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from abc import abstractmethod |
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from typing import Any, Dict, List |
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import torch |
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from pytorch_lightning import LightningModule |
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from torch import Tensor |
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class LightningRegression(LightningModule): |
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@abstractmethod |
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def __init__(self, *args, **kwargs) -> None: |
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super(LightningRegression, self).__init__(*args, **kwargs) |
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self.train_step_output: List[Dict] = [] |
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self.validation_step_output: List[Dict] = [] |
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self.log_value_list: List[str] = ['loss', 'mse', 'mape'] |
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@abstractmethod |
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def forward(self, *args, **kwargs) -> Any: |
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pass |
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@abstractmethod |
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def configure_optimizers(self): |
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pass |
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@abstractmethod |
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def loss(self, input: Tensor, output: Tensor, **kwargs): |
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return 0 |
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@abstractmethod |
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def training_step(self, batch, batch_idx): |
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pass |
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def __average(self, key: str, outputs: List[Dict]) -> Tensor: |
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target_arr = torch.Tensor([val[key] for val in outputs]).float() |
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return target_arr.mean() |
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@torch.no_grad() |
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def on_train_epoch_end(self) -> None: |
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for key in self.log_value_list: |
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val = self.__average(key=key, outputs=self.train_step_output) |
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log_name = f"training/{key}" |
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self.log(name=log_name, value=val) |
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@torch.no_grad() |
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@abstractmethod |
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def validation_step(self, batch, batch_idx): |
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pass |
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@torch.no_grad() |
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def validation_epoch_end(self, outputs): |
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for key in self.log_value_list: |
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val = self.__average(key=key, outputs=self.validation_step_output) |
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log_name = f"training/{key}" |
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self.log(name=log_name, value=val) |
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