{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "#hide\n", "!pip install -Uqq fastbook\n", "import fastbook\n", "fastbook.setup_book()" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "#hide\n", "from fastbook import *" ] }, { "cell_type": "raw", "metadata": {}, "source": [ "[[chapter_multicat]]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Other Computer Vision Problems" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In the previous chapter you learned some important practical techniques for training models in practice. Considerations like selecting learning rates and the number of epochs are very important to getting good results.\n", "\n", "In this chapter we are going to look at two other types of computer vision problems: multi-label classification and regression. The first one is when you want to predict more than one label per image (or sometimes none at all), and the second is when your labels are one or several numbers—a quantity instead of a category.\n", "\n", "In the process will study more deeply the output activations, targets, and loss functions in deep learning models." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Multi-Label Classification" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Multi-label classification refers to the problem of identifying the categories of objects in images that may not contain exactly one type of object. There may be more than one kind of object, or there may be no objects at all in the classes that you are looking for.\n", "\n", "For instance, this would have been a great approach for our bear classifier. One problem with the bear classifier that we rolled out in <> was that if a user uploaded something that wasn't any kind of bear, the model would still say it was either a grizzly, black, or teddy bear—it had no ability to predict \"not a bear at all.\" In fact, after we have completed this chapter, it would be a great exercise for you to go back to your image classifier application, and try to retrain it using the multi-label technique, then test it by passing in an image that is not of any of your recognized classes.\n", "\n", "In practice, we have not seen many examples of people training multi-label classifiers for this purpose—but we very often see both users and developers complaining about this problem. It appears that this simple solution is not at all widely understood or appreciated! Because in practice it is probably more common to have some images with zero matches or more than one match, we should probably expect in practice that multi-label classifiers are more widely applicable than single-label classifiers.\n", "\n", "First, let's see what a multi-label dataset looks like, then we'll explain how to get it ready for our model. You'll see that the architecture of the model does not change from the last chapter; only the loss function does. Let's start with the data." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### The Data" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "For our example we are going to use the PASCAL dataset, which can have more than one kind of classified object per image.\n", "\n", "We begin by downloading and extracting the dataset as per usual:" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "from fastai.vision.all import *\n", "path = untar_data(URLs.PASCAL_2007)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This dataset is different from the ones we have seen before, in that it is not structured by filename or folder but instead comes with a CSV (comma-separated values) file telling us what labels to use for each image. We can inspect the CSV file by reading it into a Pandas DataFrame:" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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" ], "text/plain": [ " fname labels is_valid\n", "0 000005.jpg chair True\n", "1 000007.jpg car True\n", "2 000009.jpg horse person True\n", "3 000012.jpg car False\n", "4 000016.jpg bicycle True" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df = pd.read_csv(path/'train.csv')\n", "df.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As you can see, the list of categories in each image is shown as a space-delimited string." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Sidebar: Pandas and DataFrames" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "No, it’s not actually a panda! *Pandas* is a Python library that is used to manipulate and analyze tabular and time series data. The main class is `DataFrame`, which represents a table of rows and columns. You can get a DataFrame from a CSV file, a database table, Python dictionaries, and many other sources. In Jupyter, a DataFrame is output as a formatted table, as shown here.\n", "\n", "You can access rows and columns of a DataFrame with the `iloc` property, as if it were a matrix:" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0 000005.jpg\n", "1 000007.jpg\n", "2 000009.jpg\n", "3 000012.jpg\n", "4 000016.jpg\n", " ... \n", "5006 009954.jpg\n", "5007 009955.jpg\n", "5008 009958.jpg\n", "5009 009959.jpg\n", "5010 009961.jpg\n", "Name: fname, Length: 5011, dtype: object" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.iloc[:,0]" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "fname 000005.jpg\n", "labels chair\n", "is_valid True\n", "Name: 0, dtype: object" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.iloc[0,:]\n", "# Trailing :s are always optional (in numpy, pytorch, pandas, etc.),\n", "# so this is equivalent:\n", "df.iloc[0]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "You can also grab a column by name by indexing into a DataFrame directly:" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0 000005.jpg\n", "1 000007.jpg\n", "2 000009.jpg\n", "3 000012.jpg\n", "4 000016.jpg\n", " ... \n", "5006 009954.jpg\n", "5007 009955.jpg\n", "5008 009958.jpg\n", "5009 009959.jpg\n", "5010 009961.jpg\n", "Name: fname, Length: 5011, dtype: object" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df['fname']" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "You can create new columns and do calculations using columns:" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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" ], "text/plain": [ " a b\n", "0 1 3\n", "1 2 4" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "tmp_df = pd.DataFrame({'a':[1,2], 'b':[3,4]})\n", "tmp_df" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
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" ], "text/plain": [ " a b c\n", "0 1 3 4\n", "1 2 4 6" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "tmp_df['c'] = tmp_df['a']+tmp_df['b']\n", "tmp_df" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Pandas is a fast and flexible library, and an important part of every data scientist’s Python toolbox. Unfortunately, its API can be rather confusing and surprising, so it takes a while to get familiar with it. If you haven’t used Pandas before, we’d suggest going through a tutorial; we are particularly fond of the book [*Python for Data Analysis*](http://shop.oreilly.com/product/0636920023784.do) by Wes McKinney, the creator of Pandas (O'Reilly). It also covers other important libraries like `matplotlib` and `numpy`. We will try to briefly describe Pandas functionality we use as we come across it, but will not go into the level of detail of McKinney’s book." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### End sidebar" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now that we have seen what the data looks like, let's make it ready for model training." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Constructing a DataBlock" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "How do we convert from a `DataFrame` object to a `DataLoaders` object? We generally suggest using the data block API for creating a `DataLoaders` object, where possible, since it provides a good mix of flexibility and simplicity. Here we will show you the steps that we take to use the data blocks API to construct a `DataLoaders` object in practice, using this dataset as an example.\n", "\n", "As we have seen, PyTorch and fastai have two main classes for representing and accessing a training set or validation set:\n", "\n", "- `Dataset`:: A collection that returns a tuple of your independent and dependent variable for a single item\n", "- `DataLoader`:: An iterator that provides a stream of mini-batches, where each mini-batch is a tuple of a batch of independent variables and a batch of dependent variables" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "On top of these, fastai provides two classes for bringing your training and validation sets together:\n", "\n", "- `Datasets`:: An object that contains a training `Dataset` and a validation `Dataset`\n", "- `DataLoaders`:: An object that contains a training `DataLoader` and a validation `DataLoader`\n", "\n", "Since a `DataLoader` builds on top of a `Dataset` and adds additional functionality to it (collating multiple items into a mini-batch), it’s often easiest to start by creating and testing `Datasets`, and then look at `DataLoaders` after that’s working." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "When we create a `DataBlock`, we build up gradually, step by step, and use the notebook to check our data along the way. This is a great way to make sure that you maintain momentum as you are coding, and that you keep an eye out for any problems. It’s easy to debug, because you know that if a problem arises, it is in the line of code you just typed!\n", "\n", "Let’s start with the simplest case, which is a data block created with no parameters:" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [], "source": [ "dblock = DataBlock()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can create a `Datasets` object from this. The only thing needed is a source—in this case, our DataFrame:" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [], "source": [ "dsets = dblock.datasets(df)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This contains a `train` and a `valid` dataset, which we can index into:" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(4009, 1002)" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "len(dsets.train),len(dsets.valid)" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(fname 008663.jpg\n", " labels car person\n", " is_valid False\n", " Name: 4346, dtype: object,\n", " fname 008663.jpg\n", " labels car person\n", " is_valid False\n", " Name: 4346, dtype: object)" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "x,y = dsets.train[0]\n", "x,y" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As you can see, this simply returns a row of the DataFrame, twice. This is because by default, the data block assumes we have two things: input and target. We are going to need to grab the appropriate fields from the DataFrame, which we can do by passing `get_x` and `get_y` functions:" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'008663.jpg'" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "x['fname']" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "('005620.jpg', 'aeroplane')" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dblock = DataBlock(get_x = lambda r: r['fname'], get_y = lambda r: r['labels'])\n", "dsets = dblock.datasets(df)\n", "dsets.train[0]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As you can see, rather than defining a function in the usual way, we are using Python’s `lambda` keyword. This is just a shortcut for defining and then referring to a function. The following more verbose approach is identical:" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "('002549.jpg', 'tvmonitor')" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "def get_x(r): return r['fname']\n", "def get_y(r): return r['labels']\n", "dblock = DataBlock(get_x = get_x, get_y = get_y)\n", "dsets = dblock.datasets(df)\n", "dsets.train[0]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Lambda functions are great for quickly iterating, but they are not compatible with serialization, so we advise you to use the more verbose approach if you want to export your `Learner` after training (lambdas are fine if you are just experimenting)." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can see that the independent variable will need to be converted into a complete path, so that we can open it as an image, and the dependent variable will need to be split on the space character (which is the default for Python’s `split` function) so that it becomes a list:" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(Path('/home/jhoward/.fastai/data/pascal_2007/train/002844.jpg'), ['train'])" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "def get_x(r): return path/'train'/r['fname']\n", "def get_y(r): return r['labels'].split(' ')\n", "dblock = DataBlock(get_x = get_x, get_y = get_y)\n", "dsets = dblock.datasets(df)\n", "dsets.train[0]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To actually open the image and do the conversion to tensors, we will need to use a set of transforms; block types will provide us with those. We can use the same block types that we have used previously, with one exception: the `ImageBlock` will work fine again, because we have a path that points to a valid image, but the `CategoryBlock` is not going to work. The problem is that block returns a single integer, but we need to be able to have multiple labels for each item. To solve this, we use a `MultiCategoryBlock`. This type of block expects to receive a list of strings, as we have in this case, so let’s test it out:" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(PILImage mode=RGB size=500x375,\n", " TensorMultiCategory([0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 1., 0., 0., 0., 0., 0., 0., 0., 0.]))" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dblock = DataBlock(blocks=(ImageBlock, MultiCategoryBlock),\n", " get_x = get_x, get_y = get_y)\n", "dsets = dblock.datasets(df)\n", "dsets.train[0]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As you can see, our list of categories is not encoded in the same way that it was for the regular `CategoryBlock`. In that case, we had a single integer representing which category was present, based on its location in our vocab. In this case, however, we instead have a list of zeros, with a one in any position where that category is present. For example, if there is a one in the second and fourth positions, then that means that vocab items two and four are present in this image. This is known as *one-hot encoding*. The reason we can’t easily just use a list of category indices is that each list would be a different length, and PyTorch requires tensors, where everything has to be the same length." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "> jargon: One-hot encoding: Using a vector of zeros, with a one in each location that is represented in the data, to encode a list of integers." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let’s check what the categories represent for this example (we are using the convenient `torch.where` function, which tells us all of the indices where our condition is true or false):" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(#1) ['dog']" ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ "idxs = torch.where(dsets.train[0][1]==1.)[0]\n", "dsets.train.vocab[idxs]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "With NumPy arrays, PyTorch tensors, and fastai’s `L` class, we can index directly using a list or vector, which makes a lot of code (such as this example) much clearer and more concise.\n", "\n", "We have ignored the column `is_valid` up until now, which means that `DataBlock` has been using a random split by default. To explicitly choose the elements of our validation set, we need to write a function and pass it to `splitter` (or use one of fastai's predefined functions or classes). It will take the items (here our whole DataFrame) and must return two (or more) lists of integers:" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(PILImage mode=RGB size=500x333,\n", " TensorMultiCategory([0., 0., 0., 0., 0., 0., 1., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.]))" ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ "def splitter(df):\n", " train = df.index[~df['is_valid']].tolist()\n", " valid = df.index[df['is_valid']].tolist()\n", " return train,valid\n", "\n", "dblock = DataBlock(blocks=(ImageBlock, MultiCategoryBlock),\n", " splitter=splitter,\n", " get_x=get_x, \n", " get_y=get_y)\n", "\n", "dsets = dblock.datasets(df)\n", "dsets.train[0]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As we have discussed, a `DataLoader` collates the items from a `Dataset` into a mini-batch. This is a tuple of tensors, where each tensor simply stacks the items from that location in the `Dataset` item. \n", "\n", "Now that we have confirmed that the individual items look okay, there's one more step we need to ensure we can create our `DataLoaders`, which is to ensure that every item is of the same size. To do this, we can use `RandomResizedCrop`:" ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [], "source": [ "dblock = DataBlock(blocks=(ImageBlock, MultiCategoryBlock),\n", " splitter=splitter,\n", " get_x=get_x, \n", " get_y=get_y,\n", " item_tfms = RandomResizedCrop(128, min_scale=0.35))\n", "dls = dblock.dataloaders(df)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "And now we can display a sample of our data:" ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [ { "data": { "image/png": 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"text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "dls.show_batch(nrows=1, ncols=3)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Remember that if anything goes wrong when you create your `DataLoaders` from your `DataBlock`, or if you want to view exactly what happens with your `DataBlock`, you can use the `summary` method we presented in the last chapter." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Our data is now ready for training a model. As we will see, nothing is going to change when we create our `Learner`, but behind the scenes, the fastai library will pick a new loss function for us: binary cross-entropy." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Binary Cross-Entropy" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we'll create our `Learner`. We saw in <> that a `Learner` object contains four main things: the model, a `DataLoaders` object, an `Optimizer`, and the loss function to use. We already have our `DataLoaders`, we can leverage fastai's `resnet` models (which we'll learn how to create from scratch later), and we know how to create an `SGD` optimizer. So let's focus on ensuring we have a suitable loss function. To do this, let's use `cnn_learner` to create a `Learner`, so we can look at its activations:" ] }, { "cell_type": "code", "execution_count": 31, "metadata": {}, "outputs": [], "source": [ "learn = cnn_learner(dls, resnet18)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We also saw that the model in a `Learner` is generally an object of a class inheriting from `nn.Module`, and that we can call it using parentheses and it will return the activations of a model. You should pass it your independent variable, as a mini-batch. We can try it out by grabbing a mini batch from our `DataLoader` and then passing it to the model:" ] }, { "cell_type": "code", "execution_count": 32, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "torch.Size([64, 20])" ] }, "execution_count": 32, "metadata": {}, "output_type": "execute_result" } ], "source": [ "x,y = to_cpu(dls.train.one_batch())\n", "activs = learn.model(x)\n", "activs.shape" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Think about why `activs` has this shape—we have a batch size of 64, and we need to calculate the probability of each of 20 categories. Here’s what one of those activations looks like:" ] }, { "cell_type": "code", "execution_count": 33, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "tensor([ 0.1987, -2.7846, -1.1943, 0.0250, -2.2920, 3.9218, 0.3482, 0.9501, 0.5162, 0.6932, -0.1302, -2.0550, 2.5748, 1.1635, 2.9216, -4.5089, 1.1606, -2.3782, -2.3923, 0.2469],\n", " grad_fn=)" ] }, "execution_count": 33, "metadata": {}, "output_type": "execute_result" } ], "source": [ "activs[0]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "> note: Getting Model Activations: Knowing how to manually get a mini-batch and pass it into a model, and look at the activations and loss, is really important for debugging your model. It is also very helpful for learning, so that you can see exactly what is going on." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "They aren’t yet scaled to between 0 and 1, but we learned how to do that in <>, using the `sigmoid` function. We also saw how to calculate a loss based on this—this is our loss function from <>, with the addition of `log` as discussed in the last chapter:" ] }, { "cell_type": "code", "execution_count": 34, "metadata": {}, "outputs": [], "source": [ "def binary_cross_entropy(inputs, targets):\n", " inputs = inputs.sigmoid()\n", " return -torch.where(targets==1, inputs, 1-inputs).log().mean()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Note that because we have a one-hot-encoded dependent variable, we can't directly use `nll_loss` or `softmax` (and therefore we can't use `cross_entropy`):\n", "\n", "- `softmax`, as we saw, requires that all predictions sum to 1, and tends to push one activation to be much larger than the others (due to the use of `exp`); however, we may well have multiple objects that we're confident appear in an image, so restricting the maximum sum of activations to 1 is not a good idea. By the same reasoning, we may want the sum to be *less* than 1, if we don't think *any* of the categories appear in an image.\n", "- `nll_loss`, as we saw, returns the value of just one activation: the single activation corresponding with the single label for an item. This doesn't make sense when we have multiple labels.\n", "\n", "On the other hand, the `binary_cross_entropy` function, which is just `mnist_loss` along with `log`, provides just what we need, thanks to the magic of PyTorch's elementwise operations. Each activation will be compared to each target for each column, so we don't have to do anything to make this function work for multiple columns." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "> j: One of the things I really like about working with libraries like PyTorch, with broadcasting and elementwise operations, is that quite frequently I find I can write code that works equally well for a single item or a batch of items, without changes. `binary_cross_entropy` is a great example of this. By using these operations, we don't have to write loops ourselves, and can rely on PyTorch to do the looping we need as appropriate for the rank of the tensors we're working with." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "PyTorch already provides this function for us. In fact, it provides a number of versions, with rather confusing names!\n", "\n", "`F.binary_cross_entropy` and its module equivalent `nn.BCELoss` calculate cross-entropy on a one-hot-encoded target, but do not include the initial `sigmoid`. Normally for one-hot-encoded targets you'll want `F.binary_cross_entropy_with_logits` (or `nn.BCEWithLogitsLoss`), which do both sigmoid and binary cross-entropy in a single function, as in the preceding example.\n", "\n", "The equivalent for single-label datasets (like MNIST or the Pet dataset), where the target is encoded as a single integer, is `F.nll_loss` or `nn.NLLLoss` for the version without the initial softmax, and `F.cross_entropy` or `nn.CrossEntropyLoss` for the version with the initial softmax.\n", "\n", "Since we have a one-hot-encoded target, we will use `BCEWithLogitsLoss`:" ] }, { "cell_type": "code", "execution_count": 35, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "tensor(1.0835, grad_fn=)" ] }, "execution_count": 35, "metadata": {}, "output_type": "execute_result" } ], "source": [ "loss_func = nn.BCEWithLogitsLoss()\n", "loss = loss_func(activs, y)\n", "loss" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We don't actually need to tell fastai to use this loss function (although we can if we want) since it will be automatically chosen for us. fastai knows that the `DataLoaders` has multiple category labels, so it will use `nn.BCEWithLogitsLoss` by default.\n", "\n", "One change compared to the last chapter is the metric we use: because this is a multilabel problem, we can't use the accuracy function. Why is that? Well, accuracy was comparing our outputs to our targets like so:\n", "\n", "```python\n", "def accuracy(inp, targ, axis=-1):\n", " \"Compute accuracy with `targ` when `pred` is bs * n_classes\"\n", " pred = inp.argmax(dim=axis)\n", " return (pred == targ).float().mean()\n", "```\n", "\n", "The class predicted was the one with the highest activation (this is what `argmax` does). Here it doesn't work because we could have more than one prediction on a single image. After applying the sigmoid to our activations (to make them between 0 and 1), we need to decide which ones are 0s and which ones are 1s by picking a *threshold*. Each value above the threshold will be considered as a 1, and each value lower than the threshold will be considered a 0:\n", "\n", "```python\n", "def accuracy_multi(inp, targ, thresh=0.5, sigmoid=True):\n", " \"Compute accuracy when `inp` and `targ` are the same size.\"\n", " if sigmoid: inp = inp.sigmoid()\n", " return ((inp>thresh)==targ.bool()).float().mean()\n", "```" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "If we pass `accuracy_multi` directly as a metric, it will use the default value for `threshold`, which is 0.5. We might want to adjust that default and create a new version of `accuracy_multi` that has a different default. To help with this, there is a function in Python called `partial`. It allows us to *bind* a function with some arguments or keyword arguments, making a new version of that function that, whenever it is called, always includes those arguments. For instance, here is a simple function taking two arguments:" ] }, { "cell_type": "code", "execution_count": 36, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "('Hello Jeremy.', 'Ahoy! Jeremy.')" ] }, "execution_count": 36, "metadata": {}, "output_type": "execute_result" } ], "source": [ "def say_hello(name, say_what=\"Hello\"): return f\"{say_what} {name}.\"\n", "say_hello('Jeremy'),say_hello('Jeremy', 'Ahoy!')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can switch to a French version of that function by using `partial`:" ] }, { "cell_type": "code", "execution_count": 37, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "('Bonjour Jeremy.', 'Bonjour Sylvain.')" ] }, "execution_count": 37, "metadata": {}, "output_type": "execute_result" } ], "source": [ "f = partial(say_hello, say_what=\"Bonjour\")\n", "f(\"Jeremy\"),f(\"Sylvain\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can now train our model. Let's try setting the accuracy threshold to 0.2 for our metric:" ] }, { "cell_type": "code", "execution_count": 38, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
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" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "learn = cnn_learner(dls, resnet50, metrics=partial(accuracy_multi, thresh=0.2))\n", "learn.fine_tune(3, base_lr=3e-3, freeze_epochs=4)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Picking a threshold is important. If you pick a threshold that's too low, you'll often be failing to select correctly labeled objects. We can see this by changing our metric, and then calling `validate`, which returns the validation loss and metrics:" ] }, { "cell_type": "code", "execution_count": 39, "metadata": {}, "outputs": [ { "data": { "text/html": [], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "(#2) [0.10344962775707245,0.9285657405853271]" ] }, "execution_count": 39, "metadata": {}, "output_type": "execute_result" } ], "source": [ "learn.metrics = partial(accuracy_multi, thresh=0.1)\n", "learn.validate()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "If you pick a threshold that's too high, you'll only be selecting the objects for which your model is very confident:" ] }, { "cell_type": "code", "execution_count": 40, "metadata": {}, "outputs": [ { "data": { "text/html": [], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "(#2) [0.10344962775707245,0.9431872963905334]" ] }, "execution_count": 40, "metadata": {}, "output_type": "execute_result" } ], "source": [ "learn.metrics = partial(accuracy_multi, thresh=0.99)\n", "learn.validate()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can find the best threshold by trying a few levels and seeing what works best. This is much faster if we just grab the predictions once:" ] }, { "cell_type": "code", "execution_count": 41, "metadata": {}, "outputs": [ { "data": { "text/html": [], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "preds,targs = learn.get_preds()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Then we can call the metric directly. Note that by default `get_preds` applies the output activation function (sigmoid, in this case) for us, so we'll need to tell `accuracy_multi` to not apply it:" ] }, { "cell_type": "code", "execution_count": 42, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "tensor(0.9569)" ] }, "execution_count": 42, "metadata": {}, "output_type": "execute_result" } ], "source": [ "accuracy_multi(preds, targs, thresh=0.9, sigmoid=False)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can now use this approach to find the best threshold level:" ] }, { "cell_type": "code", "execution_count": 43, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "xs = torch.linspace(0.05,0.95,29)\n", "accs = [accuracy_multi(preds, targs, thresh=i, sigmoid=False) for i in xs]\n", "plt.plot(xs,accs);" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In this case, we're using the validation set to pick a hyperparameter (the threshold), which is the purpose of the validation set. Sometimes students have expressed their concern that we might be *overfitting* to the validation set, since we're trying lots of values to see which is the best. However, as you see in the plot, changing the threshold in this case results in a smooth curve, so we're clearly not picking some inappropriate outlier. This is a good example of where you have to be careful of the difference between theory (don't try lots of hyperparameter values or you might overfit the validation set) versus practice (if the relationship is smooth, then it's fine to do this).\n", "\n", "This concludes the part of this chapter dedicated to multi-label classification. Next, we'll take a look at a regression problem." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Regression" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "It's easy to think of deep learning models as being classified into domains, like *computer vision*, *NLP*, and so forth. And indeed, that's how fastai classifies its applications—largely because that's how most people are used to thinking of things.\n", "\n", "But really, that's hiding a more interesting and deeper perspective. A model is defined by its independent and dependent variables, along with its loss function. That means that there's really a far wider array of models than just the simple domain-based split. Perhaps we have an independent variable that's an image, and a dependent that's text (e.g., generating a caption from an image); or perhaps we have an independent variable that's text and dependent that's an image (e.g., generating an image from a caption—which is actually possible for deep learning to do!); or perhaps we've got images, texts, and tabular data as independent variables, and we're trying to predict product purchases... the possibilities really are endless.\n", "\n", "To be able to move beyond fixed applications, to crafting your own novel solutions to novel problems, it helps to really understand the data block API (and maybe also the mid-tier API, which we'll see later in the book). As an example, let's consider the problem of *image regression*. This refers to learning from a dataset where the independent variable is an image, and the dependent variable is one or more floats. Often we see people treat image regression as a whole separate application—but as you'll see here, we can treat it as just another CNN on top of the data block API.\n", "\n", "We're going to jump straight to a somewhat tricky variant of image regression, because we know you're ready for it! We're going to do a key point model. A *key point* refers to a specific location represented in an image—in this case, we'll use images of people and we'll be looking for the center of the person's face in each image. That means we'll actually be predicting *two* values for each image: the row and column of the face center. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Assemble the Data" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We will use the [Biwi Kinect Head Pose dataset](https://icu.ee.ethz.ch/research/datsets.html) for this section. We'll begin by downloading the dataset as usual:" ] }, { "cell_type": "code", "execution_count": 44, "metadata": {}, "outputs": [], "source": [ "path = untar_data(URLs.BIWI_HEAD_POSE)" ] }, { "cell_type": "code", "execution_count": 45, "metadata": {}, "outputs": [], "source": [ "#hide\n", "Path.BASE_PATH = path" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's see what we've got!" ] }, { "cell_type": "code", "execution_count": 46, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(#50) [Path('01'),Path('01.obj'),Path('02'),Path('02.obj'),Path('03'),Path('03.obj'),Path('04'),Path('04.obj'),Path('05'),Path('05.obj')...]" ] }, "execution_count": 46, "metadata": {}, "output_type": "execute_result" } ], "source": [ "path.ls().sorted()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "There are 24 directories numbered from 01 to 24 (they correspond to the different people photographed), and a corresponding *.obj* file for each (we won't need them here). Let's take a look inside one of these directories:" ] }, { "cell_type": "code", "execution_count": 47, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(#1000) [Path('01/depth.cal'),Path('01/frame_00003_pose.txt'),Path('01/frame_00003_rgb.jpg'),Path('01/frame_00004_pose.txt'),Path('01/frame_00004_rgb.jpg'),Path('01/frame_00005_pose.txt'),Path('01/frame_00005_rgb.jpg'),Path('01/frame_00006_pose.txt'),Path('01/frame_00006_rgb.jpg'),Path('01/frame_00007_pose.txt')...]" ] }, "execution_count": 47, "metadata": {}, "output_type": "execute_result" } ], "source": [ "(path/'01').ls().sorted()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Inside the subdirectories, we have different frames, each of them come with an image (*\\_rgb.jpg*) and a pose file (*\\_pose.txt*). We can easily get all the image files recursively with `get_image_files`, then write a function that converts an image filename to its associated pose file:" ] }, { "cell_type": "code", "execution_count": 48, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Path('13/frame_00349_pose.txt')" ] }, "execution_count": 48, "metadata": {}, "output_type": "execute_result" } ], "source": [ "img_files = get_image_files(path)\n", "def img2pose(x): return Path(f'{str(x)[:-7]}pose.txt')\n", "img2pose(img_files[0])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's take a look at our first image:" ] }, { "cell_type": "code", "execution_count": 49, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(480, 640)" ] }, "execution_count": 49, "metadata": {}, "output_type": "execute_result" } ], "source": [ "im = PILImage.create(img_files[0])\n", "im.shape" ] }, { "cell_type": "code", "execution_count": 50, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "" ] }, "execution_count": 50, "metadata": {}, "output_type": "execute_result" } ], "source": [ "im.to_thumb(160)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The Biwi dataset website used to explain the format of the pose text file associated with each image, which shows the location of the center of the head. The details of this aren't important for our purposes, so we'll just show the function we use to extract the head center point:" ] }, { "cell_type": "code", "execution_count": 51, "metadata": {}, "outputs": [], "source": [ "cal = np.genfromtxt(path/'01'/'rgb.cal', skip_footer=6)\n", "def get_ctr(f):\n", " ctr = np.genfromtxt(img2pose(f), skip_header=3)\n", " c1 = ctr[0] * cal[0][0]/ctr[2] + cal[0][2]\n", " c2 = ctr[1] * cal[1][1]/ctr[2] + cal[1][2]\n", " return tensor([c1,c2])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This function returns the coordinates as a tensor of two items:" ] }, { "cell_type": "code", "execution_count": 52, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "tensor([384.6370, 259.4787])" ] }, "execution_count": 52, "metadata": {}, "output_type": "execute_result" } ], "source": [ "get_ctr(img_files[0])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can pass this function to `DataBlock` as `get_y`, since it is responsible for labeling each item. We'll resize the images to half their input size, just to speed up training a bit.\n", "\n", "One important point to note is that we should not just use a random splitter. The reason for this is that the same people appears in multiple images in this dataset, but we want to ensure that our model can generalize to people that it hasn't seen yet. Each folder in the dataset contains the images for one person. Therefore, we can create a splitter function that returns true for just one person, resulting in a validation set containing just that person's images.\n", "\n", "The only other difference tfrom the previous data block examples is that the second block is a `PointBlock`. This is necessary so that fastai knows that the labels represent coordinates; that way, it knows that when doing data augmentation, it should do the same augmentation to these coordinates as it does to the images:" ] }, { "cell_type": "code", "execution_count": 53, "metadata": {}, "outputs": [], "source": [ "biwi = DataBlock(\n", " blocks=(ImageBlock, PointBlock),\n", " get_items=get_image_files,\n", " get_y=get_ctr,\n", " splitter=FuncSplitter(lambda o: o.parent.name=='13'),\n", " batch_tfms=[*aug_transforms(size=(240,320)), \n", " Normalize.from_stats(*imagenet_stats)]\n", ")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "> important: Points and Data Augmentation: We're not aware of other libraries (except for fastai) that automatically and correctly apply data augmentation to coordinates. So, if you're working with another library, you may need to disable data augmentation for these kinds of problems." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Before doing any modeling, we should look at our data to confirm it seems okay:" ] }, { "cell_type": "code", "execution_count": 54, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "dls = biwi.dataloaders(path)\n", "dls.show_batch(max_n=9, figsize=(8,6))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "That's looking good! As well as looking at the batch visually, it's a good idea to also look at the underlying tensors (especially as a student; it will help clarify your understanding of what your model is really seeing):" ] }, { "cell_type": "code", "execution_count": 55, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(torch.Size([64, 3, 240, 320]), torch.Size([64, 1, 2]))" ] }, "execution_count": 55, "metadata": {}, "output_type": "execute_result" } ], "source": [ "xb,yb = dls.one_batch()\n", "xb.shape,yb.shape" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Make sure that you understand *why* these are the shapes for our mini-batches." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Here's an example of one row from the dependent variable:" ] }, { "cell_type": "code", "execution_count": 56, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "tensor([[-0.3545, -0.0016]], device='cuda:5')" ] }, "execution_count": 56, "metadata": {}, "output_type": "execute_result" } ], "source": [ "yb[0]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As you can see, we haven't had to use a separate *image regression* application; all we've had to do is label the data, and tell fastai what kinds of data the independent and dependent variables represent." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "It's the same for creating our `Learner`. We will use the same function as before, with one new parameter, and we will be ready to train our model." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Training a Model" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As usual, we can use `cnn_learner` to create our `Learner`. Remember way back in <> how we used `y_range` to tell fastai the range of our targets? We'll do the same here (coordinates in fastai and PyTorch are always rescaled between -1 and +1):" ] }, { "cell_type": "code", "execution_count": 57, "metadata": {}, "outputs": [], "source": [ "learn = cnn_learner(dls, resnet18, y_range=(-1,1))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "`y_range` is implemented in fastai using `sigmoid_range`, which is defined as:" ] }, { "cell_type": "code", "execution_count": 58, "metadata": {}, "outputs": [], "source": [ "def sigmoid_range(x, lo, hi): return torch.sigmoid(x) * (hi-lo) + lo" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This is set as the final layer of the model, if `y_range` is defined. Take a moment to think about what this function does, and why it forces the model to output activations in the range `(lo,hi)`.\n", "\n", "Here's what it looks like:" ] }, { "cell_type": "code", "execution_count": 59, "metadata": {}, "outputs": [ { "data": { "image/png": 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ESARmAveZ2eha6nUCrgV6AMdSscPcT2vUudrdYyofRwUcp0izVVpWztVPLScjr5j7Z02iZxfdgE8aR2CzmMysMzAdGOPu+cASM3sBmAXcUL2uu99X7ekOM3sCODWoWERast+8uo73N2Zz1wVHM75/XNjhSAsWZA9iOFDm7mnVylKB2noQNX0NqLnn9B1mlmVm75vZlLpONLN5ZpZiZimZmZkNjVmkWXkxdSd//fdmLjl+AN9N7h92ONLCBZkgYoCcGmU5QJeDnWRmlwLJwG+rFV8PDAb6AguAF82s1pvKuPsCd0929+SEhITDjV0k4qXtzuP6Z1cxaUA3bjpnVNjhSCsQZILIB2JrlMUCeXWdYGbTgDuBs9w9q6rc3T9y9zx3L3b3R4D3gbMDjFWkWcktOsAPHltGp/ZR3DtTg9LSNIJ8laUBUWY2rFrZOL566QgAMzsT+CvwLXdfXU/bDmgOn7RK7s5Pnk5l255C7p05UbvCSZMJLEG4ewGwGLjFzDqb2YnAVOCxmnXN7OvAE8B0d19a41icmZ1hZh3NLMrMZlIxRvFaULGKNCcPvLeJ19fu5sazR2rjH2lSQfdTrwSigQzgKWC+u68xs6TK9QxJlfVuBroCL1db6/BK5bF2wG1AJpAF/BCY5u5aCyGtzoefZXPXq+s45+jezD1xYNjhSCsT6M363H0PMK2W8nQqBrGrntc5pdXdM4HJQcYl0hztzi3ih08tZ2CPzvxm+tFaKS1NTiNdIhHoQFk5Vz+5nMKSMh64eBIxHXTjZWl6etWJRKD/99p6Pt6ylz/OGM+wxIPOFBdpNOpBiESYf63ZxYL3NnHxcUlMHd837HCkFVOCEIkg6dmF/OSZVMb27crN52oxnIRLCUIkQhSXlnHVk8sx4N6ZE+kQ1TbskKSV0xiESIT49UufsnpHDgtmTaJ/fKewwxFRD0IkEry06nMe/XArl500iNNH9wo7HBFACUIkdFuyCrj+2VVMSIrj+rNGhB2OyBeUIERCVHSgYtyhbRvjzxdOoF1bvSUlcmgMQiREv37pU9bszOVvlyTTr5vGHSSy6M8VkZC8tOpzHvvPVi4/eRDfGJUYdjgiX6EEIRKCrdn/N+7wszM17iCRSQlCpIkVl5Zx9ZMrNO4gEU9jECJN7I6X132x3kHjDhLJ9KeLSBN6bc0uFn6whbknar2DRD4lCJEmsn1vIdc9k8rR/bpyg9Y7SDMQaIIws3gze87MCsxsq5lddJC6PzazXWaWY2YPmVmHw2lHpDk4UFbOD59agTv85cKJtI/S32YS+YJ+ld4DlACJwEzgPjMbXbOSmZ0B3ACcBgwEBgO/amg7Is3Fb19bz4r0fdw5/WiSumvcQZqHwBKEmXUGpgM3u3u+uy8BXgBm1VJ9NvCgu69x973ArcCcw2hHJOK9vS6DByr3dzjn6N5hhyNyyILsQQwHytw9rVpZKlDbX/6jK49Vr5doZt0b2A5mNs/MUswsJTMz84i+AZGgfZ6zn/96eiUjenXhpnO0v4M0L0EmiBggp0ZZDlDbfok161Z93aWB7eDuC9w92d2TExISGhy0SGMpLSvnmkUrKS4t556ZE+nYTvs7SPMS5DqIfCC2RlkskHcIdau+zmtgOyIR609vbmDp5j3c/d1xDEmICTsckQYLsgeRBkSZ2bBqZeOANbXUXVN5rHq93e6e3cB2RCLSkg1Z/PntjXxnUj/On9gv7HBEDktgCcLdC4DFwC1m1tnMTgSmAo/VUv1R4PtmNsrMugE3AQsPox2RiJORV8S1/7uSoQkx/GqqJt9J8xX0NNcrgWggA3gKmO/ua8wsyczyzSwJwN1fBe4C3ga2Vj5+UV87AccqEriycufH/7uS/OID3DNzIp3a62420nwF+up19z3AtFrK06kYfK5edjdwd0PaEYl097y9kfc3ZnPn+WMZnljrvAqRZkPLOUUC8uFn2fzhjTSmju/D9yb3DzsckSOmBCESgKz8Yq5ZtIKB3Tvz62+PxczCDknkiOkCqcgRKq8cd9i3/wALLz2GmA56W0nLoB6EyBG6952N/HtDFr/41ihG9am5hEek+VKCEDkC/9mUzd2vp/GtcX246JiksMMRCZQShMhhyswr5kdPVYw73HG+xh2k5dHFUpHDULXeIWf/AR6Zq3EHaZn0qhY5DH95ayNLNmZx5/ljGdlb4w7SMukSk0gDLdmQxR/eTOP8CX213kFaNCUIkQbYlVPENYtWMKxnDLd9e4zGHaRFU4IQOUQV+0ovZ/+BMu7VfZakFdArXOQQ/b/X1vPxlr38ccZ4hvbUfZak5VMPQuQQvPrJ5yx4bxOXHD+AqeP7hh2OSJNQghCpx6bMfH76zCrG94/j5+eMDDsckSajBCFyEIUlpcx/fDnt2hr3zpxIhyjtKy2th8YgROrg7ty4eDVpGXk8OvcY+sRFhx2SSJMKpAdhZvFm9pyZFZjZVjO76CB1Z5vZMjPLNbPtZnaXmUVVO/6OmRVV7kCXb2brg4hRpKEefn8Lz6/cyU++OZyThyWEHY5IkwvqEtM9QAmQCMwE7jOzujbj7QRcC/QAjgVOA35ao87V7h5T+TgqoBhFDtnSzXu4/eVP+eaoRK6cMjTscERCccSXmMysMzAdGOPu+cASM3sBmAXcULO+u99X7ekOM3sCOPVI4xAJyu7cIq58YjlJ8Z343XfH0aaNFsNJ6xRED2I4UObuadXKUoG6ehA1fQ1YU6PsDjPLMrP3zWzKwU42s3lmlmJmKZmZmYcas0itikvLmP/4MgpLSrl/1iRiO7YLOySR0ASRIGKAnBplOUC9K4nM7FIgGfhtteLrgcFAX2AB8KKZDamrDXdf4O7J7p6ckKDrxHL43J1fPL+G5en7+N13xjE8UYvhpHWrN0FUDhp7HY8lQD5Q83aWsUBePe1OA+4EznL3rKpyd//I3fPcvdjdHwHeB85u4Pcl0mBPfJTOoo+3cdWpQzhrbO+wwxEJXb1jEO4+5WDHK8cgosxsmLtvqCwex1cvG1U/50zgr8A57r66vhAAXQSWRvXxlj388oU1nHpUAv/1Tc2LEIEALjG5ewGwGLjFzDqb2YnAVOCx2uqb2deBJ4Dp7r60xrE4MzvDzDqaWZSZzaRijOK1I41TpC479u3niseW0T++E3+YMYG2GpQWAYKb5nolEA1kAE8B8919DYCZJVWuZ6jasPdmoCvwcrW1Dq9UHmsH3AZkAlnAD4Fp7q61ENIoCktKueyRFErKyvnrJcl0jdagtEiVQFZSu/seYFodx9KpGMiuel7nlFZ3zwQmBxGTSH3Ky52fPJ3K+l25PDhnMkN7xtR/kkgronsxSav1xzc38Monu7jxrJGcelTPsMMRiThKENIqPb9yB398cwMXTOrHZScPCjsckYikBCGtzvL0vVz391UcMyie2789VtuGitRBCUJale17C5n3aAq9u3bk/osn0T5KbwGRuuh239Jq5BYd4PsLUyguLWfRvMnEd24fdkgiEU1/PkmrUFJazvzHl7EpK58HLp6kGUsih0A9CGnxqjb+eX9jNr/7zjhOGNoj7JBEmgX1IKTF++ObG3h2+XZ+/I3hTJ/UL+xwRJoNJQhp0Z5ams4f3qiYzvqj07Txj0hDKEFIi/X62t38/LnVTDkqgTvO13RWkYZSgpAWadnWPVz95HLG9u3KvTMn0q6tXuoiDaV3jbQ463blMndhCn3ionlozmQ6tddcDJHDoQQhLUp6diGzHlxKx3ZteHTuMXSP6RB2SCLNlv60khZjd24RMx/8DwfKynnmB8fTP75T2CGJNGvqQUiLsKeghFkPfsSe/BIWXnoMw7SftMgRCyRBmFm8mT1nZgVmttXMLjpI3TlmVlZts6B8M5tyOG2JAOQUHmDWgx+xNbuQv16SzPj+cWGHJNIiBHWJ6R6gBEgExgMvmVlq1a5ytfjQ3U8KqC1pxfKLS5n98FI27M5nwSWTtEpaJEBH3IMws87AdOBmd8939yXAC8CsMNuSlq+guJS5D3/MJzty+MtFE5iiTX9EAhXEJabhQJm7p1UrSwVGH+ScCWaWZWZpZnazmVX1ZBrclpnNM7MUM0vJzMw83O9BmpmC4lIuffhjlqXv5Q8zxnP66F5hhyTS4gSRIGKAnBplOUBdo4TvAWOAnlT0Fi4ErjvMtnD3Be6e7O7JCQkJDQxdmqP84lLmPLyUZel7+eOM8Zx7dJ+wQxJpkepNEGb2jpl5HY8lQD4QW+O0WCCvtvbcfZO7b3b3cndfDdwCXFB5uEFtSeuTV3SAOQ8tZXn6Pv40Y4KSg0gjqneQ2t2nHOx45bhBlJkNc/cNlcXjgEMdVHag6iY5aUfYlrRgewtKmP3wUtbuzOXPF07g7LG9ww5JpEU74ktM7l4ALAZuMbPOZnYiMBV4rLb6ZnaWmSVWfj0CuBl4/nDaktYjI6+IGQv+w7pdeTwwa5KSg0gTCGqh3JVANJABPAXMr5qWamZJlWsdkirrngasMrMC4GUqEsLth9KWtE7b9xYy44H/sG1vIQ/PmcxpIxPDDkmkVTB3DzuGwCQnJ3tKSkrYYUiA1u/K45KHPmJ/SRkPXzqZSQPiww5JpMUxs2XunlyzXPdikoiVsmUPcxd+THT7tjx9xfGM6FVz/oKINCYlCIlI/1qzix8tWkGfrtE8MvcY3XhPJARKEBJxHlqymVtfWsvR/eJ4aHaybtktEhIlCIkYZeXObS+t5eH3t3D6qET+OGMC0e3bhh2WSKulBCERIa/oANcsWslb6zKYe+Igfn7OSNq20R7SImFSgpDQbc0u4LJHUtiUVcCtU0cz6/iBYYckIihBSMg+2JjFlU8uxx0em3uMbtctEkGUICQU7s4D723irlfXMSQhhr9ekszAHp3DDktEqlGCkCaXX1zKdc+k8sonuzhnbG/uuuBoOnfQS1Ek0uhdKU1qzc4crn5yBel7Cvn52SO57ORBmGkwWiQSKUFIk3B3Hv8onVv/uZZundrx5GXHcuzg7mGHJSIHoQQhjW5vQQk3Ll7Nq2t2ccrwBO7+7jgtfhNpBpQgpFH9e0MmP3k6lb2FJdx41gguP3kwbbS+QaRZUIKQRlFYUspdr65n4QdbGNozhocvnczoPl3DDktEGkAJQgL30aZsfvbsKrZmFzLnhIHccNYIOrbTLTNEmhslCAlMXtEBfvvaeh75cCtJ8Z1YNO84jtNAtEizFciOcmYWb2bPmVmBmW01s4sOUvf+yh3mqh7FZpZX7fg7ZlZU7fj6IGKUxvXqJ7v45t3v8eh/tjLnhIG8eu3JSg4izVxQPYh7gBIgERgPvGRmqbVtFeruVwBXVD03s4VAeY1qV7v73wKKTRpRenYht/xzDW98msGIXl247+KJTEjqFnZYIhKAI04QZtYZmA6Mcfd8YImZvQDMAm44xHPPPdI4pGntLynjvnc/4/53PyOqjXHjWSOYe9Ig2rUNaptzEQlbED2I4UCZu6dVK0sFTjmEc6cDmcB7NcrvMLM7gfXAz939nboaMLN5wDyApKSkBoQth6O83HkhdSd3vbqOnTlFnDeuD/999kh6de0YdmgiErAgEkQMkFOjLAfocgjnzgYedXevVnY9sJaKS1YzgBfNbLy7f1ZbA+6+AFgAkJyc7LXVkWD8Z1M2t7/8Kau25zCmbyx3f2+8xhlEWrB6E4SZvUPdvYH3gR8CNXeTjwXyvlr9S+32r2z38url7v5RtaePmNmFwNnAn+uLVRrH6u05/PZf63k3LZPeXTty93fHMW18Xy14E2nh6k0Q7j7lYMcrxxGizGyYu2+oLB4HfGWAuoZLgA/cfVN9IQD6JArB2p25/PmtDbzyyS7iOrXjv88ewSXHD9SaBpFW4ogvMbl7gZktBm4xs8uomMU0FTihnlMvAX5TvcDM4oBjgXeBUuB7wNeAa480Tjl0q7fn8Ke3NvD62t106RDFj04bxuUnD6JLx3ZhhyYiTSioaa5XAg8BGUA2ML9qiquZJVExpjDK3dMry44H+gHP1GinHXAbMAIoA9YB09xdayEambuzZGMWD7y7iSUbs4jtGMW13xjGpScMomsnJQaR1iiQBOHue4BpdRxLp2Igu3rZh8BXtg9z90xgchAxyaEpOlDGP1d9zkNLNrP281x6dunA9WeOYOZxScSqxyDSqulWG63Ujn37WbQ0nSc/Sie7oIRhPWO4a/rRTJ3Qhw5RGmMQESWIVqW0rJx30zJ58qN03l6fgQOnjejJpScO4oQh3bWzm4h8iRJEK7AxI49nlm3nueU7yMgrpkdMB+ZPGcKMyUn0j+8UdngiEqGUIFqoXTlFvJi6k+dTd/DJjlzatjFOPSqBCyb147SRibolhojUSwmiBfk8Zz+vfrKLV1bv4uOte3CHo/t15aZzRjJ1fF8SumibTxE5dEoQzZi7s353Hm+s3c3rn2aQum0fACN6deGa04Zx3rg+DE6IOXgjIiJ1UIJoZvKKDvDBZ9m8sz6T99Iy2bFvPwDj+8dx3RlHceaYXgxRUhCRAChBRLiiA2WsSN/Hh5uyeX9jFiu37aOs3InpEMWJQ7tz9deHctqInvSM1d1URSRYShARJqfwAMvT95KydQ8fb97Lym37KCkrp43B0f3iuOKUwZw0NIFJA7rRPkoDzSLSeJQgQlRcWkbarnxWbt9H6rZ9rNy2j40Z+QC0bWOM7hPL7BMGcOyg7kweFE/XaK1sFpGmowTRRHIKD/Dprlw+/bzisWZnLmm78zhQVrGFRffO7RnfP45p4/swaUA84/p3pVN7/XpEJDz6BAqQu7OnoITPMgvYmJHPxox8NmTkkbY7j925xV/Ui+/cntF9Yrns5MGM6dOVo/t1pV+3aK1kFpGIogTRQGXlTkZeEdv27Cd9TyHp2QVs3VPIlqwCNmcVkFtU+kXdju3aMLRnDCcO6cGwxC6M6N2FUb1j6dmlg5KBiEQ8JYhqysudvYUl7M4tZlfufnblFLMrZz879hWxc99+dubsZ+e+/V9cFgJoY9C7azSDEzozdXxfBvbozNCeMQxJ6EyfrtHadU1Emi0lCOC/n1vN2+syyMwrprT8y9tatzHoFduR3nHRjO3blbPH9qZft2j6xkUzoHtn+sZFazaRiLRIgSQIM7samAOMBZ5y9zn11P8xcD0QDTxLxQZDxZXH4oEHgdOBLOBGd38yiDjr0jcumhOG9KBnbAcSu3SgZ2xHenXtSO+uHUmI6UCU7lskIq1QUD2InVTsBHcGFR/6dTKzM4AbgK9Xnvcc8KvKMoB7gBIgkYrtS18ys9SqHeoaw1WnDm2spkVEmq1A/jR298Xu/g8qthutz2zgQXdf4+57gVup6H1gZp2B6cDN7p7v7kuAF4BZQcQpIiKHLoxrJ6OB1GrPU4FEM+sODAfK3D2txvHRTRifiIgQToKIAXKqPa/6ukstx6qOd6mrMTObZ2YpZpaSmZkZaKAiIq1ZvQnCzN4xM6/jseQw/s98ILba86qv82o5VnU8r67G3H2Buye7e3JCQsJhhCMiIrWpN0G4+xR3tzoeJx3G/7kGGFft+Thgt7tnA2lAlJkNq3G80QaoRUSkdoFcYjKzKDPrCLQF2ppZRzOra4bUo8D3zWyUmXUDbgIWArh7AbAYuMXMOpvZicBU4LEg4hQRkUMX1BjETcB+KqaqXlz59U0AZpZkZvlmlgTg7q8CdwFvA1srH7+o1taVVEyVzQCeomKNhHoQIiJNzNy9/lrNRHJysqekpIQdhohIs2Jmy9w9+SvlLSlBmFkmFT2Sw9GDipXbkUZxNYziahjF1TAtNa4B7v6VWT4tKkEcCTNLqS2Dhk1xNYziahjF1TCtLS7dZEhERGqlBCEiIrVSgvg/C8IOoA6Kq2EUV8MoroZpVXFpDEJERGqlHoSIiNRKCUJERGqlBCEiIrVSgqiFmQ0zsyIzezzsWKqY2eNm9rmZ5ZpZmpldFgExdTCzB81sq5nlmdkKMzsr7LigYhvcytvAF5vZwhDjiDez58ysoPLndFFYsVQXKT+f6iL89RRx77/qGuszK6gtR1uae4CPww6ihjuA77t7sZmNAN4xsxXuvizEmKKAbcApQDpwNvC0mY119y0hxgUN2Aa3kTX5FrqHKFJ+PtVF8uspEt9/1TXKZ5Z6EDWY2QxgH/BmyKF8SeUWrcVVTysfQ0IMCXcvcPdfuvsWdy93938Cm4FJYcZVGVtDtsFtFJG8hW4k/HxqivDXU8S9/6o05meWEkQ1ZhYL3AL8JOxYamNm95pZIbAO+Bx4OeSQvsTMEqnYNjbsv44jhbbQPQKR9nqKxPdfY39mKUF82a3Ag+6+LexAauPuV1Kx/erJVOybUXzwM5qOmbUDngAecfd1YccTIRq8ha5UiMTXU4S+/xr1M6vVJIj6tk41s/HAN4DfR1ps1eu6e1nlpYp+wPxIiMvM2lCxqVMJcHVjxtSQuCJAg7fQlaZ/PTVEU77/6tMUn1mtZpDa3acc7LiZXQsMBNLNDCr++mtrZqPcfWKYsdUhika+BnoocVnFD+tBKgZhz3b3A40Z06HGFSG+2ELX3TdUlmkL3YMI4/V0mBr9/XcIptDIn1mtpgdxCBZQ8QsfX/m4H3iJilkeoTKznmY2w8xizKytmZ0BXAi8FXZswH3ASOBb7r4/7GCqWMO2wW0UkbyFbiT8fOoQca+nCH7/Nf5nlrvrUcsD+CXweNhxVMaSALxLxUyFXGA1cHkExDWAitkcRVRcTql6zIyA2H7J/802qXr8MoQ44oF/AAVUTN28KOyfTST9fJrD6ylS3391/E4D/czSzfpERKRWusQkIiK1UoIQEZFaKUGIiEitlCBERKRWShAiIlIrJQgREamVEoSIiNRKCUJERGr1/wHI7lOMx0S3pgAAAABJRU5ErkJggg==\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plot_function(partial(sigmoid_range,lo=-1,hi=1), min=-4, max=4)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We didn't specify a loss function, which means we're getting whatever fastai chooses as the default. Let's see what it picked for us:" ] }, { "cell_type": "code", "execution_count": 60, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "FlattenedLoss of MSELoss()" ] }, "execution_count": 60, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dls.loss_func" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This makes sense, since when coordinates are used as the dependent variable, most of the time we're likely to be trying to predict something as close as possible; that's basically what `MSELoss` (mean squared error loss) does. If you want to use a different loss function, you can pass it to `cnn_learner` using the `loss_func` parameter.\n", "\n", "Note also that we didn't specify any metrics. That's because the MSE is already a useful metric for this task (although it's probably more interpretable after we take the square root). \n", "\n", "We can pick a good learning rate with the learning rate finder:" ] }, { "cell_type": "code", "execution_count": 61, "metadata": {}, "outputs": [ { "data": { "text/html": [], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "SuggestedLRs(lr_min=0.00831763744354248, lr_steep=1.3182567499825382e-06)" ] }, "execution_count": 61, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "learn.lr_find()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We'll try an LR of 2e-2:" ] }, { "cell_type": "code", "execution_count": 62, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
epochtrain_lossvalid_losstime
00.0511780.00934700:36
" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
epochtrain_lossvalid_losstime
00.0085580.00521000:47
10.0028720.00044900:48
20.0014700.00016000:48
" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "lr = 1e-2\n", "learn.fine_tune(3, lr)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Generally when we run this we get a loss of around 0.0001, which corresponds to an average coordinate prediction error of:" ] }, { "cell_type": "code", "execution_count": 63, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0.01" ] }, "execution_count": 63, "metadata": {}, "output_type": "execute_result" } ], "source": [ "math.sqrt(0.0001)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This sounds very accurate! But it's important to take a look at our results with `Learner.show_results`. The left side are the actual (*ground truth*) coordinates and the right side are our model's predictions:" ] }, { "cell_type": "code", "execution_count": 64, "metadata": {}, "outputs": [ { "data": { "text/html": [], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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6UligSdk+eZZv/ciHSMHulzwySj6Pgiaf25ByeyTfm/W3CHY9EesLtO8PTfneFH7hF3+Zn/q5XziMHW87Krcrt+F4uH2k0FVRcEJIyS6GLI036hDAKSTFGljYoQw3okrURLKDSKoEb+8197A13jrOITSZSHYCbJD689mYpgQxJhYxklKyAQ6RGO2aKSaSdoQ4IyUjXwyRGAKq9AMUYyKlSLfoCCHk36X8eeh/qxpJuiClSFIlxZjfJyOCJtAAJJIG7CPNJFFcWiBS7hlwgqZ870kRddZ1SVGNIBFIqCQTANYhIAnUocjQf+pROjt/kjwW+UEsP83kKg9Lue8YOzQuCBGUFicC+QFRVaQfVkGSos5l0pfz2vVK81SNlPbASJYmWXhIRARS0kPl1bpQuV25fVzcPlLoXnh9xuOPvYZqIsTAQXeQBzwSUyBpRFQhJOvUfkawMYoh9WQDJWYCxRhIRCNsDGiK4Do0D7DdjwOUlKKdN4kRGxAd3iuKZE0iJiVpJKWZnUuHGTilZMeqwwkkOlLq+kGzGRw0BWT0kEDq2wXDg0G5eh4Y1G5aNKFl/ixaSf+DTEogxsB83tG0DW3b2sMQjbDOOcQJYmzM5CpiwSG5b1DFOQcISTsbdMjH2DvBZ0L0LcA5RSXhJZGSI4lDvCeosrm9Ydc2blFm9/xjVAY1zYmQkFF/FAaPIC53QWZtHisRyWN8PKjcrtw+Lm4fKXSffvqL/Pqv/TzeTxAJCAFRjIyQZ4BibmAzlCoIpBRyQ8UsDZdyBydU8uwVE0QbSHUxzz4RVYdqAyRiXKDlAVCPquA04vrZBhKK854uJMSBdzZrW5saBEfSYryJ3YMrnSQI2WQRQYXeDFJVPCvmp9gsZsQRIOJci4g3IkmyMzozS8SBExAPIh7EBlw1srW5xd7eHpN2gjhBsUH0OCODl56MDkVEEcmkRbJCUezTCAiOxh4e16EI3jXAoEmVh0NjMEGhStQOTR1Jlen2KYrGoSjQohryb52RUvLYqrXWns2sARpB8oNqn5UzkVtofeCGh+YYULlduX1c3D5S6F6/fIFXX3yGSTtlvjignbZmCmQfDljHCwkRj8ud4JzkxivOeRs0WprG5dkIvAQbhMbZjCFNHnS1jnflBjbx3gMuD5KRQaCf8aLzNN6jMRBCRzuZMp6pncszowiuzEBCP8tJP90x+tA+1xRxUmZgI2b/G7GOtveFRLp8TuuwTKoW04o6EMXhOHW6o21bu0fn+gdf8rirSr4GWesZpmYzj0Z/24iYxqUdqkLbTkyz0ZRNOwU1P13Mr5Aimv1czeQEzjlSzLM42t8rKj2pzI3m8jho/924fWYapixUhr5OfXevaA5rROV25fZxcftIoXti5xT33HUfm5s7zOa7TCY2UzZ5MIyMAAnnjJjFJBAX7ZhsMghix4o5vkWxgXA2qA6bgZ1Tc5HkDiiEdFIc74q4lK9vHXEwO6CdTJnNO3zTsDGd4KShmC82eJmcjjyAuZ1ShjQP6tCDoMrsYI+2mTCZbKIqJCl+rDEG84xikmieFbPZoSRQjwB7+7t473AIzk9omgbnHbiex0NfqcvkHIYcQDXkg+1Tc2+V64GjzZ3oIZNLxHxemmdpTUrUSIpK6Drm846p7/LxxYyLWe+Q0azvsjYi/QOjWH9pGuZ9IGttLo+dmPAZmXHHhcrtyu3j4vaRQndrp2VjK3LylGdyMGVn5yRt29A4bzOk8/jG4b3gncc5Zz4bEZzPJoq6/HnMM2Lk8pXLTNrbOHf+Dpz3IIJ3ecYrs7Rzeea2AfQOGu9xXvA+mzhGB5748u+irqXdvJ3XL77Bhz7wHra2djK/lOLo1pR6mhTneEqpf8WULKgwen/h9dfZ2t5hOtkiRiWkkI9XUgpYRNd8gJpKRNX8PJrCYHaSIDrKQ+x9Y+ZTTPmB9tZMVcTpMPlmUvbPS75n0abXTPpvNIFk/x2SzTVP/gDNWon1S+r9a0gOjmiHSuxndUV7LaAnIqAr/qoSgtKiQfVBpyE8JXhUUu8nGwuL40DlduX2cXH7SKF7/333cv78Wb7pfR/mhedf4p3vfS9nzpzJxHOIK1qBzfRmihVHOYz604IRKBCYzQ9ANplOplljuPXDN56xlz5X5Z3vCWzt7LB7Q3jwnQ9x6vSprJUccnw2XUrgIwQlJiWE0H9mnwe6rmNja86Jk6dMU4kRF/2IzE0m5yRHmM0HWL5HjRRJc6TWKWjAeaVpfDZNHI1v8E1L6lOLYh7KwbdHb8WU2dnn+8/966FEdT2gGgE3mJwYKZIYXTQLACeZcgLqBHxDEm/BoP6hlhE5AbGIP72/04hoAkUwGysO3i5p7Jos7METGUzaY0LlduX2cXH7aPfCbef4vh/402xv7nDHXQ+wfXKrH/CCfiYYKNjPWPZX+bgwtWFjY8pRM8HXBIHNzXNsbk7pujlbO+1NbbzpJyI0viFKtFZGCzQ453IEOvb+pLZtmUyMeN773ldkkegcIS1EVZcjxSV/0cxNhwVlxFkqjHMN3rf2GYqftOYfVA/FVKEoB9aPFsOxGdTMOcl9PfR8MZPKrwcCmDYiJJwzM6x8bkeb1qUpaxC9eSo9kXRkBGoJOlCEzaqvL+UZ3618J4CHrA0cp+Ct3K7cPi5uHyl0nTSc2DmJdx7vvflmRoRatQDIzdaV720GicvH6VvTAobTy+oHfZ+ImMNIPJRUjTc792AuaK/FaIlMrxC6ELSYliGE4TzZ1BApnSw4FZIzZ78UP51kU0/JxLMAjG8aYlQc4JsGcJaiyEA4yf4qVc1kKbO/9KaR5oizEcj1M731y4gs6mBENPtuIK+YFw4nFhyyaC5LtB/3oZKvgZlw1oRC0vx46Jj8jP61FKzlRQTrReV25fZxcftIoSsiRkjBIq5fC266H3/IZ1/L6VZMsJFPW10EGcdX3xqcs4hz0oQrHVWSo1PqCdk0Ta8NFCz7d+zvnPpobXEu52sOZopok2dJj3eOlCxY4+yJolipdoc2oJamZPaSPeiO4tzXYpf1ZqxSAh/DA5Nnb/H2C4n5N4pF1nP0PJPUNKnir7Jg0So57SFJkJPhx0LAmjN6iHLk14icoymFk/J7IMTvEZXbldvHxe1bCF36CKyd582l95HflRaPP/gGPm/iJDvl3VhZOfo3+UARxeXVM/19jjSAwa835DdCmfmkN/dMA7CAiSsagcvEUUGSmMaiHicNInnmdYKTnA9I0ZgE1JupVVAS63PrbbBlpF2t9om1Y9BasulmIdmRQXVTx1BI1/9W7SEwX5dmopZHo5hiIyJm00xEKfmZaLIghaZMTs1+teNB5Xbl9nFx+8gpvgxKeX3D8FZIeZPF9eYm23Q66Uk1XOItMj8PkhN3s08vE9J7f1NfjEn7Zp87N/xtRLfBapoWVYf3jq4LzOdz+77v50weGY+BW3pYYAjoLN3LyOSSbE45N+TrSA4UHDampc3jv4fmSH+NIY2oaAvlK+3bZL/L15DyQdEUxnmpx4PK7crt4+L2Le2qkre3NOzreF4O8anp6L/xl6pKt1iAjItkvDVyCnngc1R6TKTDiLf8Wj5mVXtY/V6kRcSRNLJYHPR+orJeXURysvqIoNl+OYyEK29yJ2WT0A2RWWNW8Ufl2RjTaPq2ZRXt5kCNHNKXI/u3fF9MxSxA+tzGm8ZAsj/M53+PD5Xblds39+Xbz+2jWZ+UsOjsAjoixTfYKryZbDcdcOSvSRCDdXxSSFYV5C09OzYgridJmSmBnhxLTvlDyVlW9aySNDvxpaxcyv8paMz+p9Esa0VY1OIlIw1AnENl/JK+T3rCyvCB5KRxLVHYnPBtSxNby4MkL1VUGbxl6miY4rSxwIjkwAWeIRqr/X0OfZiXbJa7Kauviv9MS3DBZTKO8xmPCZXbVG7DcXD76CpjOecPhVJdpyRu35Kc8haO+RrxZiZYSWGJGpHYmI/Gf/0P9Kp54pwjRulJW4gHZUYt5tIwaL0TXlx21+ciJVjkmDSK3kqei3vzaTzbDm2yCbb4maLNuubFz/2tw7GZFNLP/N5In7J51/vUysNn5pSTZiBPf20dmmQfgIJz+fMcmZZeZumoTTL8ayPTn+LtVynfHJXbhsrt9XP7SE03qQ7FNFwxU44e8FtpDIfN/GWW/HohIsSUDr3mm2kZb6aBlBU35bxjzeCwGewo3+Cq1lBmzGKqaTZ5eq2jN0sG841Dz7vcDic3956TZjTu5qdaZtdyOws5l6LYo94aiO77sdL+lK5v92FkG6zF5Xa+Zd/k24DK7crt0lvr5vYtsxcmbZtPPuSv/f7CaPBhyfTS5cPehLgDEZc+H0VzD3sYx79Z/v5mIvXHiq2zd05IOjLnspnVz6IIqqkMf9/sYQGPEdaRl2bm27NJ2jSMpBbhxR0mKMYmJow7bEwi+tzEko8oeVaP5U7zz4ZE8f5EpeW9mTl+MHTpn+NA5Xbl9nFx+0hNV9UTk5DUqtAX39dRuNXM/nud+Q/DvIuE1KHJkTpbcFg0//5ab6adZAKWIMUq4cbO97FG4JzlHy6RUMQSxot/ipsJbCt3PNJMSCghhuG7Fe0BcSiOPqm8J1BOKJfij8rHZt9UUqVUwkIdokOEeqBembnNJ9WbeLk8IZlUrlQqkZIOM14CmZPSSaOX2vlUcLkQOP0dwBDsGIIex4HK7crt4+L2kUJ3b3+PF196EYAbN3bZ3d3NDTr8eF357+vB1/575fXX37BtRlJkPp/1/qVbnmHF3FrOU6R/v2qSFcd7P5muzOpWum+VaNm3pXAwm+G9R5PSthOmG9ND+1T7c7v+AbhJMcnnzi0ckTgX/Fg1d5f8TatkZSmtphyzvMJmOKeOGzSoKkXXGM67OhC9hnB8qNyu3D4ubh8pdDc3p7zjHedBlO2dLTY3Nw+/0DHj9G0nOXXqFIJw+rYTTKfTt/S7nvy6TMb++zch6IBlv9jqvzf7w+z9ZDJha2sLBDY3N2nbycpxw+/H5tvYP7XcChgizKYZ9P4pgbKUcekH4z+z/Wa5l+NiI+VeSgR70DyQ3D/F5F2619EFSpdJvidGK4VunbH4tqFyu3IbjofbR/p0m8azvb0FKN7f+gH5RphWX885JtOWpvFAxJXybkecZ1XTKH/30VLejIi3eiYPm30HouroM++NOIclrffXytHj5Wbk6VZH5UCkmLWlzQM5yvYqY7Ks9ozxK938ZX87ZVYfNJI0apOKIHqz/la8ZsOpLMI7HHd8Qrdyu3L7uLh9pNAd+4KGSOTvP2jKxTCIt1ZUcmdbTVCLYA9J54rVIl3xVY1mamWIfKfsB1TJFd/6kTC/TiFVOZ/lC1qkNDkHNDZrr2gSUIiSy8wVwolAroIk5T5yqzQT1qGIRpKzaCzaQN7ZtD+3AE4tECF2nYQSnS3PtzrQ/eOKVZEWRnkzWdMo6+ft/stDoaVKlCg48n5eKRcwafKWOFYf9bhQuV25fVzcvuUUP54VywoeHXXE14q35Nf6Gs9r9T1H53srv9esBeT7G8i4oimMvk83Hfu1QbIZMm7fzWbaYd+x5K+62VQbkrtXi3SUCy99UizPTPZybhH6OrI3O9hGPz9k5l++Xnk+pfw5UlB+/9jvlduV26tYB7dvkb0wELIkk4+XIn6tz89g6tzih1/jefvdU0uHHfH7gWSlTqje9J0eQkDN/p0xiQY/+4rZ1f8m/11+0//g5mfnZnIu+8vK7HoYX0xI5GMYm2blk3KNfKb8gDjneiurXN+JLPefFC1ppU9W+qbcQ2nHuCN0OJCv85n+hqNyu3L7uLh9C01XcmFjI+N4+4+SYpObffTsvoKvdzY9/GT9/43+Xv5zMCnyg3YIAQ/7rBB9ZIy8tbavONx7y6z/ntHmhIf89tDJWFZeyzdZDMSbxkFG10OGQO3qvYiuLBDQ5ZOML6uwen9LDZbhGwtIFFN05ZaOVQBXbldulwazVm7fYhkwxKiA1dMspCyRx1IZ6GvFN5SYwuB/Ouzuy4wkebZP2IavKigud1jKnSc3tU1FzSdEMu/QCmmBvj96szT7fVSGAnFjk8T6IKGEPmiax69vtGKm1HimHe/PRKmar3Zkf4+iqFd8jvBqvx2h5Pr6DH1F0XIEEfNDWR3W0nFKIXPxb+E0O/kk96GRtr9/ctk7yVqj5FxJbAM/w+rmh+tH5Xbl9nFx+0ihC7pExBBK+TSXq9Lb9h2DibH868OirLfyG2nfLYfYGocdrwxkXD1tblIamRDLm+sNbBgP1JK5ocufrTT2Tdp0xP3lc76Zv2v1ulBIObotGT9A2ak/fMnNBl7popv0knIKLNF8+DOf+Sbtwq47UgtypLkQUw67/8zx/rE6Vg23oHK7cvt4uH3LgjfF9HLOk5LaNhxOszbAEjHHUeBDnecMN/ENQb7TGGNPtuGLfA8sr8jRbD4YSVOvAbzZqySjjy84aAMrJD4CvTGnmhO7RwU9xlcokeQlUh5+bpHSHvJD6LBN+968LSW/cSnnErAt/3xfXb+0eXymw9uhS29v0siWfmJLQH8/oHK7cvu4uH1L94KZK3mQou2DiU9Z7aYnZN/pI5K+2YR+1CB+PbmMg1/KzA3pza0yw2Mb6Y1n9tHfS8TNRO4Loown0FUCjjSJW5HTHspMhvJjGf6CZVLaezecU/JvesKuzqrDQyNlq5K+zqj21xcRNBU/ljAcQTbF7IEZbjtX5C8dS7lucc4NxVhk9H3RGvKz0z+Y4+OOE5XbldvHxe1b5unGzgYw+pR32hwPvt2IfW4kLhpB2YdpMDPMdAP4xio7ajOMOpR4yGyutr8TZda39pZJ67DjY54HU55pU9LsL1vRFkZTXe/70vIwHEJWHR5myZvulaWJZbCG1CWWiaO26ymFI6PTFpIa8cyRVpJrCodGz5adT8avfC9O+o3/0ohcSOofKkVQiagWP1Zi2NIkn5xBUJHbXjpgSbs5RtlbuV25fVzcPtqnq0KMeZCi+b1AkGR7L1kjNO+FJBSns+1flJa0g7JfEyNfyrKCXzp51NrVr8df9Z0wDGYxAcrfZVYfBxFKnxxqbqXUu+ZT3rfpMIItEXClkcvHDyTtb0e56Xyr91/67bBjhquOvzM2FSKa68tIbDnfq5WSVk/qUPWgHmhQ5vb7XJy5BAmKCWt8jVnArPjtypX6B0hXVJebtaljQeV25fYxcfsW7gWbOVNSgk8WbEBwnjzb2+wjNhnb9VNCveCcYhrAkASteaYokWJY9r+8VawSijxjmx9rIFlhwjLBBi2gn3lv0gjy5+lwtWU845drlr9vDgQcQuRsetksT3//5ZhVUmr/2c2m12EEtnaUWTovwyGyTKLxexsrxARKUsmkU9thNffh8DuLHIs4NK2QXoYzlgdRGKcyHb9rASq3K7ePj9tHCt0YEyEGI2GAEAIiIElsR85kKRTiHC6JbbnsHErInWiFJsqAlZamNCR9S/HnQE/im1A+KjMR1nFFISh1N1NPurx+WrU3JZbItGRK2bHls5Ti0IkrZoRqTjgvzhzUskzwWI3QhCbbDkTYRyWRUmPfE4FEzMsOS0kMEUhIjja/iYZQ/i2aQumxQgIt73X0Re9dy/8vSy+1BM9syqnN/kVrKNqT5PfYAzX2SQrSn7vXEJbUteG1/JBp/njwAx4HKrcrt4+L20cL3ZQIscuznrf6mDIQTvr3incOM3USLjfUtnZOmZSCOvKW0IcRU3lTzWDktwHQtDJblnXm/Ww+9lENs/+YnDo+Vot/LHeaLhNXM/FVygAPs6P5iWw2VYTkDlCNHOw5Xnxlj6eff45LFy9x+tRZHn74HezseKJLBNeRXJPrhJqXjVEbl25/VTvI2kS5l2L6Dj0JiOvJpTBKH1wNMBT2DBH64vUz0uZSfzeRaCQtVvxe/TeH3Itm/6gJkmMUupXbldvHxO1b5OkKXdcB4H1j+x+xTEwjn2apPyqKrMPMZccNkdSUIin5/lxGyuWbGZNztf2FmP1snmfwpEqMacnEK2r/oaRMqSdm/znjdo7NvHKPukwCxZzvWDCgWzQ88djLfPRf/zy/+5kv4NsZ06lD9BSTU2c4cfIk9913P9//fd/LyVMdG9tCI6MZ/hAcNsBvhsOJnR+qXGikPEjkNudf4nwp/qyYv6scVx7EgYzWJbLU5p7USw/YSnu0RJuPG5XbldvHw+1bZC/EbHYJIXRLxCwrSLz3eeCyr0Ql+71Mgy+Lekoiur23te7LJlda0gZu8vtkU66YGDfN8GnZ5zUQs/TH8kw7HDtoETHGYpNkcqbBNOt9asszbxIj/o0bgce//BSf+O1P8cUvfZGLl5+ncQs8yuJgQbd4ibjXcPHCBq+8eIrHH/04H/rQ+/nABz7Affffz6mT22w2YWkmXdVgBkjmz+HDOz526MvyStncGr+MgrZFNqBpIGLxe1E0grH/Spd4t6pJrbbpa3nA3m5UblduHxe3jxa6MRAWID7gpCUEa7BzPv9LTllRINryOBweh6qzVXLZ4S6UxGlbfhmTddI4GFFmiuUgr+XmaRwSrguhiiPfNIBi2iWi5u7uSbRsUll/rphtK8tAUSzfESWWaC8l6uuy5pDQBE985Wn+1b/4KM88/TSq+8wWV4Fddg8WNM2ElALz+T7olJTmhK05abHLI1/Y5cbeZZ575WkeeudDfPBdD9FO8kMnjlRm6j7VxlkKTOm70ViVYISZyMsP9zIfst9LBEu/8SQErxFykefSSTa7FyEkaNkGJWsvKQeBrbRdtpNFehqbZpDJLFm7WBIoxyeEK7crt4+L20cK3ZCEeZwjLEBaQgioerw39Tsl8B5SErxX1OVG5TY6Br+XiCA5clp8TNah5TXMFlIqBJW/e6YOM+N4Zi6zdkos+b5WZ9P+lVb+Hh03JJPT+8GSpv7VBzk04Uh84VOf47/5R/+EG7vXCd2MEObEGOhCJEUh5gCFk5YEiFMO9vfoFgv2rh/w2isXOX/X3Vy6sIdunuJ9D5xnm4BLSigPlJiPSAuxZBjgJcJpqXS/ohEYzShaVNGobJ8qI5+g2Vdmg1KEiJSlkJmwgx5kA2cPSf6b0cNA6dNCas3awphhxyd0K7crt4+L20cL3X1PuA5+C+YS6ULoB9G2v7DGm5O6dMEwE6kqZQVP7x/DIscplkT0Yobp8K+mYXaDnCIyup0VwoH0s3mKacmPNS7XZ6QcE/rm45b/PozAoBpRIq+9/CL/y0/8My5deZ1FmLE4OOBgf07b2C6zISQ6iUZiB85bVf0Yo+17xYLFfI/Z3g3eePUij37lZX7wT3wn3/7tH+LsbafYYIHoPBfzkP5hVbUNQW4ioBOIgwa0DF16t2QSGc/xbtiqREcmc6aUkbs8ILpMssE8Xnlgiga20g7Tpo5R6FZuV25zPNw+Uui++Lmn+B3/Bb7thz7C5r2RGELfOd67Yhn0HZESNI2dslRoOizXLqVEjHarTnJRi1xP08yq5Y4cp970N5VJv1SOL9+snT/2xy4HHlgyA1bJOD6/aRkxm3j53HlKS9rx+BNf4tLll5kt9jjY20c7S7yebmwyn3c4Z0nc3ks21xJdF/N6+rIqKBBTwPvEjdcX/PJPPc8XPvN5PvCRb+N7vuuD3HVbQ9kWelk7GhGBIQyQtDzgksckM+ityDcZVygdY0yucWBEV44avS/EL/1dzjO6j+NE5XbldrnCurl9pNC9dOEij3/2C1x69SLf+qc+wm3v3kS3GnxzALqBd5bDp846xHkhxQg6NF7zDZsGYWS1Oqa5swsh3bJmMMbyZ2IDmlV/G2CIqoSUiEnxsQyIdUVvco0H81AiFvIZ2WOKpAhKJGmwmVATISUuvfYGn/itz7C/N4OuY3t7kxBauze1oMVkOs2aUmK+OCARc3Q7ZiblJYiqLLoZTXcN13S8+MzHuXbpeXZvvMG//yM/xImN5QEXLMhRhloLNfsZXvpjQfv+Hx5M7RM/RcFrYoZnow82lPErRHL97F+CEQCiJSWobxxgpret7ddcBm+8Bbhk600Q/FH0e1tRuV25fVzcPlLoXtl/g9cvP8eN69e4cvUC7/zIB3jggw9x/l1bMDFneBJoU0KSkGT5QsV5LyJ5NY/vCTGO8JbOeLNKTsuzh/YJ44fN4mNTrGgM42PGbTtMI4i5pF/K/iqFfC5bmqRJWczm/PIv/jJPPfU0eJhOW7oUCDEQutjft3dN1lgUVSGEGSklXOPM05Ry8RS1B3p+kJi0kAhcvXKRCxdepQv2QBRfYvEh9Q/dqF960qEjIqxi5bNCIM19ruXceQlqH5QoYkb609ia+SyATAKNHg57rYqZ8nCk3l92PKjcrtw+Lm4fKXT35te4sPsS2+46s3CVvf0bPPbY5/nTf+nbePDdd0PaIsoUcQnizdtQjH1diOAyUWKMtmOojHcMHcgzJmVP7FFHJwayFtKF7JMb8hCX/V3l3/G5xseMTbmiDagqKWsumqfDxWLBz3/0p/j1f/OrdGGO+AZigiR452g22v5cMXa0YiXlppNNROjP3TQtGpUQuqyB2MaDIURiCsS4x+7utT5KjjIKJNA/mJThHc32S/2VhQKjJZ1jDBobfR+Ve5USnCBvRIiVC5TVE4xMZ7tuHPX5yvVUKauCDn1u1oTK7crt4+L2kUJ3kSI3FteZyT4H6Tq78RrzCx0feALuObmAye1w6g5kcyNvUzzsgNkv6yskG83yZT+qYeY3E2zZr8PS+/EgjAdlPKuX98XEW7p+PrZ/31fUX9YsdDSTlu/IJl9Kid/82G/w8z/zs2ZqimfWBRbzRIiKcw1t2/TXCt2cvf1d2mYKOLpOadspSCIG8/mBEFOH846U5hwcBHALvNvk8qULZq5R/FirmtHQbgqhxuj9hbeSbnZ/vb+sHF66rtemctVYHWZ/ZSCYBe0HTcDaJkv9WY44blRuV27ni6yd20cvA9Y5KQlBdtnrlD0NuG144+qzfOl3r3Hq9rOcPHsft937AXTnFM1KJLbsvtmr9hnFBCvagoggaWR6SdmeuXTb4TfTm00jIhZNY6wBLHWMsEREMvn6BPEVYtosaulEV6+8zid/9bfARYIk5rM53WK/D0ak5EGVyaQlhBmLLrC5ucF0MuXG9RuEbo4mW+PvnKOZtDjXonObeVOao+poGk/QjtTBIlj+okuRtPSQCmjZCyX7l7DSfUVrsOwbBRm8Y6MNBfpSf1aCVGhcQ0qSNRMzOYfNDfO1R7qA5VpKXqEFUhLP1WPVae2h0r7OnsMCJ8ta2nGgcrty+7i4ffQy4OQIcU50kFwA9nnfnRtcf+OAx6+/yM6rFzl77gp37y+466Fvwp08Dc3g+3KFaKN/y6w9NrfGxxgx9ZbEXCVeCGHweY00hPHxvQbxJqRV8iqesU8seVJyoIGLrz3Piy89y2wxI2jAi6fZPMHB/gzvHd47QlxwY/cK3gtOPLN5JCzmaJI+0OCco2kaUkq0bYuqFVyJUWlbR9tMWHSRixcu8vLLr3Hu1D05xHJrmBO/3BuMpvSl/lzuSyOsmVypP0IpSfqHHD+E961w9FIj6Ilofa5YIrrCyqHHhsrtyu1j4vaRQveD33o3W63w+KMvs4g3uPP8aRofufjGRdqNCYutQOyU/b0FeweJhz/y7fjtjSVVexxQGJtNYxNpnIKzSkwR6f1cq+ZY6ZzBzxRvIv6YwOXfknpyEzFV0ZF/SFNCNBJVcZq48Mrz7O9fY+fUSWLqCN2CvfkC1UTTNEwmE1o8zgkpBQ7258znHUGEtmmYtBNCHPxzMVrdzhgTMSZEPODySp8588U+X/nKI3zgPe9gkmu8LpkyvQk5TPGaKz31Gwge8hCOmSk5mIBaepMVyy5r0uVQ/9pN7Bprgfl7+4ltMDiGXf/4JW/lduX2cXH7SKF77zu/hR/6E9/K1YuX2Tt4jWe++hKPfP4JtHue09tncXoKEcf+Aq7sfYWT5+/h3AP35DuwpAnbXqNUbbo5/7APNojdg5ltZjZI9mKPO+emm2SZqKsawc1+s5vJWL4fksbzkaqodqgmUkjs37iO945J49nc3OD6jStc29sjqbLoEl3XIeLxzYTJZAORBt/sk0IghgWT6YQmeWK04EKMwcwr19I0xR9oSz+bxrOIHc4nkgSU5qZZudeTFDOZ8lJOKTbY0rxfljAOv+zfaTmHlTNMoz4ofSMjraL0az+79/2Y/WCpmIiDD63XTmQ0Bm9Jv3l7ULlduV36Zt3cPrr2ggZScwebt53m1Vd2efWZi2y1MN05zfbWlCQLru1fw80Ct2+cwTcNi9kcJqUjhjqkhZTjNJfxqhAtWoPmsmv9HkjLeLOE9EKyMTEPnfHLmI1+N3TcykwLgOsnu80TW+zfCJw4Nef8zml2r13EowQESY4uBLo0RxHaZkLTKJuTBtfAvBO6CK2Dzekpog9omhMDuNTmZPI5QPbdOUQa6zs8/Trv/gErG6/0Hq3+PhDBNvErRIJU8guLdoWRWVP2T0nKN+zQEqvvn9KEYj49JVCWVJo/qzBvKH9o10x9y8o+KeJK++1zOWR814XKbajcPh5uH5290CVefeFZvvL5z3Dp1RdwYZ8TOxv4jZYYlLA/Y3q65cH3fJj3vP/7oGlYzDoEj3M+d1CZPXIkN38WU7Jya2pJzGWGGXxgQ6PfLEo7JlSJGpdjloi/SjpZ/m35zappp6qWr6lWgKOdTpl1+zx4/12c3dzmwqUdDrYirgvs783pujkxqa3LT5GtpmGncXgaDlSI3pLcZ7MD4lRI0TFtN/CuZT6bM21gPp+zWCxwAoENLl28SEpx2Wo5xAoq7S1fL/+tPX9W+204YcpLX5c1LPOjOUvNESi+rL5K/pJjbPm3Yw1h6aE6Plnbo3K7cvu4uH2k0H3y0UeYX3yZtNhFNKACXVDmN2YkEnfefR8f+vZv4/T5+0ntBoukuC7hmojPEdyCsTZg68cjXo0kokMRkGKujYk1nGP4uxy7anKVxPRxMGOsPYzNuVUTbPXflBIqiUjE4dg+eYrN7R3e+673Mp0lXjt5ge5gH0kRaRSaBNIw9S0bzZSzp3c4e2KLMF+w6GBzC27sXee51w+4OpvnZZeCl475YpZrfpZqUgnXOu697357yLUkjutwIyOstn/1c+uH5d8IRXDEbPamLBDGZpYdqcTceaXGrPZnsesk+gpVh2lWq319uLK3NlRuV24fF7ePFLr7Vy+x1yYcgUUIBIXpdJt3vONuHnr/e7jj3odoJqcI2uB1qIKkuaZo9K43EYxwo9kpKYjj2o19ppMpW5stlKpNRUXveag3EQyWgw3jzui3mAbbPG48UL23ZagGVQZCdVwIJA11TEmoKG3bsnNim+3pHZyYzHno3jtJB4nJ3i57ruWa94QktG7K1mSDU5s7nN45yfa5DSbTTYQbvPyK8tqlGcwtdWgx3zNzCwhhCL6kGGknLffdcy9eBPrI67Lhsjyr97dDv4OsmCZg/akDqUa8Esk97ga/1U393SsKtoZdpZxjlV2ZcdK7xEafF4FSjjo+qVu5XbndY83cPlLoHhzscnVPOLG9yd333sMd9z3I2fN3s3P6HK7ZAt+gJLwqPjlIoHkGiSkRUkDUfELeNThvdxdCYHa9Iekun/3cp/CN8Md/4I+xMWlxfXTX9cQ0LT/d1L6eiCtBBsml6pY76/Dfjt8fNpORJnkGdKRuyj13neVg0dGmwMmtEzx47x2c2dtmdz7j6vU9rl2fk2LD6VOnOH/6FKd3ttk5ucHGhifREBYdL752jTd291mkQJcCLjkaHCoNzk3N5ycQAMTh1AIOomJboOQ6r7r0wA5+MXs4ITlYdArS4CVBUntQ82t4SPN5fbM0cw8maykuWpBZKjo8L6WfRbFyUMNqLMZaDIN2cJyo3KZym+Ph9pFC96577uaP/8D3c+7Ou2mnJ1mkiJMWZWLbirib/Snji8eYy+QJxFIAg8R8vs8rrz/Lwe6c3/3kb7O5eZJv/fZvwcsO3ovNfjKYXPbP4Rc61Fc18mGVwXqz341/M/5cVa0oCDP29+fsXl5w4dVr3HPvPcz2d2ljIInnxIlTTLemnApzzpzeYTGLqHqm7SbTyZRJ29I2E4QGJ8LJk6e548xpXrt8hUvzUmHJuJakQwI0YoU6Nja22NzZIKWYZ+DizB9m0sFUGv5Omgg64/rFS3zq04+wtXOWD3zTu9mYtExbv6L1DP3jpBRyXo4OD0SSTOgIOWlc1Q5QHZt4Ln8v9GlKfSR4UEaOU9Ot3K7cLu1bN7ePFLr3PPgezt/9bnyzQdIJKewjXrLzXfvGWkX85XXkKWnZDQMcw4oOEt413H/3e5nNb/CDf2of3zY4cYQQEZzVzuw1gRwdHP19GKkO+3dEw5vIuewPWtYKxu9DXKAhcv3qBb7yyCcsNcYpaR4J3uHchAkep1OaaUdo5oSQ8DKhbSZ47/ocS+c8m5vb3HfPnVy/dpXw0htcj5EOiCL4JJzZmHLv6W28KBvvupdzZ7axFTQtxRyUvtDz+EEa7t05R+pmPPv4Z7nwwmNoe5r54goPP/Quzt9xLpuWo+h2IchSigzL3xXk/agsClwWC5SBFnv1msAhEZGlQTj667cTlduV28fF7SOFbpc6IhHRiKYFotFMKufoc03Em4reL8ez2chFm1UU84dJ3lcKIKUSwZ1y40bLiZOnGfudUpL+4CH5fCDRuLPK7DdsVa3IUh7j8rFL/TIahCFyPF65org0pfHKmTvv4Pv/5A8RugUHL7/MwYVXUDrbK0tBnKekAnlJCB4vDhHXF8QWJzjvOX3bKR5+9/20E8+TL17g4kEkirA9mfC+B85xOjYcXL7Od37Pt7E9za3SsQZwyBivzu4p0boT3PWO29ide87dfprtE9vEmHA5JWeA9Ne4fPkG3il9IjrLyf/j40slpp6osqod6uhlFzis9sBxoHK7cvu4uH2L7XqUmMpMryRJuJ6AeZuPZESKqUOiEKP5q6QMrPfgHF4BkXyrDhHPwf4VHv3y59naOsk7330/07YhRMUL/dr2QsglLUCGQUop5eBC3nIkT5ODL2g8eDcT8zDtopBfNSEEvJuAwJNPvcwddz3AqTPbhL2WjXQaJ4nEPl2YEYJH0wZO85ptxDShHEmVvC31hm5w5uwZJpsCvmX3qy8wI/LgnducaIWtyQkmG1PO3v4gbdwglGCDG6ojrWouS9ktmEnXcYa737nDpcsLdrZPZL8gllNYyJG1CkFIIfHKqxe45+6zy8TV0jfl4S21RkcVl0j0ZfL6huRAzZic+besCJh1o3K7ctsuUPpmfdw+UuiK5GRmZ7ObjNuarN5oWfInLiDiiNFILICKWPV8AOdwhSsKaGI6bfjgBx+maWzKC6W+phNEhxU9w3JL6W93iUQrr76No4E7DIeZXD2Z8+yrDhrX0O3u8fTjXyR4YbK9xf6swckurcsmSZzSOI+0NiPGSNYEJnjf4hvFiRJDXgXUJpoTjgfuhieffxXvPe+57xyzV+bshTl3nL6NtpngVEFiTjC/+X6Gdg+fCeC948beNT7zpS/jmvO8673vZtIyMp1v/n1KHWDV/u10rteoDuvXw/pz1YQrK3dKQev+4yPOsw5UblduHxe3jy54Q5lpynJG6T8rZx9OrkAkxnwjCOpcLgyihDzzpmjHO6DTxLve8z7As1jMQSPTtqFtPOJcX5f0JmLKaicVX1tJHC/NXWnvm3TG8oCvENWZT+rEmdP84J/6XqY7J/ndj32ax770RU6e3uLU5hZb04atdgsvSkgzFjEhviEKhOCZTDbZOdGw0Tq8CDEquJbNtuWeO+C2E1tMmx0efNf7eW33GRo9zekzZ9m+c5sknQ3qLTSb8llKFtQRhBius7MRWcSE+gbvImgiqtp+Wv0W3Pb72fyAdpJnegWrHZrwOcfypof3FjCijoMQMjrH8aNyu3L7OLh9pNBVzb4rSYg4UohIsuWPiEP60s7OtogebVOi0SFe2T0IfP7LLzC7ntjbvc6N3ctMN1q2d25na3ODnZ0J73rX3UwnkTCZEDdaptOWtrEUD+cs+CA58CgONJXrmn/KBsOW+w1mWjEFcvm1N7tHRgUw1D6xe8/3r2K+Lb/BOx/+MDFFvusHlcuvfZknn3ySl9wWp06e4I7TW0zEs7s74+reLlduzDjoZiAd00nL1nSb7e1NTmyd5syp27jt1CantidMtra4787zHMgGvtni/F33cu7UWeTkHUy2tog5ADB+YErLh4Ibxd+UKAVVYoQ77riHh9/3MK9e2KMVS24XcUjqjCApIihelShCWOyCKkkFCYHHHnuSG4vIww+/m50tK9RNcra+PrU0EwcS6bcmKdX2ZTCxtETn01BnwAh7JC/fdlRuV24fF7eP1nTHUVsGEyclxYtSEsJTTDix2V81ESIEibz28iV+9qd/nieefg5iQGNkMm3wjYCbEIKZcP/BX/1LvPe99yMh4jqsALJCSmaG+aaQczwvSnZul+Rxm+lKeTmyX8acZEftxTU234bgxFLnKVh9zwZV5eTt5/nwN72PN15+hVff2Of6pX30/O3cdcdJNM3oun3296+zN4/8uQb+z69c4p+eP8+ntUG6fU5MNpme3aJtPKot73v4YQ6SR9Qx3dqBDceZB8/j+6WL0Z5IxtpNmbF16V5Kerlznu3tE3jXsrEx5HUKWalT6zsryRdIOFrfsL+/y5nbPBdefpFnHn+Ujdtu5+LrJ7nedHQH+1y/ussszNjfFz7yLd/CqVM7S3046tXe3yVSHqHS/mOXuZXbvwduf/ulC/xH1/b5x+dO8dntDZI6Dna7ym3eGreP9umOOmJwGOdOKRdQBRkiq0kT4j0vv36Bn/jv/0euvfE6rp2jAm3bkmhw2pLCApKjmyu/+m9+g3sf/Cuc3mhRjcSoiIShHTngmndRpoQsRAaHddl2pG+T5vXUaL/l8rjjhuii5kr7o3sdmWRDPmU2A3EgDSdOnOHuO+7iYO8VFi1sbwr33HUbV646EpGoAbk846+9fpF3x8j/4dU3+Mq5u7j/rju5+84znDwxAWAela0TO7i5BXI2tqcsNjbZuftMP8ClLaujs4xll74qnDw54dKli3SzDQINpW6pIkt9k1Ii0TCdbLO93eFkwenbW07fntjdf5HF7m00m1Ma13Dq1ElONSeYbpxme3uLlMCNovFppZ1aGjNqb6/ZvUVT7u1A5fbXz+3/+KszHoqRv/bGNX5Mp2xtbnDnmZ3Kbd4at9+CTzennMgwaGVMNftPEmm4bg4ufOmzj3LhlVfYmM7pQmJz8wSbm5vYts+RxSzhXMK7xPMvPMW//Xdf5Id/+HtpJOFCYOwjUZyt3RZw/Yw9dID5u5a1FUgUd91Rvpai5azWwex9SGMyI3hpSO0E76c8eO8D7GyeYH8Wuf32bR649x4m7iKhmzLdPM25k9f5yYnnr752iY8++ADvvO9O7jp/lpNbmzhV5t2ceQzElEADu3uXCbrBPQ99E7Kz1c/6/diu+uTy56vpQ9ZYx8aG49WXX+SLX3mDH777z7HT5Kh5f6oheGOakM9/N2zsnOY7v/uPs3djl0XwJAurgwjeTZlsbOZhECy/ciVIMza1dBin4S6OT+AWVG5/fdz+mfcEfuypZ/jJO27n/lOn2TlxmnecO1u5zVvj9tGlHVPs1XQcKC6v0isFInxW+Us6i+DVk1S5/MbLCPtEIsiEppnQ+AmLOCOGQIwLyMnioh2f+Lf/jve//2EeuO88yS3wtLQSbWM8aUEVUUdygrihI2w75AgpkWIA5mi5rb5a0LgQsWkSy4RzWbsp+XujjpOEEsy4EcAprQpxOqWbd5w6eYYTp4XTO1tMm212NgLbGwrNHlsbLS+cuo2/8zBMJxuc3thCg7C319kOqbqg08622U4wOX077/nub+fEifNEcUjv13LcRLzSyp4QDk0utzog6lHvaB28612341IkaoszGuHEI+JxrkGco0lKJwGyAFA/pZ2eZjI5wWR2QFgsiMEKU6tvchrPAtUma1qu9xNae7LrsW95Sf0xgmuygNRxoXKbr5vbT94/4e/c9w5STNwVjds7ldtvmdu3ELqZlHmg+vfO21pnV2amBAmSOMQ5rl27wbPPfhXv83JJLywWHSKOEDrCaPO+xnnQA/ZvPMe/+aWf4U//mR/hHe94BxsbwVxbokgUJDfVqc3K4+itOb/zSpSkdtBRWPl6WVtYnXHLzFY62darnL3rLl7/yuP4ZkpKAe8EjcrmlrBzIrB/KeLcBtNNj2Br5mfzGSEEfGODGQmoi4hvSKnBb5xi8/QdeVhsz6VVLeYm/1LRCvpPpP9H/IRz584TLh9w9XLk9Mk5U0k0OrEdoEQohbidmB+xCx0uNSD7fOnznydNzvLQe+5h2m5BKiuv/JJpelO/5jHqzS8d2mkkPkR7WTMqtyu3j4vbt9R0basNZ+9TsBMnn9dKF5Mn4dSKUCQiX/jC57l48TVL0VAr3ZZSYj6fI6K9uTRsPeKIXeKrX36cF5+7yvs/9CF+4Ae+g7vuugPTQWLvI5HsuVYZyrNpcr3moloKEoPokCB+WAfeXBpvcNbbX0PFi0JOARDHidvPcuKOO7hx6RKz+T53nrmNzekmzsM87vPGG5c5mCe2d84wbTwxdsROCBrpYiIHz/HO6N6lgPoWlYm57FY2QjwMy368Urourwt3ivgJvt1E05znn3yJBx54rwkUPDBf6oxSMtCJI+mMFK5y7cqL6HROjPfgJxMzaVVzZL9ElW9+UHpCHtJOG3sG8+yYULlduX1c3L5FyliJAua94TWStCEHCPsZ3blswqTExcuX+e3f/m2cG2ZR73yusG+zQFkfnZIRw7ctfrqBTx37u8/xm7/xAl9+5Iv8uT/7w3zHd30ItzVoAIVgrkQeFOjTe4ClQhQ3+7JGLu9D/WF9x5KjxhR/zWDyOQU3mXL6zvNcev1VGu/Y2dqiaRq6tMWJrTu46x03+MqTTzGfb7A1vc3IoLZ5X9CEipj7XyCGwLxT1GVTNi/sLzU8xTp/1PKh1OCAwUcn4vJD5UnacNddp9k/eIaYHiRIwxIjdNSN+X0AVHf48Dd/N7EVJo2l1DhTz/IEP57RC0mlb9/Qh9p/qqPjjxuV25Xbx8XtW6SMWX5gKTRhA2kFn1Wb/Mrbk/iIxilf+OwjXL16kWayQUwKkvCNEjXiEIsypkDSiEOJSWmaFt86WreBNHNCuMHV15/gX/zPr/LKGz/Mn/1zf4LbTwiUba/zTdoKlETSGcqGdQS2hr6/fx16XsYjUBI9ivMdC5xYrc6EFboIgKUKAbnUW/YcqeO2u+7CP/II89kBL7z2KrJouHTtMjeuX2WxmLM1PUUKgfniAMnLLfGWG+lVcerQqCyimWGuW+BCQpvcT9xs4sj43xVtBoZZttEJTveJbHDunm1uu/M0bU+doumkwSfoEkLDBIWoOLfBzu135+27l6k0pDKVT7NfTvIDXLZfybmV5GFYzSnVt0DQtw2V21RuHw+3b6npWum14W9NyQrZycj8co6oMJvNeeLxx7AEZvNbTVozKWwbIiOErRhxJHEQsBJ5ah4l71qmkynd/Abd/Dof+zc/C8Cf/fM/xG0nOxxTGqb5ts18kWJ2pLJ2Ow2aRx5IhGzGrXbGyMQaEbbvSG3sla9l57SR2j59gs2zm1x66ipvXH6Bg4uXuXZ1hjYNp87exukzO2xMG2KKeJ2jeHPmJxu8KNCFjpiU1rdIChZ1aDywXNnKQiTjFsNh2gzke1VAHV3nafwOzk/J1h7a+zKXz3fi5AkmzrG/PzeNQspVdakoCeiSorXahMOatIo3i7ivC5XbldvHxW131JcpGTGTdvZKqR/4PuVFy4U8L778Kq+89pJFJtXMtqZpLYooViwkJRvYnqRqu4eG0NF1gZSEtt3E+waXItLt85sf+xV+6Vd+lav7iXmwbZ2X2kCeqfNMNLRt+VX2mhp/lpIO5oz2I5cHJBc4kWFAIC/FVAG/yb0PPcju3iWuXrvGZJrYmCi3nTzByRNbzOcd16/NSNqSxFY/JU0kYBET8xiYB+vX1jskBLrFnD5aujSStxzqpcEuj1BKlhMqMiTh9zP06KGULETatu3N5XK+3tzNGGsoxYc1fpVzmhUsN4uC3qy81T29fajcrtyG4+H2rbMXog2E+bqKT8ndNLhR4emnnyaEmTnXUdq2GRrWN9i2CoGISIMSSFFZhMTioEPU0TYN0JDiHCeQFpf52K/+Cs5t8CM//IM021Zh3pJ6UjZJhpmrDMybTUtLM9FIQbC3Mc+m2ZRwYWTq5JVKucqSpil3nL+Xs+emXLl4jYOYoGu4+toNdq/u4bYbosKVq1e5/dwpTuxs0sVAiKBJCFgVqdYJjfdICly/fJnbNzZx3hFipGmAXNnJtpAuWouObmC8Jma4x6QdMR6geoBIY213tjxSVJCYfYgiIMPKJuf9yPwbfG7F/Tb2cS11pOqoQwfBpf34/P5B5Xbl9nFx+2j3Qiamc54kjqSR5GxbDLuo5Jk9src355EvfwGcENUjHrz3Vn8Ul31UEbJJJ3mNuS2vFFxIEBckhU4dTdOQPMS0oIkT3ME+j37qd/iej7yfEw+cw7kpQgOipn2khCPhpCOqI1tJN3VHT8oyo6Vl8vYrW5J1tGAmk+ud5eYLFBSVxF68jY2z72XrzC7NXkOIAadz/AZMTzgmrbBYzNjdSyxiQJy3ZaXAIikaA9snpmxOPROJzC68yJXZPnG6zWeeeImPfPM3cee5qZUdzANvs2ywZPaSwA1WZlAEL5AkErzafoIIHtudVvPDtTRHiyC0SE5HKttZW38UUgpp3FcqDAWdE+M0mlI7FqSvJaB9cKcce4xqLpXbldvHx+234NPN5ohY9FFTyhvCWeDBhjLxykvPcv3KJRpns85kummzvZYZ1vfn0zR2YC93uOVLai6j51D1xJi4/747+JN/8vvZahNdt6BtJrn4RGLk4eq1k95KOCTKa102mAe9SXBId1lkM9lDhZJkymIeufjGNZ557kU+8fFHuXJ9j//t//ov8sWP/SwnpjvsXg1II2iCbh6JSeg00ekB0+nUCmWrMp8v2GxbNiYtjfO4psGnAyayR5eEj3/sM3z6Uy/wg3/6W/im997Lye2JFZHWDnvyIi5vHYIjBwa0J0XOeEdw9Fxb4uMQMe9nfmxrk3G8hhXTipuIdZgIsI+lL/481mDGvzseVG5Xbh8Xt28hdMmDvDy1mq/JZs2UPILnmSefRmLEuRbfYHsnuZaUAqpxsHR0aE5Zz5yS4vF473FOcd7l5eWCk5az5+/ge/7Y93Pm3Cm8137GsbSe4rMaSNl3oAyK/2FdsNS1ZicM7wvdm0SgYX9PeOWVyzz93PM88fjzvPLSZWazQEiXOAjbzCfv46FveZUrzzzPbSc9KSntZIsQ5lw/2OVGiISEaQQRuq7DxcTOqZNsNA2td7TthEnTgkx59LHnuXLhBbrJK/yL/+lVfuvcfTz00Hne/fC93H//HZw8eYLGBYT5ocaNpbk7nHgj7mrle1n2XzlXVvzooRxbRlnyOPiu9M3IuURIzSk/hUPHh8rtyu3D8fZz++iUMbXUC82pJL0fA5uFNa/U6eaRl156BZ9rhTZta43u13cXf9lQqKPvEDCSqViAwXmaprFqTl3Aec+VK9e5dPU6D7/v3bSbm+aXURgSvJfzEgdntt28G+UCLnWG2n3YKfzoW0cIcPHSHs+/9DrPPfsqzz7zOlcu7TKbXyfEGcqcpAEXAjrf5yd/6hf5K3/he3nq0edo0tS2cGkcuAZiY8GURSKwIAYLANxx+xm2p1u0rqFtGqbTFtopszjl137jU3RdwjWCdm9w+eKcT118jk9+8gucOn2W+x+8g3c+eAf333eWO86eYGMj+5bUUfImw3zGtRvXeQd3WbpMIY9qjgCPpvxBfRr6BrmJa9aV9t2ycqHL//a+0pu/4qbfHAMqtyu3j4nbtxC6mqvoJ9tLShNJGkQSSRQnLYmOS1cuc+nqRVKjeOcQmQLm7O7nCS3eDtMjkiZcKuucI4pVhG+aBte0+InH+Rmxm9PNO65euoxvNvCuRZOQNNqWy+Wc/XYbKzer3ERKdSMHehIgotIBwpU3Ao8+9ixfffIFXn7lAnt7MxbdDebzPQuqhDmLxcIGxjmIAYmBZx/9PJ+87xxn77ibFx/9t3QHLUJLSAtitOpSLQknkc3phBM7p9nZ3GHDbzBtp2xs2L1t3HaWX/utz/HCy6/AZBNJllITugOi7gFw7fJlvnTjWR57bJtpu8mZs7fx8Lvv4T3vvpezt28xmcDe3iVeeOoxvvLoc9z1wYfY2HbYnoiZTmrr/UUtIV69x2VTbQgcDFrW+MGXXIe1LHksvVu2Lxn+tmuUqLsgpFyyMF/l+FC5Xbl9TNy+tdDVBH3DSq7g6JU8T371KVt/7h1N4xGxxORhEzwQmeSUGulNgcEfwuATUyXFBE6YTCZ0GokxEEJgPp8Tt0uhkpQ3xVMjVkl01iEdxPxaOmTJ2Ie9lmDXFVSU/f3I5z7zLJ/+1Fe4ePGSpROliMo+s/kui3mHqmexOCClSNM0aLQgR+uUjbTPb33s1/mTf+b7eOjb/jgXn3mRi69eZP/GDVonbHhPM4FJu2VFnze22ZpM2NxqmGw0RNdy8o5389yLb/BLv/4JovNI3i7GOU/I6Te2rh2c6wg6I3UtL794iZdfeplPfvLz3HfveT78ofeyObnO9s5pTp9oYb4gbkyQcZX8fpAl/0/678xUyqRbec57k7Yfu9EqqdERfZrTis4w+NCOOZhWuV25fUzcvkVpR81SfGStuGHwNSnzLvLUU89geyZZonVMHRrpb1icObydA4kW0TVHdybPqH22L9UC51tc65lMpsRoNUUXiwUhBEKMOO+wnVfJ0U8zCTUt3/DY35bvaNS9gsqCl1+6zC/94qd5/vlLdOyxiHvEsMABKUQO9vcxN1qi6+Yggtey5YonJiVJRzi4xK/98m/z7/2FH+Phb7ufOy6+xPUrV9i9+Aaza1eZuMTWdIetjU22t6ac2N5isrnB5PZz3POeD/LCq1f57/6nf8X+LEAjNN7Sl2x/LQWxra5JEV1MiRJRf4BzHvEzDvYnfPWJfV584QLvee+9vPud57jngftYHCzgVJvHcjSmfXxB8kNe6qrKqJ+KSZW/WzJ1B11gyH0sQY7BH9l/Xy53jLJ2QOV25fbxcPvoPF21nDZNsSRumLmkguJIqrzx+hu8ceF1nG+yeUReSpmJoIpIhNaTV2QjOmPsV7FBz6kwmktOxMA8Lmi8ZzKdEKOymM2Yz2Y0vsnEbvDejdwqxRQrXTAiYDkqCUhCnBUQefrp1/m5n/sN3rhwjVl3g5gSi0VH01ge4sHePovFzK6jnhRz9f6sDtnsLCQ8LkUOrl7ml37xZ/mLf/EvcPLsu2inlzh3xz2QlO7aq7C/y4Z4Nja3OXnHec4/9G5uv/ddfO5LT/Df/uN/yuWr17L/UECcpeBk81cUkgiSBBEzb1MH3jcIEdUZznUczOZ84Yv7PPPCC/zJ7/sWukVnvaFmAvmE7cLKUGXLeGljIEtEK51bxr9sW7Jk3fYoAQtLO7Lj+zp4KKqOvlB3n5azflRuV273WDO3jxa6WOOSprxzJwz7LhkRvvrEY4TYsTHZzKs7ykwQs1mV95Yi0TbbljuYHfxFGxAnOFyfcuP8wOyU/44dLA5mzBZ7tO0E711vgolzucJPSX/JZFSgzELj9BFVNAmPPPI0P/fzv83u3ozZ/IDZbGEJ6yGA94QYWXQHpt2os3Nnd1AMEVT7XWM1JZJGkAVXLrzMT330o/x7P/qj3Hv3A0iaMd2ccGrnm3D717l+4SJ33Hk/J+++D93c5JGvPM5/94//KZcuXMl9aPfjVlJSYoKyP5YQESe2EomSXyp4L6S4QJhz4cIBv/pbX+S7vuMj3HP/WSa6IAgk8ZmUQ6TW2Vlsl4Q8vr1fSjE1MM/w2reIXuMrRZwLMc3YtvYWMpvkkd4XdpwLJiq3K7fpL79ebt9y54ikii+tyPpz8XnNZns89fQTtK3Lm+yZthBC7IlZyCAo6IKU5rZpXFkloqYxIOM172JBh7xkLxF5/fXXuXZtl9tu3+nNsCZ5q+aUC5csOcSzptH3bZ65bT97x5e+9Bw/89O/zsE8EnXBfLFL1wXayRQEUuyYz/YJoQP14D0imlN/XP7XfD7l/EkVJ7a54cXXXuZf/uRH+Ut/+S/zrd/6ATYnDmmmnLir5Z73ebZ3ThI6zyc//Un+2//6H3HxwgW8nw4PFIAmM1fxKIpzWeNJCe+FGKKt4unyenxxeFGc96Ruhka4fPEqn/ncE9z78D288zYLEnTO4aIO/T+m1MhsWtIGjsD4uJt9WdZ+ZGT6UUISty7x93aicrty+1Z4O7h9C/tO+n80N6CsvECVS5cusbt3hbZtaBqH98WXlSiVfsSB97YU0Euk8dEKuPc+L2O9alk7bq8QSqFnQVmwu3eVp596jtksEkIg5bXmq9uZ9D6ZMv+oLrVbSTz3wkv84i/8OnsHc0IIdN2cEGZ0XdebFgezPWb7N/LvPDGY762cs5BzXLjDunMC0dNKw96lC/zzn/gf+OVf+QQqt3P21G1sbZ6gmZ4gacunfu13+C//7j/g1ZcvEMT8gSlZcERcMb3GQZ7h3jQmy1ZURcpvkpJCIoREp4mw2EMX+7z+0sv8/C//Dq8cTFBtcTrs0bUkPA6ZnG327+dyxgeNj5dDfmwktwR3WwXlTJvpq0C9NeK/Pajcrtw+Hm7fQugGmy2y2aRJ0NT0A/HSS6/iXJNnbm+5iN7+bVyLdy1OGhQPIrjG47zvzbSUOmLqsBJzS4o9qkroAqrKRrvBZOJ56rmnuHTpKotuQUgBzS80otGKQffryhVsRc+QbqMauXEdPvrR3+TS9RsknZN0TtctCCES4pzQLehCx3y+oAsWMfFecF7M0e8E37a4yQR1pcq8dba4XFXKCYjgG4/ODvg3P/Nz/PN//lPM2eK2cztMJPELH/2X/MP/4h9w4/o1vDirRKULICCuRNWx5H0Jdl7f4MSNShFqT5ZCC03RygtGJYZE6OboYo/nH32cX/yVj3Np10rrCRHUW/1UbImqJEuf6okz0C6bnwrJ+tiBFdLWvNZdS9+vagcJ27k2ooS3rGG8/ajcrtyG4+D2WyhiPp5Jhw4JMfD6a2/QNht439K2U6ImXHLgG0jWCFutZ93WNBMrCCIzSxFR237ZAg3j1Izi84osFglNLa6F3b1rPPXMs5w/f47NEG2FTtI8iEoxX4Ri1uUQsPg88zh++zc/yXPPv4qf2h5LKS0IwfINQ1yAdmgM5tfKq156IuDwTcNkOrVtSIIFYcR7nAMvZbVRXuPtFImKpgN+49d/gb3ZjB//yz/KL330Z/nYx36WhSZSmx3vuNxNFg03eAa/l6BJyZw3bQHtfUdSzGMsh8jlIEhIwYi9WPDYZ77EyY0d/syf+jDbXEMVokBMtpTV1u3kYtOF6mrnHkwr6f1i5Nlesn90JFfGLKLsbKvJNJubj1k/Krcrt4+L27dMGSsELTOemUowmx1w7er17PhvQcTK0pGd77mSPOJwFA2AnkSliMSqw3kwZ+xb5zxdDAgR7zzPPPM8H/7QB22L5I0Nklv9TfFx6fgOEE1cubLHxz/+GRbdPq1rwTkW8zldWFiggEQMC0K3gBTxYrZi2UFAnNBMJjTtxMwKD0RnKUBlLX6uFCeqpLTIW68oEzfjs5/4Vb7yyd8hdXskEdvvKm8x4lyb6Wi+NOtDu255MMYBE+fs6LG/T5yzNeohm8mZpEkDRIePB3z605/jznvu5DvedwrPLGsTDSLtQLZ+HPKYJF16CFY5cvQM75bGp+hOeiiJ14nK7crt4+H2ke4Fc/wPqnhZLokoV69dZTZfgNrumzGmPDvkRuRocIyBEDtisO2pl3xTqXRg8ZvkNSWacgK3/Rs1kmLHYn7AxTcu8dLLr7JYBBaLQIwl+KFLPqihrxKqc1Q6nnj8CS6+cRHVjq5oACEQQ4cVh1ZinJPCAlLCO5eDB+bjatsJ7WSS13zbxoRJzckvzuObFue8DaHzNM0E5/K+ULHDpzkpXkPdZVsaqkqD0IrHqweanBNq12x8azubIvnzvLtAecjz+/6eU+pnZtuvyx42SYpER8cBcf8Cv/Prn+bqbILgaVRwmjcL7P1rOTWpmHhwOCXH5F0l51ix681ieZMzrR+V25Xbx8Xto326uZKP5lJnVrLOchkvXbqOaoc4JaY5qgu8s1IUHk/xnWhKpC4wn3fs780IXVnvbmq9Oc6DRSmzH4UUIZMxpQAxoiHn8s1nPP7Ekxwc7BO6A7oUSFqKUqd+MGyWEkQ9Eh1xLnz2M4+w6OZ9cKLrFqSwIHYdKQhOtkDbbD5hs3TxaznBtw3eOVLsiGEBYWHFqBOQHE49DUKDwyl4prRuStPYeRwxp+hs5ofYjrZd8cjlBpUUBSe2X5cUWqg9yPbezLEYAmXDPUttKRsjQopCLmpFShBTMp9YSLzx+iu8+Mq1vG9VIOFoSCQXKHX8xzwbwg1WK7b8ZQ9JnuXHQYRixuW8xUJFWeHoseYuVG5XbnM83D5a6I6idoVIiLB/MOepp5/ENVaoOAQlRc1BiaJqN4g0+W+bWX1j+YGl/J0UJ/lovbKiuQq8zWoxR3NttouktMuLLzzLGxcvsegCMYZ8Ph1aOfLXpeyIeuKJ53n6qZd6AoNVQ+oWsY/e+kaIyZYmOt/YaiPXgGvxzRRxTY4Id6QYUKwItmkLjc3O3tuSRBGidiSJ4DzOb9H4DdpmAycbeOeR/CouoyFdx5svUPKKp14DGIZZxOFdQyltp5m45aHLIXRbUZQA0ztIqUE08ruf+yIHoUXLRn86Xls+YhK9jtaX1zvMtwVjjUDy32k444jpx+5ZgMrtyu38z/q5fcslQf1Op6IkEjEJjz3xBJeuXkLFERPMQ2LRJboum00a8tUtkOB9Q9N4vJccABi6oK916cW2vh5p6IN5kZPRVUgpMtu/wVNPP83BIhC7xZLv57DXbN7xsY99ktkiEDX067xTSoRopqHllSsxLohRcXirptS2TDY2bBVPiLZGPsZMFod4i/qm/EAhOQjgnRV3ciDOoeJB/cjEovdpjbcQKaRSbN6NqAUzGl86JQsMQfNLxC+ZSoL1Jc5ZEY78k7Lk0kvkqaef5blXbpDwCGkkhMaUkV5Y9GMlLPVtIeTwXoqcsbFeOc7KExbz7nhFb+V25fZxcPstrsMsKzASFy9d5rHHHrdorbaEGHPFJavE34UFMQWSdigR7xqcTHHe2ee9iWRIKdnsP9qrqNxIGayUgt2QtqATNEYee/wJrt3YI8XQ+8iW/V5l1nI88shTPPPMy0QNKB0xBhaLBSlGYpyz6PYRZ/mKXRfyxOVoWo9MILJgNt9jPt8zTSAlYt60UMpKJTFymtVa6mpm81VAJWSaRZAOJPQkUIYAwuDTwsjtrJB28e0V35b00WQHSDYVc5/lmVucEFMiql036YKkC2I4YPf6Hp/74lMEZWk8ynXKuL9ZIGHVvzgcVsx2CzhpWRnUawb5dxymVRwHKrcrt1cY8TZz+xbuhUTTmG9jMvG0ojz/9FeJ8znaRUtEjh2tQExzZuEGnR4QNeXZSHCtp53aemvbKTX7bkrkEXBuMpgYeZDH0cyQIBJJzEjMEJlz4/JFXnnuRXP4p7JKKNpGgLk8n6Is5oGPf+IzzPSAwMI0mq6DENAUiTpHtcOLonGGo6FtJlZRSh0uRuLcNhaM0VbllFkxKojkgEDCTMW+tx2OBodHU67xKYM/LqXsq9PhnGUZp6hFUxscXm07EocVXZEc6FDVJS0CjNiaZ1rLfwSJltoDyVb9aCKEjqbb5elnXuT6LCLYseVB7iUEilXUGplYCohiW5iPjxdQbxM9iYQj0lqpQxao5FxG6UgaetP12FC5Xbl9TNw+MmUsBjg46GjbhhAE1YYH3nWOi1de5bVX93A5W8ZmKUXEg6Z8waYnW0rZb5IsB1JTNDMjwnRzgiCEvElgT86RRmAO/YB6n88XObkz5dSJHWKwQSkDNaSgKEkjn/7sZ3nu+RdtABOIKjEFQg46lC1biqN+Mplkv5fP+Y3BfpdNSUcyXxhtJojlPCrJZuR8HksbiohY3U1NNkP2wQO0J6PNjuVTcuUq61ffJ4snnM+cK177Qhbs+1JFyfZktbEQUdMGelNIiXnV1d71a7zy+nXedecEyPmX/QxfyMjoOvnT/Jn0b9OIzDbrN82EnROnEEmktLBIfYrZzAVNQjMZNgxcNyq3K7ePi9u3yNMdInYxmmly5sx5msmUeXcR19lqFhFoWptFu85K16WoNNlX03UdzjlCiKQYEMnrrqUFFRKhn/37mW4U6Gg8TFrHIiTUeVLn2N7cYWd7g8VsRghbuVJSNlEEUM8TX32WX/21jxPU/DFebF4NqSOEUoEfnGvQJOa8z+krMVid0ySp33DPHhSX3U5lSCKuECM/IBrMtxdjyDVYsUInyVsCvSour88vPjTrRI/GmH1L4Ed9AJgmJWbi2DOQTZxc6ahPgeqJKxQtQERIURES6hxOI9oFHnn8Be45++58b6UoiBFuSKkaHoKxSZYfCUrSuvnhjC8hRObzwJlz59jYmJgmQMSST+3+Tp06fTT93lZUbldulxOul9tHr0gjgkRKhR6l4WAPLrx+BZGE4IkhEFNgPgfxnsmkIUXbN6ppoG0nCIGuy7UzwXwyopzYOUEIkcXc6ng2jetnQFBiskHbmrRsbm5y8coNYoJps8E77nwH3ie6sM/BwQZN45nNZoQQOHVqhyuXbvALP/9b7O1ZJ3XBCFGWNtrDYp3ZthMr3KwJ7x0hdMRSWcgJTd5qRST7lyShLOwBGiVIG8lcT3jvJtYXLqejpLxWe9SOMuBWPNpZQY+Ulh5MwHIk84AikhPeC1lS/53LwY2sppl2lAtGFyTVXGKw46mnX+TiN93NXeelz0ddDSbcTAylBB0YFfPOdEZRusWcRXeD2QLOnL2Nk6e2EWko22FbFH9yFP3eVlRuV24fToy3n9tHa7pCXuottiJROq5efZ353i4eoS/wUKKmSZgHIfgZk3YLmglKIi5mdIsZKSRQz+bmSd7/oQ+ys73FZz71SUBJwTxbzvnsQHeIS7iU2Jp6zp25jc3J7bjG8cBD9/LgOx9AnAUuVMWCDiJIFEKX+JVf+ndcuHCdQCKFiKRI0kDCiiWH0KFk/1LTomp+rRhnhFhSYXId1TyzNk2bnfkdXiw4of0sXMrDJZxriFEhBUQUUsI2MbSZ0HsbHNsYMZeOU0hdsOBMLoRdHPXGhey0R/tSgeVVSgjaEkZbLSXO2XrznBgeYmdtQfBFE2PB7PpVvvDo67z/fe/FaYKU+mLOouWKnT0Q2oAKKnEwARkHdooGErhx+RKPPvcG7//gh4lpn/3ZNufO3kk7aUDNT3i8Pt3K7crt4+H2LUs7WqeQncPmCzM/UyRFyVHbEsXTPLMlQneDxWLB5uYWMZUosOD9lA9/6Fv54Ld9iI//zr8laEK8w+NQnZsjvJmaaUSDyIKNzQknT29z1713sLm5xeaJbTZyqot3zsxDEuIivm34whef4ktfeQznG9Ii5Bku0XVdPyuav2nw16QcqIghgIglZeecQu99n2MINruaL08QsZScktrjvRvNvJbcnRQEv9Q/40CB+eryrCrZZ9bnpgz+PxHBi/Rz7/jzUj4wlQIuauvqHZGUOroUbRWQyylI4q0MYVzw9BMvcXlfOHN6YYndOvjleg2kOLzKEtSRAmCm7/AAJSKxm/Hqy89zbX/Ghz/wAe4M0O2/yO133sXJnZN4rzgJHDcqtyu3183to4uYp8RsdsDWpqcLCQ9cv75LjDa7dWFoKP0Qlz8iMSzY3U00rbNABMrGxpQLr7/CT/4vjzI/2MdnnxReSOqJMRBDsFUrTUNDx872CSbtBpPWM51O2NraYmNjg8lkQjtpsclVEW3YvRH5jd/6HIuQaJqQk84TMaa8iV7sSRFTwkkLQAiLHHlsbL25t+Rs720HV+/NZxVTQDXPjnhS6nqfXdnWJcYFqhHRROz9PEa2cmxfGLn81lnx5fKwpGT5hb6s6BmZREULUBhMtUxEFdsW3FJb7HxW7QqSYpFsymyfkJTYvX6ZLzz+Eg/d9RCJqySxVUfDuPpcKX9IiSlkXfaCWUoPktDU4dOCg92LfOYzn+Rd73yIh9/9LrqXX2R26gxnzp1F3Vua898WVG5Xbtu4rp/bR7LeidUKtcrxiTiH5559nsY33PPgXVy4dJlrV67ZkrzCyt5fk1CxknSarOAaCCEseOW1FxEarDxdvsMcnfS+IUbzUzXS4Sct2ydPsLV9gunGJpub22xtbjGZTPCNbe/sXJMd7C2/+7uPcOHi1RyhtS2hywxc/pW8Z5Kq4nyOpmIBANe0FlxwDU1rBFWsqLKmRCKMZsTsmM8RaxRiLOSzgIBFaku9UpthYzRSFgtE7WgzeVNCpHxRfGmSP0uE2OUiHdZfUcj+uAbU4Z0FcELKQQus5quW+gHkGR5baeUUVPf55Ke+xP/qu9+T/WnjXMPCQMk+LlvqOhxTtCKyJpKbnuYQFxBNE/vqVx/jyvXLfPh976dbBG7s73IwOz5Nt3K7cvu4uH1Lp1rjba154zx713fZvXGN7/v+7+Z7vv87+fbv/ABNays7rGMHfUDI/iuxNdCgOGf+F6vA3xFTpPyXtKM44Z3XvK2JRYvbjQkb25ts7WyzvbPD5tYWk40NJtMpjTQYLxJvXLrCZ3/3izROIXXZtzWYR9awRNfNKOu6G99YEopraJpNq4jkbJlkirCIgcViTrdYELtgxMPb8sOsDIlKrrRndVRTiiRNpGhL7cH8UlaN38hhxUNCTwQRZw9rXvpYEsqHpHLFgj8Bq91ZkrM1J9AviLogxAVKxDmTFlbv1TQDK0MnIG12UXmQllY8l155iUeffC7zMFBqhCbNkVn69O/+ocz07s0w+veOmGbMbtzgtZev0HWCk8DFVy7y8U98lpdffZG93atcu3LlVvR7W1G5Xbl9HNy+pX1X8hG9g25+hXvvO8P9D95DTAEJHkkOkZSLaIyIKSWXsUQ4x5XoE+K0n0HK1iDle8Eh0uFkws7OhK2tU7TtJk1O7G58k5dfNkxcg0xsa5NPfupLXL66x2RjsmyuaHH2x8EE0q5fdRNjAEkktURtFCsUkuaWF5gszWfStog0oyr7Fll1OWAgYP2SuyEmwTHUBtWyp5IqmuuJDvmeKUeZh/Xz5kcr/WZEc9Ig3taca08IiKkj9LvU0vet9bPrC0T7nA9aRkpVCRqJs10+/8iT3P0D76dhztig1uL80l7p6fs2qzDZJB0MsrLtzGuvXOHGfMG73nOeSVRme/t8/nNXePBdDzOfz25Fv7cVlduV28fB7SOF7tiPJc5x5vw7OHfXOxBJuCSEWYBUopGDU9z+TX2nC0OOYLkhccFMBt/0AzSsDvEgM5yc4M53nGFzuo13Ld55Su1PlwuHiHM03nP1+owvPvIE87jABUvotnJ7Qz6mOeQdzpl55tyUlBJdmFtUlIBzC0D72dZmaAFf1pgPhPfOkrRTIUe0DfCK4730gVCYIrkuaPGdFZPIzlW0ln65JCNfk1i+p/cTUia/7ddVggKlTqldylJ7zFQqfTbM5EaiEvSIGpn4yFeffZXFH/sADdrfU2m65gDEasrNoBWUz+zB7rouj8+cK7s3uP+d383s1ae4cnVO6LZ44vGv8tLLLx9Fv7cVlduV26Xp6+b2LfJ0S2MiMMH7qc38eSbaX3Q58aN3ehkRRXLnwFA+T9C+bmYhjPYpJSVFR/szeryHjem0zyMc5wnagNpM5Lzjicef5fqNXSKBeTe3gVZl0YUckc2mWE5lEQRJSghzqzlK3v4kl49TsKIgLmsfORm+DC6CkTnPyCU9ZhjOPPgy5BM6aSwnNN+v/S73k5AfnHJvZt6IlMR4tWCBA00CeSPBXrvKfW7LRe14wSMqWQPwhC5Amtu2M43rfX4oaIhcfuMCz7x0ifffswN0oC0Qcn9kv6HatWU07n1ajdoICkJSh20m6PFui9On7uIdd57hyScf5cWXd4mRPrf1OFC5Xbl9XNy+RT1dS3xG1FIwsv8qaSRKZBYWJDHCCX3vZgJ5rChGmZ0U1UDKOXWSz5PSgoT5wsrMLZJwTNjccEwaKyiCl1yLOM8+SfoOC2HBY489SQoxl6dbEGI3rDrRHPBIUIpIa7K9p2I3I8UZKSxIYWbtQ2iaaX5t0E6mtO3U/HiZiKs7tBYTrHEu101NaDISxWDtjdEKp6ja5woDqaDPQTRS0p9Xc5oMOTVHxNE2E9q2zZv8gYjH+zZHo11ei162CseCLjk6baEfyd/b35DQ+Q3+3ccfYW/RkKRwLZfF09ALDTQaNzIhMyeHB0phEZSY7KlrG6FpPO3Gad7//m/mPQ/fy4a/QZP2j6Tf24rK7crtY+L2kZruYnZA7GJW060+Z4opu5gTBzd2cfm29E3O0S+BJFjnK2hUnCszH6CWFm0+lLI9dcPG5oSm9f10qdk0KANaOmFvd86li1cQl9NBUiKFTExLJMznyDVI0ax9KBo7RBONa+1hEofzG1YpvxFav2GriETyjKz9z8fmUjEhzeyDkmO4tDuBWmCg5B0OkVszp5y3ptoKHe2vaUrZ2OQpf0qOSrtcK9WhaquPYoxEQm5bRKXrfZG2NNTZzO2KJmGJ6S89+yJf+eprfPMHz9EQGO/WOs6vLKX3il4w+POM0c6eDBChcZ5J06IiiNvm/gfvZWtDuPjaa0fR721F5Xbl9nFx+0ihe7B3wMsvvEHbTtg5sUlMM2IMiNiqkRtXr9F6R9f7SYYKSmNS2j+lAIQaEbMp5JwnkXfjtGFBtcE7j2+ErpsT04IkExIbJLViHElLsrSwtzujW+RAglp3xZDXTKeEis3iQjFTEqil/SQRnJ/StJuItKaJ+I28akdo/MRSi1QJGnAasTU6kmdi6/w0Skwvj2nZwqUQtJCwrLJZepzFBj/EvHIn+wjVZY1DxsSw7irOfnE+73SKEdVPaXzMGlGJrjfYbrcNtijJdkzV6HG0iAtonBGS49NfeIL3Pnw7JxvN6VBFiCSLSKvlpRphy12UB8bM7d68dnmL8nxcIkLa4ez5d3P2Hc8fRb+3FZXbldvHxe0jhe7F117m4PLrCLZFMi6BU5ybEGm5fG2XLiTzaY+IaC009bv4VcyUKyQ1omoyzcKLI+VIpM1ytvLj4sVrLPZm3Htv4M677iRtdKTpAd1ey+bmSWYb23TbG8zmCzpdIG6CBJeLOUecE6JGYjSyQt4WJbvacJ7Wb+D8hEm7hRNBnSWRp2TFQiyQkrUYbG19dq1D9u2UyboUjO41ILWZP+V8P+cUJ44SGHC4smtM1ohs6WeIEe/aXPFpeLhFrJapgM3iKrkfI5ITxhEzfZw4msZnf58RVpngaKx/UwDKUlC1bVTMqcbrr77BK5dmbJ1vcXl1kpFQ0GTLWE0jKz7CYnoVeyyY5pgNcxorA0jK5ioOTZ7I9Cj6va2o3K7cPi5uHyl0J07Y2bB1z4kIzvaNktSxt2gI0aKmukpKipJO37FWjs3+LBaRHWN7TSG+j9ra/XWAozuYc+PGLhu7uwQNJOnQ5gSuDUhMTJJj0yk+BURbvJ8ym0UUZ9pC3qK6DF7SlHdC8kwn22xuncQqQtk1LWpLT7BgjBveJwFxmUhkX5LN+N4P68YLbNNYS2vRlO9ZLH1HnJkrqS/95/LMDyEGXMIqTBUNqph8uVKUc20mbV5vnrU0I5GzFBwRvLM8STOdzeRUVbvXfDuSzT0h0R3s8fTzr/PAHfcx6QuOmOZFktGq+RIUodcMUlKcRrou5LQeQRpvS0bjIisKkZS8aQ7HhMrtyu3j4vaRQtd7aHzAi5JECClBanpHPyMrokRex2ZX2djNjvGFsRQHu3OCy9teiGqZY0AsUqipI2rijRef5tLLz9B4oXHQuG3adpPJxibOTWi2HKELLILgW3O4zxdzM8XKIAi2MV7vu0p5eWRkOtnqNQSnRh7vsx8rE6KkmZiJUVJShBBDf7/lIXTO9SaXLePMDwmWt+l8Lqcn9KabPUSa641amoxaVrodq0IIllpjl2ozyfs5l1K8RJP5umwNPaSUS/mliNMGUdvTq6Tj2PJR0yScOBoXefml1wjf8k5anY/8Xsu+t348yX2UAyxJo+WHluEU15uY9jDn4Iy8OTHfblRuV24fF7ePFLpbm55TpxyNT2YcxIZLV5Xrc2WeQ4BJi+mkow7SgbEU4hYmg+UWtjZQHjyKpIjtfyeEqJDAu0TrlE1ndUfbRi1a6HdpJ3s0bYtvT3BdzxG9Yz47oNUZTbPBVDxdN0fzyh0znwKiio9K0hmz3T1it8uJU+dp/Y61z5mz3syiRCvOtm1JKS+7DNksS8RkKUe9BpODniXQIK5US8o+t7zPVAgzmrwEk9HxfV9hOY9mKmn2M+rScVZKrtRvdabpxNFmedKBOpTA3sEVurCfi5pMQZ0FPXKVeyNVsPqxLuFJXL++zzwI09Gy0PKAFl72Jlj266kmi+1ooguBmLdsFYqWV35vhbWXCL5mVG5Xbh8Xt48Uuq1zTCeeJJ4rl+Dy1cgCnwdEaVw2K0pjRwVCzNeSV6yUrYxzMraOlgd6nG0J4vLWHqpIUmKn+JCQVtDG9miK6vDqiRpJ0tI1Z0jpHDcWDSEcIHkWit0ezk1p/SYkZ8sH815Xpol0FgXWjtnsBs5N2dluQRoa7xBRNNqxIRcVsVcgbwtFzM4wM4tsz6SUPJYsb6tlnDaoRpxTVH2eAbO5g9o5bIhzTqet3DFzLWb/mMMsNY9K6kvPiVoAoE+kF/OD2dp1qz4lLJgfXGe+fyMLgalpIs7bXleqlpiOx8kkm2ymmURtcTo3raT3e7lsRpfZvwihnPqTd8y1hzgS4gIQGt+aXy4Xqi7b2hyjolu5Xbl9bNw+uuBN42g3lOtXNphOJ0y2DzjYn1nqiTQ0HiQlFt0i+6rGqrnYXlIjL0meFyi2m2ApJC5HL82Mi3iJ7EwSU69stpYG4pwwaWFzQ2gkEdwmM3eOIOdxbWBjcwPxB8zmN1gs9nPidGPpMc6z6IqPynIZi0klqqTQ0XUznDf/l3OubPZpa9z72U96H5XkKEEqAyJW27Ovvq9WYJocUNBUTE4zR0QaHORk72RmkXNIyr6k5ABP0og6C5xoNLPKu02gM0GQKzwhHu98Nr+st7vFAQcH14lphqMlxQ5kjnMbtN7SiJz3iKRc7d+S+8FxsLvHjYMFG1vkNpXVSLK0omdYxTP4vjRZ4r5V6redZ63QdSKNtpw5RplbuV25fWzcPlLovnHdoWxw9nzHjStzDmb7uXw7FHPKi6dJDV0KK0YX2R4Z/CSSvfgWbMjTap4JVSCmxATHtG1ofWI6yWRshGkDjU+0baSZbHIj3s6LFwLJXSExtwIeizldtw9YEeekEY0geBpvdUyDjh+i4ueBFOakNEd9RFweZLXEaSTn5kVLqE9x9LClYa29eHPW9wWwxVJQrKReeUhz8eUc//SSH1hnD4liq6LKfli980ij+e6yFgNCI202gQWRaH67nEKYklVtcl5wAdN+4sIezLig61q8m9A0U9rJFOcnWIlCKwgyv36Fp166yu3vOUuzJFxu9n8VQpb3KSrdImRTzxYEJO1waI7qy6qFvnZUblduHxe35Ujfg9/WjemCrUlDTI5uEQguO8aT2n5EJu/pYiSkZW2AccABmwEF88U4MS2gcTlFxQmNmjfMWxAzr/awl3eCE/BOaJuGy/tTXrvekHQv94rPs2KXo4gtJa9QdJJTVAIp2dJIqzAUcDjadhthgormHMOhwn/ZTrp0k0AfOCjpJYVwNpt6M+Hy6iOhQZjmNBZbSimZ6DZzD8EZsjPeBjNfQ6wtIpYOo5q30S7mF2QfmfTMsDzPSIoLoCN08xxFz8EIBMSZ2eVaq3zlSiHrkhp0ghPnz/Phd93OVLol8pUkfvp/84OuxUKLPPPUU7xxaY8uCZPTp/nIB95JE/uUdpx4nnn2WZ56+sljUXgrtyu3j4vbRwrdioqKiopvLI5xk6qKioqKP3qoQreioqJijahCt6KiomKNqEK3oqKiYo2oQreioqJijahCt6KiomKN+P8Ddhxt9IPGBbQAAAAASUVORK5CYII=\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "learn.show_results(ds_idx=1, max_n=3, figsize=(6,8))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "It's quite amazing that with just a few minutes of computation we've created such an accurate key points model, and without any special domain-specific application. This is the power of building on flexible APIs, and using transfer learning! It's particularly striking that we've been able to use transfer learning so effectively even between totally different tasks; our pretrained model was trained to do image classification, and we fine-tuned for image regression." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Conclusion" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In problems that are at first glance completely different (single-label classification, multi-label classification, and regression), we end up using the same model with just different numbers of outputs. The loss function is the one thing that changes, which is why it's important to double-check that you are using the right loss function for your problem.\n", "\n", "fastai will automatically try to pick the right one from the data you built, but if you are using pure PyTorch to build your `DataLoader`s, make sure you think hard when you have to decide on your about your choice of loss function, and remember that you most probably want:\n", "\n", "- `nn.CrossEntropyLoss` for single-label classification\n", "- `nn.BCEWithLogitsLoss` for multi-label classification\n", "- `nn.MSELoss` for regression" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Questionnaire" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "1. How could multi-label classification improve the usability of the bear classifier?\n", "1. How do we encode the dependent variable in a multi-label classification problem?\n", "1. How do you access the rows and columns of a DataFrame as if it was a matrix?\n", "1. How do you get a column by name from a DataFrame?\n", "1. What is the difference between a `Dataset` and `DataLoader`?\n", "1. What does a `Datasets` object normally contain?\n", "1. What does a `DataLoaders` object normally contain?\n", "1. What does `lambda` do in Python?\n", "1. What are the methods to customize how the independent and dependent variables are created with the data block API?\n", "1. Why is softmax not an appropriate output activation function when using a one hot encoded target?\n", "1. Why is `nll_loss` not an appropriate loss function when using a one-hot-encoded target?\n", "1. What is the difference between `nn.BCELoss` and `nn.BCEWithLogitsLoss`?\n", "1. Why can't we use regular accuracy in a multi-label problem?\n", "1. When is it okay to tune a hyperparameter on the validation set?\n", "1. How is `y_range` implemented in fastai? (See if you can implement it yourself and test it without peeking!)\n", "1. What is a regression problem? What loss function should you use for such a problem?\n", "1. What do you need to do to make sure the fastai library applies the same data augmentation to your inputs images and your target point coordinates?" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Further Research" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "1. Read a tutorial about Pandas DataFrames and experiment with a few methods that look interesting to you. See the book's website for recommended tutorials.\n", "1. Retrain the bear classifier using multi-label classification. See if you can make it work effectively with images that don't contain any bears, including showing that information in the web application. Try an image with two different kinds of bears. Check whether the accuracy on the single-label dataset is impacted using multi-label classification." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "jupytext": { "split_at_heading": true }, "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": "3.7.7" }, "toc": { "base_numbering": 1, "nav_menu": {}, "number_sections": false, "sideBar": true, "skip_h1_title": true, "title_cell": "Table of Contents", "title_sidebar": "Contents", "toc_cell": false, "toc_position": {}, "toc_section_display": true, "toc_window_display": false } }, "nbformat": 4, "nbformat_minor": 2 }