52 lines
1.6 KiB
Python
52 lines
1.6 KiB
Python
import torch
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from torch import nn
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from torch.optim import Adam
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from ml_lib.modules.blocks import ConvModule
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from ml_lib.modules.utils import LightningBaseModule
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class BinaryClassifier(LightningBaseModule):
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@classmethod
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def name(cls):
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return cls.__name__
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def configure_optimizers(self):
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return Adam(lr=self.hparams.train.lr)
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def training_step(self, batch_xy, batch_nb, *args, **kwargs):
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batch_x, batch_y = batch_xy
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y = self(batch_y)
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loss = self.criterion(y, batch_y)
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return dict(loss=loss)
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def validation_step(self, batch_xy, **kwargs):
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batch_x, batch_y = batch_xy
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y = self(batch_y)
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val_loss = self.criterion(y, batch_y)
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return dict(val_loss=val_loss)
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def validation_epoch_end(self, outputs):
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overall_val_loss = torch.mean(torch.stack([output['val_loss'] for output in outputs]))
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return dict(log=dict(
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mean_val_loss=overall_val_loss)
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)
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def __init__(self, hparams):
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super(BinaryClassifier, self).__init__(hparams)
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self.criterion = nn.BCELoss()
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# Additional parameters
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self.in_shape = self.hparams.model_params.in_shape
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# Model Modules
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self.conv_1 = ConvModule(self.in_shape, 32, 5, conv_stride=4, **hparams.model_params)
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self.conv_2 = ConvModule(self.conv_1.shape, 64, 7, conv_stride=2, **hparams.model_params)
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self.conv_3 = ConvModule(self.conv_1.shape, 128, 9, conv_stride=2, **hparams.model_params)
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def forward(self, batch, **kwargs):
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return batch
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