270 lines
11 KiB
Python
270 lines
11 KiB
Python
from statistics import mean
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from random import choice
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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 torch.optim import Adam
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from datasets.trajectory_dataset import TrajData
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from lib.evaluation.classification import ROCEvaluation
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from lib.modules.blocks import ConvModule, ResidualModule, DeConvModule
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from lib.modules.utils import LightningBaseModule, Flatten
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import matplotlib.pyplot as plt
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class CNNRouteGeneratorModel(LightningBaseModule):
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name = 'CNNRouteGenerator'
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def configure_optimizers(self):
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return Adam(self.parameters(), lr=self.hparams.train_param.lr)
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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 + [label, ])
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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 + [label, ])
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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_E{self.current_epoch}', plt.gcf())
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plt.clf()
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maps, trajectories, labels, val_restul_dict = self.generate_random()
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from lib.visualization.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_E{self.current_epoch}', fig)
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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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super(CNNRouteGeneratorModel, self).__init__(*params)
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# Dataset
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self.dataset = TrajData(self.hparams.data_param.map_root, mode='just_route',
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length=self.hparams.data_param.dataset_length)
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# Additional Attributes
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self.in_shape = self.dataset.map_shapes_max
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# Todo: Better naming and size in Parameters
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self.feature_dim = 10
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self.lat_dim = self.feature_dim + self.feature_dim + 1
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self._disc = None
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# NN Nodes
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###################################################
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#
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# Utils
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self.relu = nn.ReLU()
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self.sigmoid = nn.Sigmoid()
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self.criterion = nn.MSELoss()
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#
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# Map Encoder
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self.map_conv_0 = ConvModule(self.in_shape, conv_kernel=3, conv_stride=1, conv_padding=1,
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conv_filters=self.hparams.model_param.filters[0])
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self.map_res_1 = ResidualModule(self.map_conv_0.shape, ConvModule, 2, conv_kernel=3, conv_stride=1,
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conv_padding=1, conv_filters=self.hparams.model_param.filters[0])
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self.map_conv_1 = ConvModule(self.map_res_1.shape, conv_kernel=3, conv_stride=1, conv_padding=0,
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conv_filters=self.hparams.model_param.filters[1])
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self.map_res_2 = ResidualModule(self.map_conv_1.shape, ConvModule, 2, conv_kernel=3, conv_stride=1,
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conv_padding=1, conv_filters=self.hparams.model_param.filters[1])
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self.map_conv_2 = ConvModule(self.map_res_2.shape, conv_kernel=3, conv_stride=1, conv_padding=0,
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conv_filters=self.hparams.model_param.filters[2])
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self.map_res_3 = ResidualModule(self.map_conv_2.shape, ConvModule, 2, conv_kernel=3, conv_stride=1,
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conv_padding=1, conv_filters=self.hparams.model_param.filters[2])
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self.map_conv_3 = ConvModule(self.map_res_3.shape, conv_kernel=5, conv_stride=1, conv_padding=0,
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conv_filters=self.hparams.model_param.filters[2]*2)
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self.map_flat = Flatten(self.map_conv_3.shape)
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self.map_lin = nn.Linear(reduce(mul, self.map_conv_3.shape), self.feature_dim)
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#
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# Trajectory Encoder
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self.traj_conv_1 = ConvModule(self.in_shape, conv_kernel=3, conv_stride=1, conv_padding=0,
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conv_filters=self.hparams.model_param.filters[0])
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self.traj_conv_2 = ConvModule(self.traj_conv_1.shape, conv_kernel=3, conv_stride=1, conv_padding=0,
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conv_filters=self.hparams.model_param.filters[0])
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self.traj_conv_3 = ConvModule(self.traj_conv_2.shape, conv_kernel=3, conv_stride=1, conv_padding=0,
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conv_filters=self.hparams.model_param.filters[0])
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self.traj_flat = Flatten(self.traj_conv_3.shape)
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self.traj_lin = nn.Linear(reduce(mul, self.traj_conv_3.shape), self.feature_dim)
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#
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# Mixed Encoder
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self.mixed_lin = nn.Linear(self.lat_dim, self.lat_dim)
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#
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# Variational Bottleneck
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self.mu = nn.Linear(self.lat_dim, self.hparams.model_param.lat_dim)
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self.logvar = nn.Linear(self.lat_dim, self.hparams.model_param.lat_dim)
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#
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# Alternative Generator
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self.alt_lin_1 = nn.Linear(self.hparams.model_param.lat_dim, self.feature_dim)
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self.alt_lin_2 = nn.Linear(self.feature_dim, reduce(mul, self.traj_conv_3.shape))
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self.reshape_to_map = Flatten(reduce(mul, self.traj_conv_3.shape), self.traj_conv_3.shape)
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self.alt_deconv_1 = DeConvModule(self.traj_conv_3.shape, self.hparams.model_param.filters[2],
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conv_padding=0, conv_kernel=5, conv_stride=1)
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self.alt_deconv_2 = DeConvModule(self.alt_deconv_1.shape, self.hparams.model_param.filters[1],
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conv_padding=0, conv_kernel=3, conv_stride=1)
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self.alt_deconv_3 = DeConvModule(self.alt_deconv_2.shape, self.hparams.model_param.filters[0],
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conv_padding=1, conv_kernel=3, conv_stride=1)
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self.alt_deconv_out = DeConvModule(self.alt_deconv_3.shape, 1, activation=None,
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conv_padding=1, conv_kernel=3, conv_stride=1)
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def forward(self, batch_x):
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#
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# Sorting the Input
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map_array, trajectory, label = batch_x
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#
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# Encode
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z, mu, logvar = self.encode(map_array, trajectory, label)
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#
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# Generate
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alt_tensor = self.generate(z)
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return alt_tensor, z, mu, logvar
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@staticmethod
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def reparameterize(mu, logvar):
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std = torch.exp(0.5 * logvar)
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eps = torch.randn_like(std)
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return mu + eps * std
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def generate(self, z):
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alt_tensor = self.alt_lin_1(z)
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alt_tensor = self.alt_lin_2(alt_tensor)
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alt_tensor = self.reshape_to_map(alt_tensor)
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alt_tensor = self.alt_deconv_1(alt_tensor)
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alt_tensor = self.alt_deconv_2(alt_tensor)
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alt_tensor = self.alt_deconv_3(alt_tensor)
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alt_tensor = self.alt_deconv_out(alt_tensor)
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alt_tensor = self.sigmoid(alt_tensor)
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return alt_tensor
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def encode(self, map_array, trajectory, label):
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map_tensor = self.map_conv_0(map_array)
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map_tensor = self.map_res_1(map_tensor)
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map_tensor = self.map_conv_1(map_tensor)
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map_tensor = self.map_res_2(map_tensor)
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map_tensor = self.map_conv_2(map_tensor)
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map_tensor = self.map_res_3(map_tensor)
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map_tensor = self.map_conv_3(map_tensor)
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map_tensor = self.map_flat(map_tensor)
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map_tensor = self.map_lin(map_tensor)
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traj_tensor = self.traj_conv_1(trajectory)
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traj_tensor = self.traj_conv_2(traj_tensor)
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traj_tensor = self.traj_conv_3(traj_tensor)
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traj_tensor = self.traj_flat(traj_tensor)
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traj_tensor = self.traj_lin(traj_tensor)
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mixed_tensor = torch.cat((map_tensor, traj_tensor, label.float().unsqueeze(-1)), dim=1)
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mixed_tensor = self.relu(mixed_tensor)
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mixed_tensor = self.mixed_lin(mixed_tensor)
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mixed_tensor = self.relu(mixed_tensor)
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#
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# Parameter and Sampling
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mu = self.mu(mixed_tensor)
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logvar = self.logvar(mixed_tensor)
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z = self.reparameterize(mu, logvar)
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return z, mu, logvar
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def generate_random(self, n=6):
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maps = [self.map_storage[choice(self.map_storage.keys_list)] for _ in range(n)]
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trajectories = [x.get_random_trajectory() for x in maps] * 2
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trajectories = [x.draw_in_array(self.map_storage.max_map_size) for x in trajectories]
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trajectories = [torch.as_tensor(x, dtype=torch.float32) for x in trajectories]
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trajectories = self._move_to_model_device(torch.stack(trajectories))
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maps = [torch.as_tensor(x.as_array, dtype=torch.float32) for x in maps] * 2
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maps = self._move_to_model_device(torch.stack(maps))
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labels = self._move_to_model_device(torch.as_tensor([0] * n + [1] * n))
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return maps, trajectories, labels, self._test_val_step(([maps, trajectories], labels), -9999)
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