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* added callbacks * Update callbacks.py * Update train.py * Update val.py * Fix CamlCase add staticmethod * Refactor logger into callbacks * Cleanup * New callback on_val_image_end() * Add curves and results images to TensorBoard Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com>
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,176 @@ | ||
| #!/usr/bin/env python | ||
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| class Callbacks: | ||
| """" | ||
| Handles all registered callbacks for YOLOv5 Hooks | ||
| """ | ||
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| _callbacks = { | ||
| 'on_pretrain_routine_start': [], | ||
| 'on_pretrain_routine_end': [], | ||
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| 'on_train_start': [], | ||
| 'on_train_epoch_start': [], | ||
| 'on_train_batch_start': [], | ||
| 'optimizer_step': [], | ||
| 'on_before_zero_grad': [], | ||
| 'on_train_batch_end': [], | ||
| 'on_train_epoch_end': [], | ||
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| 'on_val_start': [], | ||
| 'on_val_batch_start': [], | ||
| 'on_val_image_end': [], | ||
| 'on_val_batch_end': [], | ||
| 'on_val_end': [], | ||
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| 'on_fit_epoch_end': [], # fit = train + val | ||
| 'on_model_save': [], | ||
| 'on_train_end': [], | ||
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| 'teardown': [], | ||
| } | ||
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| def __init__(self): | ||
| return | ||
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| def register_action(self, hook, name='', callback=None): | ||
| """ | ||
| Register a new action to a callback hook | ||
| Args: | ||
| hook The callback hook name to register the action to | ||
| name The name of the action | ||
| callback The callback to fire | ||
| """ | ||
| assert hook in self._callbacks, f"hook '{hook}' not found in callbacks {self._callbacks}" | ||
| assert callable(callback), f"callback '{callback}' is not callable" | ||
| self._callbacks[hook].append({'name': name, 'callback': callback}) | ||
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| def get_registered_actions(self, hook=None): | ||
| """" | ||
| Returns all the registered actions by callback hook | ||
| Args: | ||
| hook The name of the hook to check, defaults to all | ||
| """ | ||
| if hook: | ||
| return self._callbacks[hook] | ||
| else: | ||
| return self._callbacks | ||
|
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| @staticmethod | ||
| def run_callbacks(register, *args, **kwargs): | ||
| """ | ||
| Loop through the registered actions and fire all callbacks | ||
| """ | ||
| for logger in register: | ||
| # print(f"Running callbacks.{logger['callback'].__name__}()") | ||
| logger['callback'](*args, **kwargs) | ||
|
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| def on_pretrain_routine_start(self, *args, **kwargs): | ||
| """ | ||
| Fires all registered callbacks at the start of each pretraining routine | ||
| """ | ||
| self.run_callbacks(self._callbacks['on_pretrain_routine_start'], *args, **kwargs) | ||
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| def on_pretrain_routine_end(self, *args, **kwargs): | ||
| """ | ||
| Fires all registered callbacks at the end of each pretraining routine | ||
| """ | ||
| self.run_callbacks(self._callbacks['on_pretrain_routine_end'], *args, **kwargs) | ||
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| def on_train_start(self, *args, **kwargs): | ||
| """ | ||
| Fires all registered callbacks at the start of each training | ||
| """ | ||
| self.run_callbacks(self._callbacks['on_train_start'], *args, **kwargs) | ||
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| def on_train_epoch_start(self, *args, **kwargs): | ||
| """ | ||
| Fires all registered callbacks at the start of each training epoch | ||
| """ | ||
| self.run_callbacks(self._callbacks['on_train_epoch_start'], *args, **kwargs) | ||
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| def on_train_batch_start(self, *args, **kwargs): | ||
| """ | ||
| Fires all registered callbacks at the start of each training batch | ||
| """ | ||
| self.run_callbacks(self._callbacks['on_train_batch_start'], *args, **kwargs) | ||
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| def optimizer_step(self, *args, **kwargs): | ||
| """ | ||
| Fires all registered callbacks on each optimizer step | ||
| """ | ||
| self.run_callbacks(self._callbacks['optimizer_step'], *args, **kwargs) | ||
|
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| def on_before_zero_grad(self, *args, **kwargs): | ||
| """ | ||
| Fires all registered callbacks before zero grad | ||
| """ | ||
| self.run_callbacks(self._callbacks['on_before_zero_grad'], *args, **kwargs) | ||
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| def on_train_batch_end(self, *args, **kwargs): | ||
| """ | ||
| Fires all registered callbacks at the end of each training batch | ||
| """ | ||
| self.run_callbacks(self._callbacks['on_train_batch_end'], *args, **kwargs) | ||
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| def on_train_epoch_end(self, *args, **kwargs): | ||
| """ | ||
| Fires all registered callbacks at the end of each training epoch | ||
| """ | ||
| self.run_callbacks(self._callbacks['on_train_epoch_end'], *args, **kwargs) | ||
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| def on_val_start(self, *args, **kwargs): | ||
| """ | ||
| Fires all registered callbacks at the start of the validation | ||
| """ | ||
| self.run_callbacks(self._callbacks['on_val_start'], *args, **kwargs) | ||
|
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| def on_val_batch_start(self, *args, **kwargs): | ||
| """ | ||
| Fires all registered callbacks at the start of each validation batch | ||
| """ | ||
| self.run_callbacks(self._callbacks['on_val_batch_start'], *args, **kwargs) | ||
|
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| def on_val_image_end(self, *args, **kwargs): | ||
| """ | ||
| Fires all registered callbacks at the end of each val image | ||
| """ | ||
| self.run_callbacks(self._callbacks['on_val_image_end'], *args, **kwargs) | ||
|
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| def on_val_batch_end(self, *args, **kwargs): | ||
| """ | ||
| Fires all registered callbacks at the end of each validation batch | ||
| """ | ||
| self.run_callbacks(self._callbacks['on_val_batch_end'], *args, **kwargs) | ||
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| def on_val_end(self, *args, **kwargs): | ||
| """ | ||
| Fires all registered callbacks at the end of the validation | ||
| """ | ||
| self.run_callbacks(self._callbacks['on_val_end'], *args, **kwargs) | ||
|
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| def on_fit_epoch_end(self, *args, **kwargs): | ||
| """ | ||
| Fires all registered callbacks at the end of each fit (train+val) epoch | ||
| """ | ||
| self.run_callbacks(self._callbacks['on_fit_epoch_end'], *args, **kwargs) | ||
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| def on_model_save(self, *args, **kwargs): | ||
| """ | ||
| Fires all registered callbacks after each model save | ||
| """ | ||
| self.run_callbacks(self._callbacks['on_model_save'], *args, **kwargs) | ||
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| def on_train_end(self, *args, **kwargs): | ||
| """ | ||
| Fires all registered callbacks at the end of training | ||
| """ | ||
| self.run_callbacks(self._callbacks['on_train_end'], *args, **kwargs) | ||
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| def teardown(self, *args, **kwargs): | ||
| """ | ||
| Fires all registered callbacks before teardown | ||
| """ | ||
| self.run_callbacks(self._callbacks['teardown'], *args, **kwargs) |
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