| from utility.loading_bar import LoadingBar |
| import time |
|
|
|
|
| class Log: |
| def __init__(self, log_each: int, initial_epoch=-1): |
| self.loading_bar = LoadingBar(length=27) |
| self.best_accuracy = 0.0 |
| self.log_each = log_each |
| self.epoch = initial_epoch |
|
|
| def train(self, len_dataset: int) -> None: |
| self.epoch += 1 |
| if self.epoch == 0: |
| self._print_header() |
| else: |
| self.flush() |
|
|
| self.is_train = True |
| self.last_steps_state = {"loss": 0.0, "accuracy": 0.0, "steps": 0} |
| self._reset(len_dataset) |
|
|
| def eval(self, len_dataset: int) -> None: |
| self.flush() |
| self.is_train = False |
| self._reset(len_dataset) |
|
|
| def __call__(self, model, loss, accuracy, learning_rate: float = None) -> None: |
| if self.is_train: |
| self._train_step(model, loss, accuracy, learning_rate) |
| else: |
| self._eval_step(loss, accuracy) |
|
|
| def flush(self) -> None: |
| if self.is_train: |
| loss = self.epoch_state["loss"] / self.epoch_state["steps"] |
| accuracy = self.epoch_state["accuracy"] / self.epoch_state["steps"] |
|
|
| print( |
| f"\r┃{self.epoch:12d} ┃{loss:12.4f} │{100*accuracy:10.2f} % ┃{self.learning_rate:12.3e} │{self._time():>12} ┃", |
| end="", |
| flush=True, |
| ) |
|
|
| else: |
| loss = self.epoch_state["loss"] / self.epoch_state["steps"] |
| accuracy = self.epoch_state["accuracy"] / self.epoch_state["steps"] |
|
|
| print(f"{loss:12.4f} │{100*accuracy:10.2f} % ┃", flush=True) |
|
|
| if accuracy > self.best_accuracy: |
| self.best_accuracy = accuracy |
|
|
| def _train_step(self, model, loss, accuracy, learning_rate: float) -> None: |
| self.learning_rate = learning_rate |
| self.last_steps_state["loss"] += loss.sum().item() |
| self.last_steps_state["accuracy"] += accuracy.sum().item() |
| self.last_steps_state["steps"] += loss.size(0) |
| self.epoch_state["loss"] += loss.sum().item() |
| self.epoch_state["accuracy"] += accuracy.sum().item() |
| self.epoch_state["steps"] += loss.size(0) |
| self.step += 1 |
|
|
| if self.step % self.log_each == self.log_each - 1: |
| loss = self.last_steps_state["loss"] / self.last_steps_state["steps"] |
| accuracy = self.last_steps_state["accuracy"] / self.last_steps_state["steps"] |
|
|
| self.last_steps_state = {"loss": 0.0, "accuracy": 0.0, "steps": 0} |
| progress = self.step / self.len_dataset |
|
|
| print( |
| f"\r┃{self.epoch:12d} ┃{loss:12.4f} │{100*accuracy:10.2f} % ┃{learning_rate:12.3e} │{self._time():>12} {self.loading_bar(progress)}", |
| end="", |
| flush=True, |
| ) |
|
|
| def _eval_step(self, loss, accuracy) -> None: |
| self.epoch_state["loss"] += loss.sum().item() |
| self.epoch_state["accuracy"] += accuracy.sum().item() |
| self.epoch_state["steps"] += loss.size(0) |
|
|
| def _reset(self, len_dataset: int) -> None: |
| self.start_time = time.time() |
| self.step = 0 |
| self.len_dataset = len_dataset |
| self.epoch_state = {"loss": 0.0, "accuracy": 0.0, "steps": 0} |
|
|
| def _time(self) -> str: |
| time_seconds = int(time.time() - self.start_time) |
| return f"{time_seconds // 60:02d}:{time_seconds % 60:02d} min" |
|
|
| def _print_header(self) -> None: |
| print(f"┏━━━━━━━━━━━━━━┳━━━━━━━╸T╺╸R╺╸A╺╸I╺╸N╺━━━━━━━┳━━━━━━━╸S╺╸T╺╸A╺╸T╺╸S╺━━━━━━━┳━━━━━━━╸V╺╸A╺╸L╺╸I╺╸D╺━━━━━━━┓") |
| print(f"┃ ┃ ╷ ┃ ╷ ┃ ╷ ┃") |
| print(f"┃ epoch ┃ loss │ accuracy ┃ l.r. │ elapsed ┃ loss │ accuracy ┃") |
| print(f"┠──────────────╂──────────────┼──────────────╂──────────────┼──────────────╂──────────────┼──────────────┨") |
|
|
