUKZW3bvJjUyLBNjOYQBODZxubKka48rwgjMIxNNc2Ncku4rIVJ4YQM7F2D7NGQbML8E2avBrgud1VJhZr4ExNAck4uf3SSbf8sDxwuJGouEsaT1txz6Bo67UeJmP6BVb5CZqErWwM0DVeabPoOGKWrEliE5IdfBDD2Ibcjn5PnmWaifSja3ekfxBNROWJ+j24BU8UY4NEa73iEzBOz9WzxPEhSzDZg8KXhjZUi+dNtTEXQWzIx8d8dF5MJcR1qy98ROmhB2oK7Chz6II/CB82BsEBgkXkrYwfOIx6Xtho6yYsQYrGYAKsAdN9ETOwH1u4oqhF8Esw1XlUmi6SKSIUjzMn6yWgQjl9NUsG/kgTtGRyi5LgcP7CeaFfk03YxjrVFAFn6HRJypNvECpU0wVChy365bOXrkWeLjZ8kCxXVyjXTG5AmgPgu2fZaSwntiw0SryLfCRYqGy87hAoF/ghkPJsZeYuetFm7WxbUcJqdeojaxTOCfwcoMyJUIIY4DIkvcuXnbwi0X2ToyQmrPbthWlhTOyTrNeoM/+5PPvOZMSKrbvZSq2fd+bEylurLkVh85EqMQIzfsFqRyK7tJXmNZEiZMjMPxc2K1d90iiYCMDVsKkCutp9NaZNDdyNihOaanYHsFrAIEJuTz0jzD2VaU3RqI9u2HuI2561aoV+mMvwTzYlgyQ8CtN0u48Oy/yGxSZdFsQ4yAsV58+sZZMAbpVDvMelDcoxxMrwPFzTT3nhWXPJK6ApwB8JeZOCSRSGEYOg0YrKyXFGQEWOtpHFjE8+HoUXHZtc5PmhVRAEBPwBl1DX1kIejyBO1AtNT714qPfwv49XeAqftZ6HA/T0I+KgzA63ajCAMk5YXaVdGfpkQUFqrw5ON09y3ye5+GDyys8eEXjF++5cf42K/+H9SnJvi1D32E/erIq3kDpvrRc2knYF89wFfPL/fY2e/eAtuL1+HaFjsqFayche26TE7N4Ichx6p1/uzpE9SA7ep4O9fBbaPXCLbli9UO/GW2OmA76zEx8P0OGLr0ZD1WJBWQYRxRa3SoKep1aXgQtzSM7RQIMbALRUbvfZvsCgMW0b/V2b93LxbwuT/5NI98818u70LJ+Ndut3vLhU9eEZ7AJuD/RBhYn+XlFrzJy9Vhv4pMq/w5uZnFcxCelim29Wq4fZd4TU2ld3HsOQiMRQHI/IDQEKJNzZM0S9a9jhkvIOvYdPyQwboHhSJmLk/zOZ/cRBUcm0FnIzgWnYkzSqnnBIzcLK55cCYpfmoD7nrJEoQtsNdDtUMcQLGEEJniZWkdhkyAwYosfAob6VZlRxsaEWEV3M28+LWz1J5cpK128haL7FfXYQbxprSCrS7V0Qs8oq9zOWJwNc1C4y/9dNu1qLc5lGOvA1FNAUiTuA05gGUw6qqUWJfeaMaUJrPqOEJVgmEQBIuX3erTBHaOjBARc/jIEaokKIQ2Ag6JZxkgwKbGjqsA5xOSUgN4Yhbu8U9xy9Bm5sMQ27JJ2RaVt+0BLKY/8ac9gHBSfV64BP7BM+zasZH6zDK2PSC4rw9hfRHHWSSOwDsn3qvjLJKOgHgRK7se7AG2OxBEyxi2g+UUMJw8tmlSLA/DzTvl2y5HPPXkk3iNGQhCnnplBmDNcUUYgQywG1nMO5G460sXfQe95pRTSBkBSNupu5Abc6wqZL8wBDeCaU/KCErFzVKjH5wlX4bpk7IIshkLI46xXTcp6G8HYDk424r4TQ9Hy4tjMFjaiF+dE2lBHcQ79NS0GEG4Au56qCnOgCE5ftl2l6FyPWRsuk9+i8EyqjjJh2bETAMKd14DTY+ZiWVmnj3Lo9+UQhDVtBlPXa8JZELmkSU2jRgEnb5DvX41t/GVoso7gf/9JoQQ5KkDqLqHnjCsvg5uCJs0PfIkSYCnXYYCPd/kKqCQI1N8iZ3DyI5wiVFkkGIhT0sVjj244wcJIzj+/AT7FQ1VayDr2sVL6Q1MAENLUKudxc5USRdMBvMOlPMQmbz9nQ9QqpR45NOf59i0zEMfueZWfY7gPLjGMmkHpk/LN5+dTWzkzHnInofiBtiSG5Cqd3+ZUuUarIyFlcmSdlwsx8UtbKMwpAwA0K7WaDRmcDI2h488e9lZEG0I1/K0rwgjMEBSRVxA9ZFHdvsvv4Lj7Fc/ufOw4zzcfhpKm2DGk/ht5+ggXgCO41CsBGxxC7j5UNXoWFiOJZKDliXpvlYAaQvTKWBj0Ql8BjMWnUaTwUKOrDunCnNCybcZJBkLdwDGlul16g0RF/85tfPkNopi77NKc6VyowTvEVT3dQgCyI+f4a+/CJMLSq5afc86SRiuHeoYec183ynMc/ndbC5njAJvBwbvRnirOpYYQ27chXUJtTNg1YWg8AM6ZtBkY107Dj284LbbAYPsoRcY5NKVgDYZ4iimdlJIV5Ztc/SZwzxOsrsHCKEp7jvdi40h5JoF58Gpn4KJkD3DRRGUMW1S99/NLsti/6H9uNNnaKrjZoC6olT7qrxdQ586QtRFPG3AWoB0CLZrY2UCQtPCdfM4bo6M42DYDm6hKMDxaZ8gjJk86VEo5LEti8OH1s6D6DIRbeA1cXutcUkjsEbfgb9GQiIQb/5ct9vdmUqltiHlI1pw9Zlut/uLl/qMAcS9MoDUOujaEtPfE8B9S/ApLi7XdOHQ4cMkMHROFkkJqEx18FsdhoZauK7oCNiOi52xhPZqO8InKFdUHYAjZbUZB9POYjYMiFuYdobOyQaDzka64RypOAQvTOjKJYTuG6sT7yFtanLOA805+NoBuvVl2fW/9gK+J3n/x55RSPRx2VCnSYoJNc1GA3jasYjUc7q4R3eo/W5W1G0FHtyBbJeNvhPw1AdDQlc2kRAIFLPSJild1DlKvSz189vgNhgZfYH7nr64NzjIINtvqhDFEY16g8nqONMNn1qfAQC5Dmt19F1taPsVIWBqqVhQK9iDm11YaEPR5Z49e/jqV/9CmkyRFL2WACuE1lJST2Wqa6dHb9EZBpadwXQcDMfGchyyhTxp28XOFTAsIc1M1gXhtZwsW0KX6VqNI+dXv7N6LrwSD+9yPIHPckHfgW63+3b9dyqV+gMky6dHrdvt7nwF50CXpKtdegnmzymm+VaopGF3DR5Zgj96JQelVwwHyGZ1+JsKVJxexuEU+Wth+/B67nngrYRBC8cRwxB5PmbakrubU/r6vgdFSaabvofp+HQ8DyueE/lurSpk0KtzOD4mkgP1BSitkx4o8yGkHYgb4PvLxCpHf/QQ1M4lYJ2O2Q0SL0BH1DrzrhFuXaYDKyf7q63TX2vcgRAuo4MJbz2jeE69CkiNPDqAbQj5YIPWicuRNJCAFTXh+MnZv/+n+O/xPh773bNrIvxb1+UYGi7R8lscO3KEwPfZWigQvdhgvM/0KekJjnLxbEFKve4YsrsNIexJ11EmIWzDOV86W900xMj98OlCiSe+sJffeeLrtEmqst2l5Nrr32nEQKCvwJZBLCeHkZG+gZaTIWu7mGYGsDAthyCMaTXrzDTaGJkMtm1gmVB7bm3/7pWkR/W4pBHodrsH1A7/spFKpVLATyEe/KseyyTWt9+bZBpYB0MGfAL4ZWUIPv4qP0cbBZ37zZ2GW04vMm8cI21CpZIQW4pDZTmZpkrY58riHThpqS5se1iBR2pMrXRThQPmHLSliaq+ISHw1BJsfVHVRJ2GeJ38z19KOBF6t59FPAEb+Z9qoPayfVRTb75XNNn+UURwG//5RCZhKxCdB/MG9SKfBP8zAKsDr1NWsZfj0VmC/iNPAPvgdB1pRFikfPetfKW+j3sffblKUGHTAG+5f4TbR8oYXkAh65K2XdxCAUIon36BEJmUO3bczHS9inl++aJEIt364idJYEsnu57Jr32dXbSStmyblKBFwSUcn2CycWSFsW2QAK39NKm6+qYukL96M7Ftgesyb1hkbAfDkIsW+REhEWQi5sM2vt9iayFPTIQdR7gFB+/1xUtWBr6S8VoxgTuAZrfbfaHvuetTqdQYYhg/1O12D67+1mT013/r6aGBP5bEsmaQXO1Hl+Dd62D/EjyFWO5XuwiayDFaT51gJzCjpK4bjQa7222yjkMrCLHdGYrDFTCiBHq2LFKmK5VMvgehLziCEQEdKIqUmOnCXQE8+oSEJxoemFZFINMkrQY1Vz+NTCKN7hdIdngNcjmIsfhu7/ZrDb2jTpD0GQLF09IzvAcIkhRO9YIYSKJkt+8IfYTda0NpKd/2wWqz54ERxkdCDjwXU/qRB7jngXeAPU9qgwYWI7p/9yXihoONgVsucXzq2R4+uWPHjRSLLo5jQHWKidnOy8KCDC8PFXo8q+cX8etgWSfINkMKpiXu3KZRuKpMqVDlttFRnjj6zyvmoL4n2h/RC3YGGLr6GvL5PFZO6MRRHNEOAsL5EM/zcJwsWbtAy/cI/AA7Y2Op/g9Z28K0oVKp8LEHf5pf+dznL+/mXWK8ViPwTuALfY9PA1u73e7ZVCr1BuDxVCq1o9vtviwkS6VSDwMPg+iSa5Z5pE5KM9Y0uTQAjCV5vrQE70H6ndmbYOIcmOvg/16SzMIrjYc0WeTw0TncTXO0zsFk9Z+47/6bOf7cFA3v79k5cjNDwxVsR9gKjpsmCufJZfIiCOjVVOwfCWHHGcQc6UjFXoNeX1D1it6uP0/iHKsKhh4NVO+hup+PLsfR9Xc2392Y/2JDXycdxXuIJ2BBkukbJ+kh2UYu6jlPGr0CCYyZIVFPMdRj5UJcNQXWfvnXUIWhTW9liDtJVA1WjtSPP8xdP/4wMAb/eAjjiwEOcMfVsHOkTKGQx2s6HD70rVVxAU1i1gJKx0nyFwZCVQ9DaNfqkBmjYFhwvwUb7sS87W7cvQd6ntlaQxtuCzBsC7dcYHJqAsOHOIqwMxls28bOyHecDwRcMuIQCwNLfcKgqj8plsu862cf4rOfe5TxCzCQVzNetRFIpVIG8L8Bb9DPdbvdBWBB/f2vqVSqBtzEKgmfFc1HzFQ3rTYLbQT00BdQEzNy+h+bwDknf46sAyrwRVWgtndWWIb74ZLtpzVJRocKrjqmuQRP7xOt9jiEFyeqtBonyRccTMvEzoBt20zTpFQo47gWUegQtH3CYD352CLVENbgdF0i34AEO0ROlTSJbIJe4Jqwo8G/eRJX0ich5gXqeryW4pHLHSHiyWhBaE1K2gq0zsOuceBuEldOr3U7huUABqZIHFiNlOZJ6gh0RWEGBvJwreZ/Z7g8x3cE3jTCBydG+flPfoLjY1PYtoHXrNOY8Zns07zWGKZLkj0wESNXIjHShauFOBoEYLtpjMigO9Mg9eSXYdSDa9/Gnp/5Gd7y+S/xmefXXowtxLO1gTiOaPk+x55/iewGIQ75LR8n67DDGcYwTNJEZMyQtJPBsS2yGUN0Bi1DfgwLw48wMPhuiKq+Fk/gPwLVbrd7Sj+RSqW2AH63211KpVI3IH0HXrzUgaIY3A1SHzCDTHjd3EGniswb6JUM98gpw9Dr6hhA9zSkNkjf9yEknXUYaYawVmvkDomrpr2OrcgNmzmf7D+N2WXZ4YMOlj2Aby1jmNBqD3DMPMLWokulIECA58V4U2ex6sqFflHcfr1jaBhMewF9uinM9r1OO8zpC97TIHE5vx8GAGR56lJirQagOX+36BdAAlpoyxotg/MS3KT3XA0BV/uOpNVVlLIp8yTmvr8CcbWh6VB6jJB7z2fJMQXPH6F9YIzgHw6x82oonReh0MlziZKaoz5dk4wsenWN1M9D1hMekxt1cN2A1DZTQMJaVcKXG+6nuK0Iz7+05rXTgGQaqM/OUJ+dEZ9oAVhYxGCRUruDm3OZD0PcMCQOQ1zXJYxDbBPRH4xDOgYMZhyCdvD9MwKr9R3odrt/jnjjX7jg5buBj6RSqRjpJvCL3W73kmGruV5EMaNZ5STuAI6ryq11MLhEEi9oNNqmr8EmoOp14oWk1t1GDMKbkVj2/bBqnzhtBLIkEaue5AZyTukNqgYhBINlwpbICbiFZYquReidohF60vLQl11hpi7aB0dJomEbIdxoNZ7+xYT6PK2VouU3dNgQkGAF301g6HKGPgcbWTy6CdQb1Tl2TsPgKD01I3ykMsdDvlB4AgpVsLOwIUeiJe/AsjLBUSz38yrN8dO1BXV1IJfk7uihDYtBolGAfPhNZTI3vZU38mG22BmOHjjExItzPQ5FiUSESX9HfaQQ2UDi0/JV7gKOxC+QzVhkdo9IUce398Hr3smvfeADfPirv3DR65dC7t1RlmmTRE2BujzOAnhND99v4QcejmfTcl2yjkPQLmBZaQw7Q5xxsMOIMAx58333c+Sp16YlAFdI7cAt6VT3qL5/ess7D6yTCjfzavUY4GroOpBKQ6cFrVl5S3ETVM8lrqpmy2lBjCwCIv4n1ibQDCKofKXvvXp3KF0NWWeQMOpIFZ6RiA8ZgKNWc2MK5hfoacEFJJ1oQsQoQYLshyQwWVq9vv8nJqm507uVxhO+n3fufYgBOKwe6733N5AaDvMGkrhmGwlQGCHGoKzekAW2DcKACd+ek/XdRPGR10sd+FU6xdDfTdVAXD9di9CPEfgkyIk2nxUknEDe/7lP8MjHPsrB452ep2Wo7zOPhJvFdcDSyiPr0ABUbcF9G9ledjGHi7D7AbjpvUDEx996G7/y2Dcu+3rqOgZ9SfLq+M7VA2xxbMy0QT6Xw3ay5PMF6XFp2cSOi2M7RLFF2I75nY/+Pl8+98Kan3PBuHJrB9hALw/WbQBLkLoBCERRt8d5VZyTFEAoi3bwh+itmIpKu7EkC8fs+5lAbvb/C/weqzMRdQGs3sgKwPw64elPnAdDETQswL0WXHc9UbDITAOOLvT2tZ6rHpMsWO3K6xojHTprXoBO9cV959DfxFHvXt8v9//CoaVAeu2xUUIwqHIBrZhUUieZR25CDvnCJ+nJr5PpAIPilum8nQVky9A0wPGBCKwmbHgWzrVgky7s1Q77rcgi17UIZfn/P34BxltQ2AVvf4ieqb3f5e1jZdLht4jjjdSm56RXnzr/Y8DEUoITxIi3No8YZ/2dD0zM4QcGu52KiNW6j8OmIX75U5/i2N5b+Su9WV3GSKtrGtMTqpcu1uEcadYTtU0yUUhsgB1mMKwQsx0TWy3SGcknvf1to8R/WWPvwqsHCK8MI7BMj/uacpEVUidRqIpISOAaCdOeoq4/tpBtVm0GGY0dzMrLVJ8hppE+ah6rg4YNZPFtVYd1lpKP0aB3jOT6i6cXe0CeRpj7XXZIPAKdQopI3FAttJUnAfwskrRhmyTT9u+1+PWYYSUk4yC7pzYOeKJbMKgRTA8lNELCnR1RL/4aUj2pLeQupDS6kIXYEh5GGAovI1eATSV1EIskRwIr92wXeBvsCsAfZ+yLexlp+HDvqDqZiMydI9zlxzz15SnsDRvZshDwRpZ5DDmVIRICpOaS/KR6XFonolTPTMPtQyaNapVC0YI7t8kLX1fkU5/8CFv+28f4+IuyWVysnFmTKvXQ0KhhJOD4fBxhxob4QXGEEQrYEsSRRE5RRMYyuOfu3UR7/5n9FzT8u1Q5df9n//sPXWSmU8pt6HUeArnvFklKGdXEU9ef6AnWn5LWgd21wGmZtDrVCPBB4L2szjHoIjZosO9wOgbXQFKdhNSjd29tIDRXhr5T0gCnr46hLb9eLy0SYq3uwA6XT3f9Xo8QuUW6RFmH+qD4DKdVFkODKiAn30TuWxGxum0SbFA/bwKuIdqPsSma8hgwfCsM7EZuboFkieo6vguHC5s+AKNPUmxYHDjwZe4I66ScQHVdLlJ4fZH0gSnMKGJ6YVmZh+R000h4UwLuuGEz73v/z9IYH2Nmqoptwj2VkMo73wzzMTgRtE+KvP1AgcF7K/xB7gPc/sUvMzk2xVNHz65KUDL7rh0k6V77atEktBwHy8lgWWnSVgZbt2k30mA6cr4RSkDSJOsWGdr1w+x/JqkqHJQreFnz58rABNxU9+ibSXiyWjhA3yFtAAJUpyGS7UjPzv7sk9bF0kwbSDwL5a5NIYvt3kucmya5WqiYTT2vMex+hqPel2okQJ8OCfpvhu4PqvEGPXQYoVNKV9LYhSDBeulpXMUkaTMAsOMG5c2VkS+pQ/gQ2IfchzTwH5ALsYfEkBc2S313pqC0CMoIjDr86k76mb8iOvCnhCf/hcyt14NVgsBl4h+qfPKxb/RaaQfAg5sE84mJ2V6psHNkhMD3KYyOwExVxGbbvqDD44dF5+31DuweElm67DaI0yI/77h06y3+7i/H+PLef8a01xMEMX64jO1sxvNbmCwL3rUObHs9WceRAiIng+HYGJaNpbQIBX+KMQwLy3SJVVMcQ3XODsOQWr3OJx/9i16l6CCQZYDGSh7BFY4J6NJUnQGYgBXtuEKkNAn1eIhkRZokO05VHUNLj+saTh2sq4xTOYSSBV+bhjsvcmp6d99CAjnpxesqIU6PJMWnCT9DrHTr+4e+Udpd0xENfH8R/1cydIpTewDalTWQa6ObE2+NwQ4g1UDCdn2PtEHWv4vIhTmC3K9bByHMiKJKzwCs1gv3FYzKLmaeNCgO/yAUFPIczGNYMTtvGMR4sQMb4J67r+eOirRFyzg2vucR+HXCMGT/n36CWnWO++6+Htsy8KZe4NiYpPfca89we+MkhZFhsEQeqlmvkytXSO3ew1vufidvOXSI/U/u4/ChCQzbFU1aS7YI27bBANdxyeYc3Kz8thwHLAPLMHuqxGEsqVDDyBKHsRgF3bvAD7DDmMqOm6kfF25LB+hcJpHoyjAC60hYOwaykENkwuRIVkkB2Y4LCAKtPYY2yerU2IHinWCQpJw1WtgGPFmEuyuwbx/cfZEG6h1kEZgk2YcIEZPQnm8e2LlBGk/MqNdfqppL7/aaGRlf5LX/3qNJwqTTRhH19zRyHW7boUI0G/HOhkn0y/T9iKBdF7UnTAQwLADZW+GG3SSo/mq9dV7h2FSheO9DNJ79KwoapMwa2JaBbZuSuVmAfPUl8mZI0G5z9EiHxvkkOzODEL2cx16ighjw2xFDaJ+Go3/U4Z47/oXb9vwwgwUTy1SaE74HN7ThTSPcVXB5456Aw0eqHB2vk99WwbQd0raNaRq4roPrumQzDraTASsNFhiGhWmYRHFEHIfSmqIdE4chQSjKxfF8TGzEYNlsrZQYbjQYPze3xgVZfVw5RgASlKlXwYFcbb2d6rTTNpItM02CQusSO+1/a+9AjHzio2vuiaLk3bUHxo/A8EVat+pahgyJDmIWwQUMZCG7C7I4dOHsY5f59XXxypU+jiEejvaEZkiMgr1OQtT6OBRHEffKuQayrpRO6mYtpRfIaFWU/PWw9yUoXwM3vBnV05yVBUZrj/rTdYojRSlmX2v86EMURodh6nGIT8qZGzAxMdcLySZfhODFMwQkPRV0dYIOGXwkkaH3oxC4a51kFD57ENJ2laFbh0So9k1vhQU9S4Cbi6SKPkOhhVscwjQcyNi9WD/rOKTSynhkbNXKPRna68oAue9Atx0RtFqEUcR8ENBoNGgTYTZtDMuC/yWNgG6L2k+ON+m15AYSxZF+jm3c9z6DxN3XHkGoXq/z1f2QrI+sagXvD2Vg7huw8RIdcvXp6B1e1znoyMPpe+5yF/blorj/3mOCxABqwN9DLYwlOHgUdvwQ8ABQuAbSIyKcMqBLoxwo+zBQQSzzA/DgF+A7DfU4y+UaAIBqrcpnv/hFfuO//irmtRd54cAuuKkE7IXTHuRmmFHfR4N02sO7S323vBIWBXi871BFVDfireDaA2w3DGr1RZ7aO0et/nV+5ld/Gr4DhJb0tdAzwmiTKeexrTI4RVK6ZOKVtgS+ClJXmWSudRMJh+9UKNYDMsUi6UwZa98hjh0fo8PZyzrklWEEtCueJ6my0WV1euHq9KA2AHo16sUNSSmr0/c+nWHQ6pn6+b624dr7yNhwCrjuMltl97v6HRL0f5AkiXU5438FAwAJb2+HehyTgKbZDdLUyfklYPgaUUe9dheJBVYQ4kABifdtoAHLRaKxKmbFg9fBmiDgggcb+tUUYM/P7+Hv3rqPz336SX7ug/df4uwVkOS0iJ0pDi6tZI/WkHTgHTfA4Rfh9tEBArJwMFlIGYQ05lhQLK1nq1P8/9s7+/ioyjPvf+90DskJnpGchYlklEEy6hQSJUJYSGl8gD6wCitYy0q72lJd3W3Vldp+lBVZdbUtuhWrto8W60ulFd/QiOhu0GAhGiCJTiABggR1kEnIqBPIlIxkxpznj/ucmbxK8G0mcr6fTz4zOXNm5po5c65z39d9Xb8LNI2JJRHchbrsZRmM03773TgcLtpCEdzLl8JpPsh0wzleRI9Q8BfEcHCP13CPn8X8i2cCPQVDAAAZi0lEQVQlNnd0yZ6noVCE5mA782af2u/T08cJaCRTgD0ko/nWSWutu1m/KWthHpLOIp/kdMBaUdCHQYYKow/LfARLu8J6bWuia8Yc3C55YRr32Gdfm/+qKvtSQSWyViAvU86nrURej0d2RZLpj2ahSyITIoZcXyiydoBDjbL0OhBFyZslNRlGlvT7nrFdfqLhEE6fD0Z6ejzmcrloDjbDIT59WgBwRKGjOsrm6mgPB2D93OqAaSFZKBWq7GLy5KQrzwZ+MiGDcLiLfS1w12udRNjLVODuR38AHifk6nRUb6Kyyk9lZQdrgZ+Vl/PTV16AM6fAl+EAPoXsDMgeCe6RGkXj+6/ChOMfjHw5ZJDMo7WqZVSS6aY6PUvQrSu8TjJImEvSWeQD3mzQsiHDunpky/exnIrlTKw0PQ/yN3oeZC+Eg5Vg3PE51VK+hmzE7NKmJ2O0ntGgj0WKELqlbDuhkDzREyFFJ0ktJGCEDzJdoBbS2tDOyptWw9v9y4Aqmo6zuJj2pkY42nOSFY1EeWrNGlavqjim7Y/+toYRMy/kh39+OLHNbNsIwKVnQo1ZNLYHuKa2g/2m1TnAnTu72NoiFzSsGoN3gJt/9gTPrngQmoJke3w0BDoSs82r93fR9MgaeN9as04/0sMJCJIjRJVkLbrVo8KKPkHPdTofMGs8TP97mP4dmH42nHMGjDsDhrvlTh9G4Yi1zng6DD9ZioBaOe7WeraVo5tvvm4esBAqtsBH13/ZX0B6IJBXvFykT7Rm7l6SbUN8mMuZajJmGwpLeW1ZOqzJTD+QGgsfNkJX9xqyXlfD01zkeksIBOG+G+7t166OsFx8dXrzpVR7N1wunfpDf2X5Tcv4f3c986mfL664iPWqurOyOUuALDWbuZNh8pjkjPNZesrUjUGOGuqRqwYTRsEFC88m7sigoaqG2MdxZi/4Dkpm8j3+6zdPQCw9Bt39kR7JQl5h1FqaYVYZqhXYsxJJLFF5KxFFB3ynwPASksOCAOCHDwMy/3KEDkfMKp+MLJIi+Qq80yjFQJuQESINGZy20l4zM+CIA1o75a+/AhbOP7YUejogSIZUrGVNsObw2WgnOXHpLjRNRdN0XE7NTEpxoCgqmuYkS81CVVVQ46aakgrhMEpzFTS9yqiwTM3WIlKCMe4Azy+QxyYGfPMU2H0QnNng8cKZ3wNK4IgThhf3sbnpKT9XLFrMprbt/Q/rjwSlwkdG32GtVLmDkgn/yOs71iEGurQdApEjBngQrgQWfTuDyqouJs85g/tf3tujBF0gV0e6i94WAk/f+l18JfnI3m5gtGexrqKBBY/JCj8P8N4nH0F7VGoUpgghRBonC0FypGTVCThJqmp0L7my1t88GTDcKmexEgjMpNxYQHYeDkXA5ZbrrtZ1rKsZGpuk9HcE2AQdNbLzj9OP1FX2YpYre6S+YKYGxVU8sx+oOoBY9BV8H92wZi/dY6UqcllO005GVTQcMRWHCmgqqlNFU60TW0FRHDhUjXiel1G5LpyqRpbmRHU4UFWVHEVFdThwqAoK8taBw/zOI+CAj+Ma0fYojpCXeHWEfeXbiIZhlCqXBiPW1MpaenWazVmiHdDeCOoWiDQSa81FmeGTo4OM5Pzee0kRtwR+yXXf/Qn3bnyg75cwfOCTp2L1DmZddjZVOzez7vkq5l/cf2yBEbDonG/x5PY3Et9r9wG6Z1wGW6q7CH4C/1d38b9bVnLnvctY+uQOIJlO3p16INDaiiuYhaY5UUrmIBrCTP5WLouqojz59ovmc1SzCCr9SA8nYIDVVDQx7DcTehJr/rkkW1ypmA35XCQ1rhvg0EbZ7CJy2GyK0WmKgGrSYRwJwfpqOaE1F4CrymF6i7w//234dRP4ZgE+hwwDx11wZrFZJrcFLjkdY6GD2BP7UNwLqKnJYcmNDx93N9zeZGOJmQxDJ07OiBx0p5OsLBVXrgtVVdE0DYdbRdE1clQdTVVRYg5UxYmm5hDLUvhYhSyniupQUVU5FnCoDhyKyseahxxdRagc90Qw+ygQjAIeOtw+VG0bMcVMqgnLwVZwPbitkb/lqdQMIA7hVgiGUVRzHTgahuHd6ylh5g3zeGHTs9z581u48e7bBm3bzEsLuaPxYW7+5RW0W01hB0CJtiXu956hL3+niwJgKhCJA4rCjWvWMXfOvRT++B5AJgpp3xhG3SedieBiWyRODJ09gRCxhjUUXXYdbpfCinumsXNuhVQ/zvhqg4LHw2BERU5Dyo2fgoyvrzIM414hhA48hZxZvwf8k2EYbaYC8b3ABchA+WLDMD690DpDgGokK3GakcN06yhZxURe8746TEpQJQIJIdjVCL/dJf2CHyl15QJyD8sXjfvh8W3mGFmho/1UUNzURrcllujKgOBWmNsI/7ygk3xnEBFyw5lxyJgCtMGu9RDsRPGeC648imd8nzdu+AW8fgNLv/0ij5FcVXBb5jLMvIKr5JwkT16X6kDP0clzu6SYZJZT9q13akR0HVXTyNH1xDBd13VyzHRSVAXhcMgovEORadeDIHtwu/VPJrI7EmGUuI6aezaNjTvYE5UOYIwKoWaIlJtKAN80D020C5ymTkBUh4jpvIdbJTRWSZXk9j8u5Z8mTefaH8whe9IAV/R+WHbH5byw5hE+qPbDTxYMuN/UklJWvy1TaxeMPp2ylqQikIG8skeRy4WEZW10weKf8++/XcN92w8y4ZwzuHjhLEqWfJ9YdT3DZl6DQ3OT6y1FaQ2xpaoKbWMF3uIpeLw6v717Kf/48/8c9OdIBYMZCcSBnxuG8ZYQQgPeFEK8AiwGKgzDWCGEWAosBW4EzkfKip0B/D3wgHk7MBlG4mKekAuz4gJOkqMAa7SgWxlA7UAAtjdBeQM16yHcAjMng+JHZq25MmS2WlMjeDPAMxFcPrJrmgm80UToUE9TdgLXmvr5wuuBSCtsr4BzPEAO+LxQvQv8b4HjLbgoDtMLYPpyVjRfxoXX17A1GIX8KKN0X7IIRFFkz3pVQ81S0HQVzanh1DXZOtjhkONqVe2TMZY26C5QNRx6HqM8PpyeRrZs7yR2VIq9qkD8b1IW/pE/wPwlmE5Xhagip2ZKt4KPFj+MLurxFs7RPn5z//1UVtcwO8eFGOdlsPz1ldf57ozpLN51Gfr4/p/ncienIIGWIEUMw9+PRFddfSO+xkY87iwYX0Jebj5wkLXb9+Ie66IkOBdlxhz2PvprXqvYTCwGzSHZKWjn7kY+jsGEkvP49qxZLJ5RDociQ3c6YBhGC1JFGMMwIkKI3ciL3Hyk7BjAn4C/Ip3AfOBxQ0YctwohRgghRpuvMzBWX7sodFSYdenWCMqqtbQmxJr5ZXY1wKZ6qOqAoOzY7B4NinUJtkrbwiEY6wJfEYz0wa5WaA0SCjXTe/DYAby2HfJzwRuNQIFXVo69WQ5FmgxM+YbR8UwnzVHwlgThQweMjMLoEkp+4WNaey7R3BjZmku2J1MZ9NU6rclQoHAKIq6gxRRyQmFi219lHXLg1l30dME78Ptfw8WzILdEhbADvC7QrZqAMEZzE6KXEwAoOG8egbJneOrZMi6YNwdnvhcyjz2czh4Hk4un8NLzL3HZ+OsS26teb2Rivo9sJ7RFk5MAP524+5kXBYC1Oz8ir8qP3voes+cpoLoTj23Y1MDU8i2UaG68JbOIO1Saw22M8niJ1jfS3Bwiqqi0K35cLg//+au7MKIxxLHyGFLEccUEzCYkRcA2INc6sQ3DaBFCWLU0buD9bk87YG4b2Al8ghlhgbAfmlugQCVZgmqt/4eREfxMFbpCUN0EoY8hCrE22H8I1ENQEEROH3KB9i4ZoSYKI00Tw80wxUd+OIqn/ADuT3rqCtQCi2OARwenA3x50BSQLY5Hu6HQS7a+i3gTMrtopFW+6IFJCxNLbV9LhmswvZRsXzGlHh+qS4c1T3PPB32Tq65ugco/ww2RgxTpUfBoMNLS840jfG44EjADvJLWN4Msuel6JhYVULtpMzm6k9mOOOLMbs6iiwFjGjctv5klN1xJwRQPLreb+//wDHfe998APH7H76is6ikl06vUNpE3kH9SBnW7m9hZEWBfUGF/MJk4VH7oMJe8F8btb8SBSiTuoK6mHpfbzVnnTmHnW/XsC4TY1+pHcwa45PKrEKMHEkpNPYN2AkKIk5A1MUsMw2i3lmX627WfbX3WIbv3HRjjBKMGtlSafQWgp4xNjGRwMA/oikJ9ABo64Q1o2AhRh8xmiwKxWvheAfLy5ALaO0A/Wa5ZO1wwfSbsCuBQYEw+nPV2TyeQBYSCgMcFU4vgwyYo9EL9/4AzLvPCC8FRD1RsBH2fqaZbBywc7Fc6tBmpwgVzKP6HORR//zZmV1Rw/s3X9DnQTwJ7XoBrgy/yY4cXLi5BJgwFoXUfrQ0N5F54FRyFl1aUMe/Wi+TzNjyNk2FMK/KSn6Pi1RwQaQOHBmO9cDQLMhVqXvbzqxWrKKt8kI9qO9En6dT6q3nqmSzOKixMOACAH958DdnHiIjuQf7+dv6ti+bGAJ7CAvY1R3is9sUe+/15TTlxVPK9+bywvow9DY1MKylhxqxZRFHYsKlGToOyNCbP+R6553zeL/zLY1BOQAihIB3AXwzDeM7c3GoN84UQo0nW6B0ATuv29FPpKZcH9Ow7UJgpjMcq5RU4H7P2yqoCtBa6reH9uNPhSFS2/TYLhM5ygcMDG9+RrxEF2jeCcywyQKgA4cPQVA9T58llwmgIVYXmZlkK2x03cmpOY4N0AiN16UC8p0D9QXAehjzQrQqO6mo5r++nAuxrTwYw1cecqT4m376KmqM7EoUtVnmHH7i8Frb8yz2smjJT5tJvryFcX8XWmhgTwhp19RGWr/xNj5dup5M6vx+NdpTIe4xx5VIXaiW/uQinywu6jw3PlFNW+SAAV/xgMc/v+AsNHxzA/9AT/Pul3+1j7rFq7LtXi2bFHTi0XB7Y8FyflYSNLe+yceU9lI47gzy3m/L9B9iy/2mC4SiR9gibWw4CkMvJtEWPp5Lkq2cwqwMCeBjYbRjGym4PrQN+BKwwb1/otv0aIcSTyIDg4WPFAw52ypjgGOR5HgeMOAirPK97gJAccETAdbKpQ9eJsgCIJuWw64DZUan6lND5VgHPWPmGjfWQV4iywI1v/YPs69UaJQycVXiy/HaONMHwsaCpUjXGGp3Epf4FuSoUjpVlqoEGGH+sb/Try+Krr0JfvYrfrfwl9/9mBX/c/kaPOoqHDoHvZ8u4/p4HIBojEgoTDIQJBst5eX09jfRVzV29dRsNW7cR8J/KRJ8XRZeFOdGYX3ZwfjYpGVv29hNcd6EzccLe9+fn+rzeYGkH9nzSwZ7Xyj812XfzO3vhnb2J5zy04UWcJCtDL7nyauZ2K+pJRwYzEvgWcBlQL4SoM7fdhDz5nxZCXIG8mFrj4JeRy4NNyDjbj4/1BnGSvdtdyCo1EQPjb6ZSTxEk2r5SCEoQlDgU5ICrDcJRiEKcg+RhprhqJPMKLOUhRxRGu2C8KTs0upippRUYtX1/fN7zimQ62HAzSynTDSEHRDqTmmEewBWUqxWBDumUTmAn8NO7r+and18NwJXfLGJ2RTkbnn+WOn81DUc/Igzct3YHE0r8zHRF0aIqbcEQ+9rb2He0P7VHiR8Ibz3A/kCEyb4iKqvKmThrAS9XNeL/27Ye+9634cEv7PPIkczxqPgOo/TMOdy0fClzLh388maqGczqwOv0P88HOdjuvb+B1PAcNIKk7LIO5E9AnmT7zSJU6/ehAIdMfTCHQ7YDd+rSCQDNHKQAqUqXyCFyAi4zdTVoPjchPRRHVeIJEVCLyQBZDrkqAECWFInweoC9sltKTqcpgGuKIqoZoOZhIymY5KZg0uXMveFyOAJN1X7q/DXU+f1sqPGzJxogUL2RtS1dfbLw+iMArG05TCTsZ8PRw2x4+U+fUYQ1G32Uj+LiKUw7r5QJPrmMGwgGqa2q4tGH7oVj1uEPw3NmKaVTShjj8TBtSglzL/wClJBSRFpkDI7znsbvrpzNvt0NhJqDbIkdgLAUYcxTIVsl2R5ohJkqPAJ4vxlCUP67V9kXkNVdc4HSZSQrX3KyASe4PbLDDbOQkw4pV+Qs9pHDu8RI5ia5RwO5iqyPtcoTMxfIZT5tvZTELlJl7kG8WAYQXB5wpfewL2UMB++MIrwzivher4fc91ex8g+PENxZg5wUDnTlHUYHcV442p/+Qjb6hMuZW1rEmFydm5YvIPu4S+N8sHgWj6xafrxPHPKkhRPgZBfOG/5I0dEwBPZBpAzqqyEYh8Bfu3XozECuUMbhkJ/2TX5+8JO3eMlUEJ4D/NsioChDzgd0FSKmhIvLJVVuyIP3G+UZP64QxnoSHbVBVpPl+wBioOUixxULSSiYZk6DzGeBAGhRCKhSIjtXkc7g65AP8BVy/bUlXH/t0Bk6fx1Jj1Jii0xd5ulP+iUsfgWWPQVL/htKzZO0qQt5effB5jD/en3SAVioeUBblpSXdhSBWgRRDzTGaX2ihttKL0aMuYJnb38JyAOHntAmEcgxQp4bZE+xVuS0oZ6XbrsWtpchAwXzoMsDSj6Bigaqfv8qtFri4zY2Q4v0cgJ9cMH4f4Wi/4OaD8EgcFTmEweaHKzrJQxaB9yzEmjqgFYF2lRweCHsYnN5Padc9iK3VkoRxoWPPSfLjKNZiVM3B5kjEIsjg3yBZnjfz0V5ZzPv1lcZNvEa2FUOFEOGFx4v58myXfzHWiAUk4VJNjZDjDR3AgAazHgNffnzRMeeT3DVGsKvrGfJvS/1kfFqBVYCTYFh0KxBzAehfIywh9qKfgS9/fUQDCSyiy0pw7jV39LfTrisgTJzgTMGXP1vt8h/PnSwesVHLK2VSUoNIV+/dfI2NulOesQEBsOIBXhvWQCEue2qSynbf7Df3YLAr57sZHLTq/iKIjKLCBc11eF+9yfQnNA2dQDOTIgGIegPEQiH2a/2lLzaULkNuoJQFeCH5kjEAIKqj4LjUMq1sUkXho4TSKBzy6qXueuh4XQMIOn5KPBoLVC7DVnm0D+bqzYT2VSdaKkVBTgKbSHYUn2AUBQUb08n0AQENzVyxY9u77F9YrG9MmAzNBmCTkByxDjClGn/TM3WJz7za1RW1xDY/FFinTqMXI9u/wCUD2T2Ybzl3T59AU6d+Z0+r5V7Tt9qOBubocAQiAkMTPWWv3DHfc9/5ufvawqws9fqwh7kHP8hpJ5gGUOnL4CNzWdhSDsBgGXXLsAwDBZd+l/H/dza7e/2kQWrQiYdDRBBAIYd9/vY2KQzQ94JWKxZvZzCUX2rxj6N+mPv0g+dwMl4R5zPjVf+gbLV1aSDYrONzWdlyMYE+mNHaC2bX4/w1DOPU1m9mfqaBvhk1+d81b+DzPOYc9Fcfnbd5cyZ+oWYamOTNqRH34HJk43a2tpj7/gZaQcq34TKiirq/H6i0Sh5eW6+fV4pBYVu8vLAm6bSTzY2XxTp33fgS8QJzJ0EcyeVIKsDbGxsLL42MQEbG5vPhu0EbGxOcGwnYGNzgmM7ARubE5y0WB0QQnwAHAE+TLUtn4ORDG37Yeh/hqFuP3y5n8FjGMao3hvTwgkACCFq+1u+GCoMdfth6H+GoW4/pOYz2NMBG5sTHNsJ2Nic4KSTE1iVagM+J0Pdfhj6n2Go2w8p+AxpExOwsbFJDek0ErCxsUkBKXcCQoh/EELsEUI0CSGWptqewSKEeE8IUS+EqBNC1JrbdCHEK0KIveZtTqrt7I4Q4hEhREgI0dBtW782C8l95nHZIYQ4N3WWJ2ztz/5bhRBB8zjUCSEu6PbYf5j27xFCzEmN1UmEEKcJIV4TQuwWQuwUQlxnbk/tMTAMI2V/wDeQKl7jkGod24HxqbTpOGx/DxjZa9tdwFLz/lLgzlTb2cu+UuBcoOFYNiP7Sf4Psh3DVGBbmtp/K/CLfvYdb/6eMoHTzd/ZN1Js/2jgXPO+Brxt2pnSY5DqkcAUoMkwjHcMw+hEtrOfn2KbPg/zgT+Z9/8ELEihLX0wDGMzfUWTBrJ5PvC4IdkKjDBb0KeMAewfiPnAk4ZhHDUM412kRuyUL824QWAYRothGG+Z9yPAbsBNio9Bqp2AG3i/2/8HzG1DAQPYIIR4Uwhxlbkt1zDbsJu3rpRZN3gGsnkoHZtrzOHyI92mYGltvxBiLLKn3jZSfAxS7QT663Y8VJYrvmUYxrnA+cDVQojSVBv0BTNUjs0DyO5xE4EW4G5ze9raL4Q4CVgLLDEM49OaK38lnyHVTuAAcFq3/08FmlNky3FhGEazeRsCnkcONVut4Zp5G0qdhYNmIJuHxLExDKPVMIxPDMPoQopEW0P+tLRfCKEgHcBfDMN4ztyc0mOQaidQA5whhDhdCDEMWASsS7FNx0QIMVwIoVn3gdnIvtrrgB+Zu/0IeCE1Fh4XA9m8DvihGaGeChy2hqzpRK858kXI4wDS/kVCiEwhxOnAGUD1V21fd4QQAngY2G0YxspuD6X2GKQyWtotAvo2Mnq7LNX2DNLmccjI83Zgp2U38HdABbDXvNVTbWsvu9cgh8wx5FXmioFsRg5Ff28el3pgcprav9q0b4d50ozutv8y0/49wPlpYP905HB+B7J/bp35+0/pMbAzBm1sTnBSPR2wsbFJMbYTsLE5wbGdgI3NCY7tBGxsTnBsJ2Bjc4JjOwEbmxMc2wnY2Jzg2E7AxuYE5/8DBLY7+Xq5kmAAAAAASUVORK5CYII=\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# im1 = imread(train_path + \"/sw/0.jpg\")\n", + "# im2 = imread(train_path + \"/vg/0.jpg\")\n", + "\n", + "img_raw = Image.open('../../data/catdog.png').convert('RGB')\n", + "transform_eval = transforms.Compose([\n", + " transforms.Resize(256),\n", + " transforms.CenterCrop(224),\n", + " transforms.ToTensor(),\n", + " transforms.Normalize(mean=[0.485, 0.456, 0.406],\n", + " std=[0.229, 0.224, 0.225])\n", + "])\n", + "\n", + "img = transform_eval(img_raw)\n", + "plt.imshow(img.permute(1,2,0).numpy())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 3. Inference Using ResNet50\n", + "(Go to top)\n", + "\n", + "Now let's generate the predictions from the pretrained ResNet50.\n", + "`pred` will be a list of ndarray, where each ndarray is of length 1000.\n", + "Each number of this 1000-length ndarray can be applied `softmax` to \n", + "represent the prediction confidence towards each of the [subject classes in ImageNet](http://image-net.org/explore)." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "1000" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "net.eval()\n", + "pred = net(img.unsqueeze(0))\n", + "len(pred[0])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To access all the existing classes of ImageNet, pytorch doesn't offer an in-built function. So we use json to load an external list which has a mapping for the ImageNet classes to the indices. Let's take a look of the first 5 classes!" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['tench', 'goldfish', 'great white shark', 'tiger shark', 'hammerhead']" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import json\n", + "\n", + "with open(\"../../data/imagenet_idx_to_class.json\", \"r\") as f:\n", + " classes = json.load(f)\n", + " \n", + "classes[:5]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now let's see how does the model think of our input test image?!" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The input picture is classified to be\n", + "\t[Chihuahua], with probability 0.399.\n", + "\t[tabby], with probability 0.085.\n", + "\t[Egyptian cat], with probability 0.085.\n", + "\t[Brabancon griffon], with probability 0.081.\n", + "\t[pug], with probability 0.023.\n" + ] + } + ], + "source": [ + "topK = 5\n", + "conf, ind = torch.topk(pred.squeeze(0), k=topK)\n", + "ind = ind.squeeze(0).numpy()\n", + "print('The input picture is classified to be')\n", + "for i in range(topK):\n", + " print('\\t[%s], with probability %.3f.'%\n", + " (classes[ind[i]], torch.softmax(pred.squeeze(0), dim=0)[ind[i]].item()))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Feel free to download and try other ResNet versions (ResNet18, ResNet101, ResNet152, etc.) in your own experiment. What is more, try to fineture on other datasets to see if you can improve the model performance." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python [conda env:paperviz]", + "language": "python", + "name": "conda-env-paperviz-py" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.6" + } + }, + "nbformat": 4, + "nbformat_minor": 1 +}