File size: 6,216 Bytes
3215d8d |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 |
class TrainerCallback:
def __init__(self) -> None:
self.callbacks_on_init_start = []
self.callbacks_on_init_end = []
self.callbacks_on_epoch_start = []
self.callbacks_on_epoch_end = []
self.callbacks_on_train_step_start = []
self.callbacks_on_train_step_end = []
self.callbacks_on_keyboard_interrupt = []
def on_init_start(self, trainer) -> None:
if hasattr(trainer.model, "module"):
if hasattr(trainer.model.module, "on_init_start"):
trainer.model.module.on_init_start(trainer)
else:
if hasattr(trainer.model, "on_init_start"):
trainer.model.on_init_start(trainer)
if hasattr(trainer.criterion, "on_init_start"):
trainer.criterion.on_init_start(trainer)
if hasattr(trainer.optimizer, "on_init_start"):
trainer.optimizer.on_init_start(trainer)
if self.callbacks_on_init_start:
for callback in self.callbacks_on_init_start:
callback(trainer)
def on_init_end(self, trainer) -> None:
if hasattr(trainer.model, "module"):
if hasattr(trainer.model.module, "on_init_end"):
trainer.model.module.on_init_end(trainer)
else:
if hasattr(trainer.model, "on_init_end"):
trainer.model.on_init_end(trainer)
if hasattr(trainer.criterion, "on_init_end"):
trainer.criterion.on_init_end(trainer)
if hasattr(trainer.optimizer, "on_init_end"):
trainer.optimizer.on_init_end(trainer)
if self.callbacks_on_init_end:
for callback in self.callbacks_on_init_start:
callback(trainer)
def on_epoch_start(self, trainer) -> None:
if hasattr(trainer.model, "module"):
if hasattr(trainer.model.module, "on_epoch_start"):
trainer.model.module.on_epoch_start(trainer)
else:
if hasattr(trainer.model, "on_epoch_start"):
trainer.model.on_epoch_start(trainer)
if hasattr(trainer.criterion, "on_epoch_start"):
trainer.criterion.on_epoch_start(trainer)
if hasattr(trainer.optimizer, "on_epoch_start"):
trainer.optimizer.on_epoch_start(trainer)
if self.callbacks_on_epoch_start:
for callback in self.callbacks_on_epoch_start:
callback(trainer)
def on_epoch_end(self, trainer) -> None:
if hasattr(trainer.model, "module"):
if hasattr(trainer.model.module, "on_epoch_end"):
trainer.model.module.on_epoch_end(trainer)
else:
if hasattr(trainer.model, "on_epoch_end"):
trainer.model.on_epoch_end(trainer)
if hasattr(trainer.criterion, "on_epoch_end"):
trainer.criterion.on_epoch_end(trainer)
if hasattr(trainer.optimizer, "on_epoch_end"):
trainer.optimizer.on_epoch_end(trainer)
if self.callbacks_on_epoch_end:
for callback in self.callbacks_on_epoch_end:
callback(trainer)
@staticmethod
def before_backward_pass(trainer, loss_dict) -> None:
if hasattr(trainer.model, "module"):
if hasattr(trainer.model.module, "before_backward_pass"):
trainer.model.module.before_backward_pass(loss_dict, trainer.optimizer)
else:
if hasattr(trainer.model, "before_backward_pass"):
trainer.model.before_backward_pass(loss_dict, trainer.optimizer)
@staticmethod
def before_gradient_clipping(trainer) -> None:
if hasattr(trainer.model, "module"):
if hasattr(trainer.model.module, "before_gradient_clipping"):
trainer.model.module.before_gradient_clipping()
else:
if hasattr(trainer.model, "before_gradient_clipping"):
trainer.model.before_gradient_clipping()
def on_train_step_start(self, trainer) -> None:
if hasattr(trainer.model, "module"):
if hasattr(trainer.model.module, "on_train_step_start"):
trainer.model.module.on_train_step_start(trainer)
else:
if hasattr(trainer.model, "on_train_step_start"):
trainer.model.on_train_step_start(trainer)
if hasattr(trainer.criterion, "on_train_step_start"):
trainer.criterion.on_train_step_start(trainer)
if hasattr(trainer.optimizer, "on_train_step_start"):
trainer.optimizer.on_train_step_start(trainer)
if self.callbacks_on_train_step_start:
for callback in self.callbacks_on_train_step_start:
callback(trainer)
def on_train_step_end(self, trainer) -> None:
if hasattr(trainer.model, "module"):
if hasattr(trainer.model.module, "on_train_step_end"):
trainer.model.module.on_train_step_end(trainer)
else:
if hasattr(trainer.model, "on_train_step_end"):
trainer.model.on_train_step_end(trainer)
if hasattr(trainer.criterion, "on_train_step_end"):
trainer.criterion.on_train_step_end(trainer)
if hasattr(trainer.optimizer, "on_train_step_end"):
trainer.optimizer.on_train_step_end(trainer)
if self.callbacks_on_train_step_end:
for callback in self.callbacks_on_train_step_end:
callback(trainer)
def on_keyboard_interrupt(self, trainer) -> None:
if hasattr(trainer.model, "module"):
if hasattr(trainer.model.module, "on_keyboard_interrupt"):
trainer.model.module.on_keyboard_interrupt(trainer)
else:
if hasattr(trainer.model, "on_keyboard_interrupt"):
trainer.model.on_keyboard_interrupt(trainer)
if hasattr(trainer.criterion, "on_keyboard_interrupt"):
trainer.criterion.on_keyboard_interrupt(trainer)
if hasattr(trainer.optimizer, "on_keyboard_interrupt"):
trainer.optimizer.on_keyboard_interrupt(trainer)
if self.callbacks_on_keyboard_interrupt:
for callback in self.callbacks_on_keyboard_interrupt:
callback(trainer)
|