Model Layers¶
This module contains many layer classes that we might be interested in using in our models. These layers complement the default Pytorch layers which we can also use as predefined layers.
Custom fastai modules¶
The output will be 2*sz, or just 2 if sz is None.
The AdaptiveConcatPool2d object uses adaptive average pooling and adaptive max pooling and concatenates them both. We use this because it provides the model with the information of both methods and improves performance. This technique is called adaptive because it allows us to decide on what output dimensions we want, instead of choosing the input's dimensions to fit a desired output size.
Let's try training with Adaptive Average Pooling first, then with Adaptive Max Pooling and finally with the concatenation of them both to see how they fare in performance.
We will first define a simple_cnn using Adaptive Max Pooling by changing the source code a bit.
path = untar_data(URLs.MNIST_SAMPLE)
data = ImageDataBunch.from_folder(path)
def simple_cnn_max(actns:Collection[int], kernel_szs:Collection[int]=None,
strides:Collection[int]=None) -> nn.Sequential:
"CNN with `conv2d_relu` layers defined by `actns`, `kernel_szs` and `strides`"
nl = len(actns)-1
kernel_szs = ifnone(kernel_szs, [3]*nl)
strides = ifnone(strides , [2]*nl)
layers = [conv_layer(actns[i], actns[i+1], kernel_szs[i], stride=strides[i])
for i in range(len(strides))]
layers.append(nn.Sequential(nn.AdaptiveMaxPool2d(1), Flatten()))
return nn.Sequential(*layers)
model = simple_cnn_max((3,16,16,2))
learner = Learner(data, model, metrics=[accuracy])
learner.fit(1)
Now let's try with Adaptive Average Pooling now.
def simple_cnn_avg(actns:Collection[int], kernel_szs:Collection[int]=None,
strides:Collection[int]=None) -> nn.Sequential:
"CNN with `conv2d_relu` layers defined by `actns`, `kernel_szs` and `strides`"
nl = len(actns)-1
kernel_szs = ifnone(kernel_szs, [3]*nl)
strides = ifnone(strides , [2]*nl)
layers = [conv_layer(actns[i], actns[i+1], kernel_szs[i], stride=strides[i])
for i in range(len(strides))]
layers.append(nn.Sequential(nn.AdaptiveAvgPool2d(1), Flatten()))
return nn.Sequential(*layers)
model = simple_cnn_avg((3,16,16,2))
learner = Learner(data, model, metrics=[accuracy])
learner.fit(1)
Finally we will try with the concatenation of them both AdaptiveConcatPool2d. We will see that, in fact, it increases our accuracy and decreases our loss considerably!
def simple_cnn(actns:Collection[int], kernel_szs:Collection[int]=None,
strides:Collection[int]=None) -> nn.Sequential:
"CNN with `conv2d_relu` layers defined by `actns`, `kernel_szs` and `strides`"
nl = len(actns)-1
kernel_szs = ifnone(kernel_szs, [3]*nl)
strides = ifnone(strides , [2]*nl)
layers = [conv_layer(actns[i], actns[i+1], kernel_szs[i], stride=strides[i])
for i in range(len(strides))]
layers.append(nn.Sequential(AdaptiveConcatPool2d(1), Flatten()))
return nn.Sequential(*layers)
model = simple_cnn((3,16,16,2))
learner = Learner(data, model, metrics=[accuracy])
learner.fit(1)
This is very useful to use functions as layers in our networks inside a Sequential object. So, for example, say we want to apply a log_softmax loss and we need to change the shape of our output batches to be able to use this loss. We can add a layer that applies the necessary change in shape by calling:
Lambda(lambda x: x.view(x.size(0),-1))
Let's see an example of how the shape of our output can change when we add this layer.
model = nn.Sequential(
nn.Conv2d(3, 16, kernel_size=3, stride=2, padding=1), nn.ReLU(),
nn.Conv2d(16, 16, kernel_size=3, stride=2, padding=1), nn.ReLU(),
nn.Conv2d(16, 10, kernel_size=3, stride=2, padding=1), nn.ReLU(),
nn.AdaptiveAvgPool2d(1),
)
model.cuda()
for xb, yb in data.train_dl:
out = (model(*[xb]))
print(out.size())
break
model = nn.Sequential(
nn.Conv2d(3, 16, kernel_size=3, stride=2, padding=1), nn.ReLU(),
nn.Conv2d(16, 16, kernel_size=3, stride=2, padding=1), nn.ReLU(),
nn.Conv2d(16, 10, kernel_size=3, stride=2, padding=1), nn.ReLU(),
nn.AdaptiveAvgPool2d(1),
Lambda(lambda x: x.view(x.size(0),-1))
)
model.cuda()
for xb, yb in data.train_dl:
out = (model(*[xb]))
print(out.size())
break
The function we build above is actually implemented in our library as Flatten. We can see that it returns the same size when we run it.
model = nn.Sequential(
nn.Conv2d(3, 16, kernel_size=3, stride=2, padding=1), nn.ReLU(),
nn.Conv2d(16, 16, kernel_size=3, stride=2, padding=1), nn.ReLU(),
nn.Conv2d(16, 10, kernel_size=3, stride=2, padding=1), nn.ReLU(),
nn.AdaptiveAvgPool2d(1),
Flatten(),
)
model.cuda()
for xb, yb in data.train_dl:
out = (model(*[xb]))
print(out.size())
break
We can combine these two final layers (AdaptiveAvgPool2d and Flatten) by using PoolFlatten.
model = nn.Sequential(
nn.Conv2d(3, 16, kernel_size=3, stride=2, padding=1), nn.ReLU(),
nn.Conv2d(16, 16, kernel_size=3, stride=2, padding=1), nn.ReLU(),
nn.Conv2d(16, 10, kernel_size=3, stride=2, padding=1), nn.ReLU(),
PoolFlatten()
)
model.cuda()
for xb, yb in data.train_dl:
out = (model(*[xb]))
print(out.size())
break
Another use we give to the Lambda function is to resize batches with ResizeBatch when we have a layer that expects a different input than what comes from the previous one.
a = torch.tensor([[1., -1.], [1., -1.]])[None]
print(a)
out = ResizeBatch(4)
print(out(a))
The debugger module allows us to peek inside a network while its training and see in detail what is going on. We can see inputs, outputs and sizes at any point in the network.
For instance, if you run the following:
model = nn.Sequential(
nn.Conv2d(3, 16, kernel_size=3, stride=2, padding=1), nn.ReLU(),
Debugger(),
nn.Conv2d(16, 16, kernel_size=3, stride=2, padding=1), nn.ReLU(),
nn.Conv2d(16, 10, kernel_size=3, stride=2, padding=1), nn.ReLU(),
)
model.cuda()
learner = Learner(data, model, metrics=[accuracy])
learner.fit(5)
... you'll see something like this:
/home/ubuntu/fastai/fastai/layers.py(74)forward()
72 def forward(self,x:Tensor) -> Tensor:
73 set_trace()
---> 74 return x
75
76 class StdUpsample(nn.Module):
ipdb>Loss functions¶
Create an instance of func with args and kwargs. When passing an output and target, it
- puts
axisfirst in output and target with a transpose - casts the target to
floatiffloatify=True - squeezes the
outputto two dimensions ifis_2d, otherwise one dimension, squeezes the target to one dimension - applies the instance of
func.
Helper functions to create modules¶
The bn_drop_lin function returns a sequence of batch normalization, dropout and a linear layer. This custom layer is usually used at the end of a model.
n_in represents the size of the input, n_out the size of the output, bn whether we want batch norm or not, p how much dropout, and actn (optional parameter) adds an activation function at the end.
The conv_layer function returns a sequence of nn.Conv2D, BatchNorm and a ReLU or leaky RELU activation function.
n_in represents the size of the input, n_out the size of the output, ks the kernel size, stride the stride with which we want to apply the convolutions. bias will decide if they have bias or not (if None, defaults to True unless using batchnorm). norm_type selects the type of normalization (or None). If leaky is None, the activation is a standard ReLU, otherwise it's a LeakyReLU of slope leaky. Finally if transpose=True, the convolution is replaced by a ConvTranspose2D.
Create an embedding layer with input size ni and output size nf.