111 lines
4.3 KiB
Python
111 lines
4.3 KiB
Python
import torch
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from functools import reduce
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from operator import mul
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from torch import nn
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from datasets.trajectory_dataset import TrajData
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from ml_lib.evaluation.classification import ROCEvaluation
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from models.generators.cnn import CNNRouteGeneratorModel
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import matplotlib.pyplot as plt
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class CNNRouteGeneratorDiscriminated(CNNRouteGeneratorModel):
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name = 'CNNRouteGeneratorDiscriminated'
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def training_step(self, batch_xy, batch_nb, *args, **kwargs):
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batch_x, label = batch_xy
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generated_alternative, z, mu, logvar = self(batch_x)
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map_array, trajectory = batch_x
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map_stack = torch.cat((map_array, trajectory, generated_alternative), dim=1)
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pred_label = self.discriminator(map_stack)
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discriminated_bce_loss = self.criterion(pred_label, label.float().unsqueeze(-1))
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# see Appendix B from VAE paper:
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# Kingma and Welling. Auto-Encoding Variational Bayes. ICLR, 2014
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# https://arxiv.org/abs/1312.6114
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# 0.5 * sum(1 + log(sigma^2) - mu^2 - sigma^2)
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kld_loss = -0.5 * torch.sum(1 + logvar - mu.pow(2) - logvar.exp())
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# Dimensional Resizing
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kld_loss /= reduce(mul, self.in_shape)
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loss = (kld_loss + discriminated_bce_loss) / 2
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return dict(loss=loss, log=dict(loss=loss,
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discriminated_bce_loss=discriminated_bce_loss,
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kld_loss=kld_loss)
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)
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def _test_val_step(self, batch_xy, batch_nb, *args):
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batch_x, label = batch_xy
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generated_alternative, z, mu, logvar = self(batch_x)
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map_array, trajectory = batch_x
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map_stack = torch.cat((map_array, trajectory, generated_alternative), dim=1)
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pred_label = self.discriminator(map_stack)
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discriminated_bce_loss = self.criterion(pred_label, label.float().unsqueeze(-1))
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return dict(discriminated_bce_loss=discriminated_bce_loss, batch_nb=batch_nb,
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pred_label=pred_label, label=label, generated_alternative=generated_alternative)
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def validation_step(self, *args):
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return self._test_val_step(*args)
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def validation_epoch_end(self, outputs: list):
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return self._test_val_epoch_end(outputs)
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def _test_val_epoch_end(self, outputs, test=False):
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evaluation = ROCEvaluation(plot_roc=True)
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pred_label = torch.cat([x['pred_label'] for x in outputs])
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labels = torch.cat([x['label'] for x in outputs]).unsqueeze(1)
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mean_losses = torch.stack([x['discriminated_bce_loss'] for x in outputs]).mean()
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# Sci-py call ROC eval call is eval(true_label, prediction)
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roc_auc, tpr, fpr = evaluation(labels.cpu().numpy(), pred_label.cpu().numpy(), )
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if test:
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# self.logger.log_metrics(score_dict)
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self.logger.log_image(f'{self.name}_ROC-Curve', plt.gcf(), step=self.global_step)
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plt.clf()
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maps, trajectories, labels, val_restul_dict = self.generate_random()
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from generator_eval import GeneratorVisualizer
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g = GeneratorVisualizer(maps, trajectories, labels, val_restul_dict)
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fig = g.draw()
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self.logger.log_image(f'{self.name}_Output', fig, step=self.global_step)
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plt.clf()
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return dict(mean_losses=mean_losses, roc_auc=roc_auc, epoch=self.current_epoch)
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def test_step(self, *args):
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return self._test_val_step(*args)
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def test_epoch_end(self, outputs):
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return self._test_val_epoch_end(outputs, test=True)
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@property
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def discriminator(self):
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if self._disc is None:
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raise RuntimeError('Set the Discriminator first; "set_discriminator(disc_model)')
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return self._disc
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def set_discriminator(self, disc_model):
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if self._disc is not None:
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raise RuntimeError('Discriminator has already been set... What are trying to do?')
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self._disc = disc_model
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def __init__(self, *params):
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raise NotImplementedError
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super(CNNRouteGeneratorDiscriminated, self).__init__(*params, issubclassed=True)
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self._disc = None
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self.criterion = nn.BCELoss()
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self.dataset = TrajData(self.hparams.data_param.map_root, mode='just_route', preprocessed=True,
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length=self.hparams.data_param.dataset_length, normalized=True)
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