fastbook/06_multicat.ipynb
2020-02-28 11:44:06 -08:00

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{
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#hide\n",
"from utils 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 we learnt some important practical techniques for training models in practice. Issues 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 other types of computer vision problems, multi-label classification and regression. 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 an image, where you may not have exactly one type of object in the image. 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 before is 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. And then, tested by passing in an image which is not of any of your recognised 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."
]
},
{
"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": null,
"metadata": {},
"outputs": [],
"source": [
"from fastai2.vision.all import *\n",
"path = untar_data(URLs.PASCAL_2007)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This dataset is different to the ones we have seen before, and that it is not structured by file name or folder, but instead comes with a CSV (comma separated values) file telling us what labels to use for each image. We can have a look at the CSV file by reading it into a Pandas DataFrame:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>fname</th>\n",
" <th>labels</th>\n",
" <th>is_valid</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>000005.jpg</td>\n",
" <td>chair</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>000007.jpg</td>\n",
" <td>car</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>000009.jpg</td>\n",
" <td>horse person</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>000012.jpg</td>\n",
" <td>car</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>000016.jpg</td>\n",
" <td>bicycle</td>\n",
" <td>True</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"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": null,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df = pd.read_csv(path/'train.csv')\n",
"df.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We can see that 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, its not actually a panda! *Pandas* is a Python library that is used to manipulate and analysis tabular and timeseries 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 you see above.\n",
"\n",
"You can access rows and columns of a DataFrame with the `iloc` property, which lets you access rows and columns as if it is a matrix:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"fname 000005.jpg\n",
"labels chair\n",
"is_valid True\n",
"Name: 0, dtype: object"
]
},
"execution_count": null,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.iloc[:,0]\n",
"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": null,
"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": null,
"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": "markdown",
"metadata": {},
"source": [
"TK"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Pandas is a fast and flexible library, and is an important part of every data scientists Python toolbox. Unfortunately, its API can be rather confusing and surprising, so it takes a while to get familiar with it. If you havent used Pandas before, wed suggest going through a tutorial; we are particularly fond of the book “*Python for Data Analysis*” by Wes McKinney, the creator of Pandas. 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 McKinneys book."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### End sidebar"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Constructing a data block"
]
},
{
"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 which returns a tuple of your independent and dependent variable for a single item\n",
"- `DataLoader`: an iterator which provides a stream of mini batches, where each mini batch is a couple of a batch of independent variables and a batch of dependent variables\n",
"\n",
"On top of these, fastai provides two classes for bringing your training and validation sets together:\n",
"\n",
"- `Datasets`: an object which contains a training `Dataset` and a validation `Dataset`\n",
"- `DataLoaders`: an object which 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), its often easiest to start by creating and testing `Datasets`, and then look at `DataLoaders` after thats working.\n",
"\n",
"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. Its easy to debug, because you know that if there are any problems, it is in the line of code you just typed!\n",
"\n",
"Lets start with the simplest case, which is a data block created with no parameters:"
]
},
{
"cell_type": "code",
"execution_count": null,
"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": null,
"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": null,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(fname 008663.jpg\n",
" labels car person\n",
" is_valid False\n",
" Name: 4346, dtype: object, fname 008663.jpg\n",
" labels car person\n",
" is_valid False\n",
" Name: 4346, dtype: object)"
]
},
"execution_count": null,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"dsets.train[0]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"As you can see, this simply returns a row of the dataframe, twice. This is because by default, the datablock 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": null,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"('005620.jpg', 'aeroplane')"
]
},
"execution_count": null,
"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 Pythons *lambda* keyword. This is just a shortcut for defining and then referring to a function. The above is identical to the following more verbose approach:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"('002549.jpg', 'tvmonitor')"
]
},
"execution_count": null,
"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, however 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 (they 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 second will need to be split on the space character (which is the default for Pythons split function) so that it becomes a list:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#hide\n",
"Path.BASE_PATH = path"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(Path('train/002844.jpg'), ['train'])"
]
},
"execution_count": null,
"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 which 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 lets test it out:"
]
},
{
"cell_type": "code",
"execution_count": null,
"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": null,
"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 cant 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": [
"Lets 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": null,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(#1) ['dog']"
]
},
"execution_count": null,
"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 fastais L class, you 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) list of integers."
]
},
{
"cell_type": "code",
"execution_count": null,
"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": null,
"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. 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": null,
"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": null,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 648x216 with 3 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"dls.show_batch(rows=1, cols=3)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"And 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": [
"### Binary cross entropy"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now we'll create our `Learner`. We saw in <<chapter_mnist_basics>> that a `Learner` object contains four main things: the model, a `DataLoaders` object, an `Optimizer`, and the loss function to use. We already how our `DataLoaders`, and 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": null,
"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 you 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": null,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"torch.Size([64, 20])"
]
},
"execution_count": null,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"x,y = dls.train.one_batch()\n",
"activs = learn.model(x)\n",
"activs.shape"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Have a 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. Heres what one of those activations looks like:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"tensor([-1.0028, 0.3400, -0.5906, 0.7806, 3.1160, -0.1994, 1.3180, 1.6361, -1.7553, 0.2217, 2.8052, 1.3229, 0.9369, -1.4760, -0.3204, -2.3116, -3.8615, -1.5931, 0.0745, -3.6006],\n",
" device='cuda:5', grad_fn=<SelectBackward>)"
]
},
"execution_count": null,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"activs[0]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"> note: 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 arent yet scaled between zero and one. We learned in <<chapter_mnist_basics>> how to scale activations to be between zero and one: the `sigmoid` function. We also saw how to calculate a loss based on this--this is our loss function from <<chapter_mnist_basics>>, with the addition of `log` as discussed in the last chapter:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"def binary_cross_entropy(inputs, targets):\n",
" inputs = inputs.sigmoid()\n",
" return torch.where(targets==1, 1-inputs, 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 one, 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 one is not a good idea. By the same reasoning, we may want the sum to be *less* than one, 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 colums."
]
},
{
"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 it's 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 our example above.\n",
"\n",
"The equivalent for single-label datasets (like MNIST or Pets), 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": null,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"tensor(1.0082, device='cuda:5', grad_fn=<BinaryCrossEntropyWithLogitsBackward>)"
]
},
"execution_count": null,
"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` have 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: since we are in 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": null,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"('Hello Jeremy.', 'Ahoy! Jeremy.')"
]
},
"execution_count": null,
"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": null,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"('Bonjour Jeremy.', 'Bonjour Sylvain.')"
]
},
"execution_count": null,
"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": null,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: left;\">\n",
" <th>epoch</th>\n",
" <th>train_loss</th>\n",
" <th>valid_loss</th>\n",
" <th>accuracy_multi</th>\n",
" <th>time</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <td>0</td>\n",
" <td>0.903610</td>\n",
" <td>0.659728</td>\n",
" <td>0.263068</td>\n",
" <td>00:07</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1</td>\n",
" <td>0.724266</td>\n",
" <td>0.346332</td>\n",
" <td>0.525458</td>\n",
" <td>00:07</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2</td>\n",
" <td>0.415597</td>\n",
" <td>0.125662</td>\n",
" <td>0.937590</td>\n",
" <td>00:07</td>\n",
" </tr>\n",
" <tr>\n",
" <td>3</td>\n",
" <td>0.254987</td>\n",
" <td>0.116880</td>\n",
" <td>0.945418</td>\n",
" <td>00:07</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: left;\">\n",
" <th>epoch</th>\n",
" <th>train_loss</th>\n",
" <th>valid_loss</th>\n",
" <th>accuracy_multi</th>\n",
" <th>time</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <td>0</td>\n",
" <td>0.123872</td>\n",
" <td>0.132634</td>\n",
" <td>0.940179</td>\n",
" <td>00:08</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1</td>\n",
" <td>0.112387</td>\n",
" <td>0.113758</td>\n",
" <td>0.949343</td>\n",
" <td>00:08</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2</td>\n",
" <td>0.092151</td>\n",
" <td>0.104368</td>\n",
" <td>0.951195</td>\n",
" <td>00:08</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"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 labelled 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": null,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"(#2) [0.10436797887086868,0.93057781457901]"
]
},
"execution_count": null,
"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 often be selecting correctly labelled objects:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"(#2) [0.10436797887086868,0.9416930675506592]"
]
},
"execution_count": null,
"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": null,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"preds,targs = learn.get_preds()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"...and 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": null,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"TensorMultiCategory(0.9554)"
]
},
"execution_count": null,
"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": null,
"metadata": {},
"outputs": [
{
"data": {
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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"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. But 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)."
]
},
{
"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 blocks 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 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://data.vision.ee.ethz.ch/cvl/gfanelli/head_pose/head_forest.html#db) for this part. First thing first, let's begin by downloading the dataset as usual."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"path = untar_data(URLs.BIWI_HEAD_POSE)"
]
},
{
"cell_type": "code",
"execution_count": null,
"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": null,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(#50) [Path('13.obj'),Path('07.obj'),Path('06.obj'),Path('13'),Path('10'),Path('02'),Path('11'),Path('01'),Path('20.obj'),Path('17')...]"
]
},
"execution_count": null,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"path.ls()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"There are 24 directories numbered from 01 to 24 (they correspond to the different persons photographed) and a corresponding .obj file (we won't need them here). We'll take a look inside one of these directories:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(#1000) [Path('01/frame_00281_pose.txt'),Path('01/frame_00078_pose.txt'),Path('01/frame_00349_rgb.jpg'),Path('01/frame_00304_pose.txt'),Path('01/frame_00207_pose.txt'),Path('01/frame_00116_rgb.jpg'),Path('01/frame_00084_rgb.jpg'),Path('01/frame_00070_rgb.jpg'),Path('01/frame_00125_pose.txt'),Path('01/frame_00324_rgb.jpg')...]"
]
},
"execution_count": null,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"(path/'01').ls()"
]
},
{
"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 convert an image filename to its associated pose file."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Path('13/frame_00349_pose.txt')"
]
},
"execution_count": null,
"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": [
"We can have a look at our first image:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(480, 640)"
]
},
"execution_count": null,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"im = PILImage.create(img_files[0])\n",
"im.shape"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<PIL.Image.Image image mode=RGB size=160x120 at 0x7FA45C869B10>"
]
},
"execution_count": null,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"im.to_thumb(160)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The Biwi dataset web site explains 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": null,
"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": null,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"tensor([384.6370, 259.4787])"
]
},
"execution_count": null,
"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 person appears in multiple images in this dataset — but we want to ensure that our model can generalise 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 which returns true for just one person, resulting in a validation set containing just that person's images.\n",
"\n",
"The only other difference to 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": null,
"metadata": {},
"outputs": [],
"source": [
"biwi = DataBlock(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)), Normalize.from_stats(*imagenet_stats)])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"> important: 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 kinda of problems."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Before doing any modeling, we should look at our data to confirm it seems OK."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 576x432 with 9 Axes>"
]
},
"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": null,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(torch.Size([64, 3, 240, 320]), torch.Size([64, 1, 2]))"
]
},
"execution_count": null,
"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.\n",
"\n",
"Here's an example of one row from the dependent variable:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"tensor([[0.0111, 0.1810]], device='cuda:5')"
]
},
"execution_count": null,
"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 kind of data the independent and dependent variables represent."
]
},
{
"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 <<chapter_intro>> 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": null,
"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": null,
"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 `(low,high)`.\n",
"\n",
"Here's what it looks like:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"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": null,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"FlattenedLoss of MSELoss()"
]
},
"execution_count": null,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"dls.loss_func"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This makes sense, since when coordinates are used a 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": null,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"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": null,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: left;\">\n",
" <th>epoch</th>\n",
" <th>train_loss</th>\n",
" <th>valid_loss</th>\n",
" <th>time</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <td>0</td>\n",
" <td>0.045840</td>\n",
" <td>0.012957</td>\n",
" <td>00:36</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1</td>\n",
" <td>0.006369</td>\n",
" <td>0.001853</td>\n",
" <td>00:36</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2</td>\n",
" <td>0.003000</td>\n",
" <td>0.000496</td>\n",
" <td>00:37</td>\n",
" </tr>\n",
" <tr>\n",
" <td>3</td>\n",
" <td>0.001963</td>\n",
" <td>0.000360</td>\n",
" <td>00:37</td>\n",
" </tr>\n",
" <tr>\n",
" <td>4</td>\n",
" <td>0.001584</td>\n",
" <td>0.000116</td>\n",
" <td>00:36</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"lr = 2e-2\n",
"learn.fit_one_cycle(5, 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": null,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0.01"
]
},
"execution_count": null,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"math.sqrt(0.0001)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This sounds very accurate! But most importantly, we should have a *look* at our results with `Learner.show_results`. The left side is actual (*ground truth*) and the right side are our model's predictions. "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x576 with 6 Axes>"
]
},
"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",
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"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 different directions of those trainings is determined by the loss function, which is the one thing that changes. That's why it simportant to double-check your are using the right loss function for your problem.\n",
"\n",
"In fastai, the library 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 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 customise 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 an hyper-parameter on the validation set?\n",
"1. How is `y_range` implemented in fastai? (See if you can implement it yourself and test it without peaking!)\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. Have a look at the book 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": []
}
],
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