Hook callbacks¶
This provides both a standalone class and a callback for registering and automatically deregistering PyTorch hooks, along with some pre-defined hooks. Hooks can be attached to any nn.Module, for either the forward or the backward pass.
We'll start by looking at the pre-defined hook ActivationStats, then we'll see how to create our own.
from fastai.gen_doc.nbdoc import *
from fastai.callbacks.hooks import *
from fastai.train import *
from fastai.vision import *
show_doc(ActivationStats)
class ActivationStats[source][test]
ActivationStats(learn:Learner,modules:Sequence[Module]=*None,do_remove:bool=True*) ::HookCallback
No tests found for ActivationStats. To contribute a test please refer to this guide and this discussion.
Callback that record the mean and std of activations.
ActivationStats saves the layer activations in self.stats for all modules passed to it. By default it will save activations for all modules. For instance:
path = untar_data(URLs.MNIST_SAMPLE)
data = ImageDataBunch.from_folder(path)
#learn = cnn_learner(data, models.resnet18, callback_fns=ActivationStats)
learn = Learner(data, simple_cnn((3,16,16,2)), callback_fns=ActivationStats)
learn.fit(1)
| epoch | train_loss | valid_loss |
|---|---|---|
| 1 | 0.112384 | 0.083544 |
The saved stats is a FloatTensor of shape (2,num_modules,num_batches). The first axis is (mean,stdev).
len(learn.data.train_dl),len(learn.activation_stats.modules)
(193, 3)
learn.activation_stats.stats.shape
torch.Size([2, 3, 193])
So this shows the standard deviation (axis0==1) of 2th last layer (axis1==-2) for each batch (axis2):
plt.plot(learn.activation_stats.stats[1][-2].numpy());
Internal implementation¶
show_doc(ActivationStats.hook)
Callback methods¶
You don't call these yourself - they're called by fastai's Callback system automatically to enable the class's functionality.
show_doc(ActivationStats.on_train_begin)
on_train_begin[source][test]
on_train_begin(****kwargs**)
No tests found for on_train_begin. To contribute a test please refer to this guide and this discussion.
Initialize stats.
show_doc(ActivationStats.on_batch_end)
on_batch_end[source][test]
on_batch_end(train, ****kwargs**)
No tests found for on_batch_end. To contribute a test please refer to this guide and this discussion.
Take the stored results and puts it in self.stats
show_doc(ActivationStats.on_train_end)
on_train_end[source][test]
on_train_end(****kwargs**)
No tests found for on_train_end. To contribute a test please refer to this guide and this discussion.
Polish the final result.
show_doc(Hook)
class Hook[source][test]
Hook(m:Module,hook_func:HookFunc,is_forward:bool=*True,detach:bool=True*)
No tests found for Hook. To contribute a test please refer to this guide and this discussion.
Create a hook on m with hook_func.
Registers and manually deregisters a PyTorch hook. Your hook_func will be called automatically when forward/backward (depending on is_forward) for your module m is run, and the result of that function is placed in self.stored.
show_doc(Hook.remove)
remove[source][test]
remove()
No tests found for remove. To contribute a test please refer to this guide and this discussion.
Remove the hook from the model.
Deregister the hook, if not called already.
show_doc(Hooks)
class Hooks[source][test]
Hooks(ms:ModuleList,hook_func:HookFunc,is_forward:bool=*True,detach:bool=True*)
No tests found for Hooks. To contribute a test please refer to this guide and this discussion.
Create several hooks on the modules in ms with hook_func.
Acts as a Collection (i.e. len(hooks) and hooks[i]) and an Iterator (i.e. for hook in hooks) of a group of hooks, one for each module in ms, with the ability to remove all as a group. Use stored to get all hook results. hook_func and is_forward behavior is the same as Hook. See the source code for HookCallback for a simple example.
show_doc(Hooks.remove)
remove[source][test]
remove()
No tests found for remove. To contribute a test please refer to this guide and this discussion.
Remove the hooks from the model.
Deregister all hooks created by this class, if not previously called.
Convenience functions for hooks¶
show_doc(hook_output)
Function that creates a Hook for module that simply stores the output of the layer.
show_doc(hook_outputs)
hook_outputs[source][test]
hook_outputs(modules:ModuleList,detach:bool=*True,grad:bool=False*) →Hooks
No tests found for hook_outputs. To contribute a test please refer to this guide and this discussion.
Return Hooks that store activations of all modules in self.stored
Function that creates a Hook for all passed modules that simply stores the output of the layers. For example, the (slightly simplified) source code of model_sizes is:
def model_sizes(m, size):
x = m(torch.zeros(1, in_channels(m), *size))
return [o.stored.shape for o in hook_outputs(m)]
show_doc(model_sizes)
model_sizes[source][test]
model_sizes(m:Module,size:tuple=*(64, 64)*) →Tuple[Sizes,Tensor,Hooks]
No tests found for model_sizes. To contribute a test please refer to this guide and this discussion.
Pass a dummy input through the model m to get the various sizes of activations.
show_doc(model_summary)
model_summary[source][test]
model_summary(m:Learner,n:int=*70*)
Tests found for model_summary:
pytest -sv tests/test_callbacks_hooks.py::test_model_summary_vision[source]pytest -sv tests/test_callbacks_hooks.py::test_model_summary_text[source]pytest -sv tests/test_callbacks_hooks.py::test_model_summary_tabular[source]pytest -sv tests/test_callbacks_hooks.py::test_model_summary_collab[source]
To run tests please refer to this guide.
Print a summary of m using a output text width of n chars
show_doc(num_features_model)
num_features_model[source][test]
num_features_model(m:Module) →int
No tests found for num_features_model. To contribute a test please refer to this guide and this discussion.
Return the number of output features for model.
It can be useful to get the size of each layer of a model (e.g. for printing a summary, or for generating cross-connections for a DynamicUnet), however they depend on the size of the input. This function calculates the layer sizes by passing in a minimal tensor of size.
show_doc(dummy_batch)
dummy_batch[source][test]
dummy_batch(m:Module,size:tuple=*(64, 64)*) →Tensor
No tests found for dummy_batch. To contribute a test please refer to this guide and this discussion.
Create a dummy batch to go through m with size.
show_doc(dummy_eval)
dummy_eval[source][test]
dummy_eval(m:Module,size:tuple=*(64, 64)*)
No tests found for dummy_eval. To contribute a test please refer to this guide and this discussion.
Pass a dummy_batch in evaluation mode in m with size.
show_doc(HookCallback)
class HookCallback[source][test]
HookCallback(learn:Learner,modules:Sequence[Module]=*None,do_remove:bool=True*) ::LearnerCallback
No tests found for HookCallback. To contribute a test please refer to this guide and this discussion.
Callback that can be used to register hooks on modules. Implement the corresponding function in self.hook.
For all modules, uses a callback to automatically register a method self.hook (that you must define in an inherited class) as a hook. This method must have the signature:
def hook(self, m:Model, input:Tensors, output:Tensors)
If do_remove then the hook is automatically deregistered at the end of training. See ActivationStats for a simple example of inheriting from this class.
Callback methods¶
You don't call these yourself - they're called by fastai's Callback system automatically to enable the class's functionality.
show_doc(HookCallback.on_train_begin)
on_train_begin[source][test]
on_train_begin(****kwargs**)
No tests found for on_train_begin. To contribute a test please refer to this guide and this discussion.
Register the Hooks on self.modules.
show_doc(HookCallback.on_train_end)
on_train_end[source][test]
on_train_end(****kwargs**)
No tests found for on_train_end. To contribute a test please refer to this guide and this discussion.
Remove the Hooks.
Undocumented Methods - Methods moved below this line will intentionally be hidden¶
show_doc(HookCallback.remove)
remove[source][test]
remove()
No tests found for remove. To contribute a test please refer to this guide and this discussion.
show_doc(Hook.hook_fn)
hook_fn[source][test]
hook_fn(module:Module,input:Tensors,output:Tensors)
No tests found for hook_fn. To contribute a test please refer to this guide and this discussion.
Applies hook_func to module, input, output.