Offline Datasets res net optionality
This commit is contained in:
Si11ium
@ -1,4 +1,5 @@
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from random import choice
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from random import choices, seed
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import numpy as np
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import torch
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from functools import reduce
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@ -36,28 +37,36 @@ class CNNRouteGeneratorModel(LightningBaseModule):
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# kld_loss /= reduce(mul, self.in_shape)
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# kld_loss *= self.hparams.data_param.dataset_length / self.hparams.train_param.batch_size * 100
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loss = (kld_loss + element_wise_loss) / 2
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loss = kld_loss + element_wise_loss
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return dict(loss=loss, log=dict(element_wise_loss=element_wise_loss, loss=loss, kld_loss=kld_loss))
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def _test_val_step(self, batch_xy, batch_nb, *args):
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batch_x, _ = batch_xy
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map_array, trajectory, label = batch_x
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map_array = batch_x[:, 0].unsqueeze(1)
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trajectory = batch_x[:, 1].unsqueeze(1)
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labels = batch_x[:, 2].unsqueeze(1).max(dim=-1).values.max(-1).values
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generated_alternative, z, mu, logvar = self(batch_x)
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return dict(batch_nb=batch_nb, label=label, generated_alternative=generated_alternative, pred_label=-1)
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_, mu, _ = self.encode(batch_x)
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generated_alternative = self.generate(mu)
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return dict(maps=map_array, trajectories=trajectory, batch_nb=batch_nb, labels=labels,
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generated_alternative=generated_alternative, pred_label=-1)
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def _test_val_epoch_end(self, outputs, test=False):
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maps, trajectories, labels, val_restul_dict = self.generate_random()
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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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g = GeneratorVisualizer(**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(epoch=self.current_epoch)
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def on_epoch_start(self):
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self.dataset.seed(self.logger.version)
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# torch.random.manual_seed(self.logger.version)
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# np.random.seed(self.logger.version)
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def validation_step(self, *args):
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return self._test_val_step(*args)
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@ -75,14 +84,18 @@ class CNNRouteGeneratorModel(LightningBaseModule):
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if not issubclassed:
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# Dataset
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self.dataset = TrajData(self.hparams.data_param.map_root, mode='separated_arrays',
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self.dataset = TrajData(self.hparams.data_param.map_root, mode='generator_all_in_map',
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preprocessed=self.hparams.data_param.use_preprocessed,
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length=self.hparams.data_param.dataset_length, normalized=True)
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self.criterion = nn.MSELoss()
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# Additional Attributes
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# Additional Attributes #
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#######################################################
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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 = self.hparams.model_param.lat_dim * 10
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self.use_res_net = self.hparams.model_param.use_res_net
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self.lat_dim = self.hparams.model_param.lat_dim
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self.feature_dim = self.lat_dim * 10
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########################################################
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# NN Nodes
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###################################################
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@ -93,82 +106,100 @@ class CNNRouteGeneratorModel(LightningBaseModule):
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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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self.enc_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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use_norm=self.hparams.model_param.use_norm,
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use_bias=self.hparams.model_param.use_bias)
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self.map_res_1 = ResidualModule(self.map_conv_0.shape, ConvModule, 2, conv_kernel=5, conv_stride=1,
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self.enc_res_1 = ResidualModule(self.enc_conv_0.shape, ConvModule, 2, conv_kernel=5, conv_stride=1,
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conv_padding=2, conv_filters=self.hparams.model_param.filters[0],
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use_norm=self.hparams.model_param.use_norm,
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use_bias=self.hparams.model_param.use_bias)
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self.map_conv_1 = ConvModule(self.map_res_1.shape, conv_kernel=5, conv_stride=1, conv_padding=0,
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conv_filters=self.hparams.model_param.filters[1],
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use_norm=self.hparams.model_param.use_norm,
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use_bias=self.hparams.model_param.use_bias)
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self.enc_conv_1a = ConvModule(self.enc_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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use_norm=self.hparams.model_param.use_norm,
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use_bias=self.hparams.model_param.use_bias)
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self.enc_conv_1b = ConvModule(self.enc_conv_1a.shape, conv_kernel=3, conv_stride=2, conv_padding=0,
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conv_filters=self.hparams.model_param.filters[1],
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use_norm=self.hparams.model_param.use_norm,
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use_bias=self.hparams.model_param.use_bias)
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self.map_res_2 = ResidualModule(self.map_conv_1.shape, ConvModule, 2, conv_kernel=5, conv_stride=1,
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self.enc_res_2 = ResidualModule(self.enc_conv_1b.shape, ConvModule, 2, conv_kernel=5, conv_stride=1,
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conv_padding=2, conv_filters=self.hparams.model_param.filters[1],
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use_norm=self.hparams.model_param.use_norm,
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use_bias=self.hparams.model_param.use_bias)
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self.map_conv_2 = ConvModule(self.map_res_2.shape, conv_kernel=7, conv_stride=1, conv_padding=0,
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conv_filters=self.hparams.model_param.filters[2],
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use_norm=self.hparams.model_param.use_norm,
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use_bias=self.hparams.model_param.use_bias)
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self.enc_conv_2a = ConvModule(self.enc_res_2.shape, conv_kernel=5, conv_stride=1, conv_padding=0,
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conv_filters=self.hparams.model_param.filters[2],
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use_norm=self.hparams.model_param.use_norm,
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use_bias=self.hparams.model_param.use_bias)
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self.enc_conv_2b = ConvModule(self.enc_conv_2a.shape, conv_kernel=5, conv_stride=1, conv_padding=0,
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conv_filters=self.hparams.model_param.filters[2],
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use_norm=self.hparams.model_param.use_norm,
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use_bias=self.hparams.model_param.use_bias)
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self.map_res_3 = ResidualModule(self.map_conv_2.shape, ConvModule, 2, conv_kernel=7, conv_stride=1,
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self.enc_res_3 = ResidualModule(self.enc_conv_2b.shape, ConvModule, 2, conv_kernel=7, conv_stride=1,
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conv_padding=3, conv_filters=self.hparams.model_param.filters[2],
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use_norm=self.hparams.model_param.use_norm,
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use_bias=self.hparams.model_param.use_bias)
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self.map_conv_3 = ConvModule(self.map_res_3.shape, conv_kernel=11, conv_stride=1, conv_padding=0,
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conv_filters=self.hparams.model_param.filters[2],
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use_norm=self.hparams.model_param.use_norm,
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use_bias=self.hparams.model_param.use_bias)
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self.enc_conv_3a = ConvModule(self.enc_res_3.shape, conv_kernel=7, conv_stride=1, conv_padding=0,
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conv_filters=self.hparams.model_param.filters[2],
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use_norm=self.hparams.model_param.use_norm,
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use_bias=self.hparams.model_param.use_bias)
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self.enc_conv_3b = ConvModule(self.enc_conv_3a.shape, conv_kernel=7, conv_stride=1, conv_padding=0,
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conv_filters=self.hparams.model_param.filters[2],
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use_norm=self.hparams.model_param.use_norm,
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use_bias=self.hparams.model_param.use_bias)
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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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self.enc_flat = Flatten(self.enc_conv_3b.shape)
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self.enc_lin_1 = nn.Linear(self.enc_flat.shape, self.feature_dim)
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#
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# Mixed Encoder
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self.mixed_lin = nn.Linear(self.feature_dim, self.feature_dim)
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self.mixed_norm = nn.BatchNorm1d(self.feature_dim) if self.hparams.model_param.use_norm else lambda x: x
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self.enc_lin_2 = nn.Linear(self.feature_dim, self.feature_dim)
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self.enc_norm = nn.BatchNorm1d(self.feature_dim) if self.hparams.model_param.use_norm else lambda x: x
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#
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# Variational Bottleneck
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self.mu = nn.Linear(self.feature_dim, self.hparams.model_param.lat_dim)
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self.logvar = nn.Linear(self.feature_dim, self.hparams.model_param.lat_dim)
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self.mu = nn.Linear(self.feature_dim, self.lat_dim)
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self.logvar = nn.Linear(self.feature_dim, self.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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# Todo Fix This Hack!!!!
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reshape_shape = (1, self.map_conv_3.shape[1], self.map_conv_3.shape[2])
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self.gen_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, reshape_shape))
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self.gen_lin_2 = nn.Linear(self.feature_dim, self.enc_flat.shape)
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self.reshape_to_map = Flatten(reduce(mul, reshape_shape), reshape_shape)
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self.reshape_to_last_conv = Flatten(self.enc_flat.shape, self.enc_conv_3b.shape)
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self.alt_deconv_1 = DeConvModule(reshape_shape, self.hparams.model_param.filters[2],
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conv_padding=0, conv_kernel=13, conv_stride=1,
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use_norm=self.hparams.model_param.use_norm)
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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=7, conv_stride=1,
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use_norm=self.hparams.model_param.use_norm)
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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=5, conv_stride=1,
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use_norm=self.hparams.model_param.use_norm)
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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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self.gen_deconv_1a = DeConvModule(self.enc_conv_3b.shape, self.hparams.model_param.filters[2],
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conv_padding=0, conv_kernel=11, conv_stride=1,
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use_norm=self.hparams.model_param.use_norm)
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self.gen_deconv_1b = DeConvModule(self.gen_deconv_1a.shape, self.hparams.model_param.filters[2],
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conv_padding=0, conv_kernel=9, conv_stride=2,
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use_norm=self.hparams.model_param.use_norm)
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self.gen_deconv_2a = DeConvModule(self.gen_deconv_1b.shape, self.hparams.model_param.filters[1],
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conv_padding=0, conv_kernel=7, conv_stride=1,
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use_norm=self.hparams.model_param.use_norm)
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self.gen_deconv_2b = DeConvModule(self.gen_deconv_2a.shape, self.hparams.model_param.filters[1],
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conv_padding=0, conv_kernel=7, conv_stride=1,
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use_norm=self.hparams.model_param.use_norm)
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self.gen_deconv_3a = DeConvModule(self.gen_deconv_2b.shape, self.hparams.model_param.filters[0],
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conv_padding=1, conv_kernel=5, conv_stride=1,
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use_norm=self.hparams.model_param.use_norm)
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self.gen_deconv_3b = DeConvModule(self.gen_deconv_3a.shape, self.hparams.model_param.filters[0],
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conv_padding=1, conv_kernel=4, conv_stride=1,
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use_norm=self.hparams.model_param.use_norm)
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self.gen_deconv_out = DeConvModule(self.gen_deconv_3b.shape, 1, activation=None,
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conv_padding=0, conv_kernel=3, conv_stride=1,
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use_norm=self.hparams.model_param.use_norm)
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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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z, mu, logvar = self.encode(batch_x)
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#
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# Generate
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@ -181,42 +212,26 @@ class CNNRouteGeneratorModel(LightningBaseModule):
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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.activation(alt_tensor)
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alt_tensor = self.alt_lin_2(alt_tensor)
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alt_tensor = self.activation(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.activation(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, batch_x):
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combined_tensor = self.enc_conv_0(batch_x)
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combined_tensor = self.enc_res_1(combined_tensor) if self.use_res_net else combined_tensor
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combined_tensor = self.enc_conv_1a(combined_tensor)
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combined_tensor = self.enc_conv_1b(combined_tensor)
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combined_tensor = self.enc_res_2(combined_tensor) if self.use_res_net else combined_tensor
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combined_tensor = self.enc_conv_2a(combined_tensor)
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combined_tensor = self.enc_conv_2b(combined_tensor)
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combined_tensor = self.enc_res_3(combined_tensor) if self.use_res_net else combined_tensor
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combined_tensor = self.enc_conv_3a(combined_tensor)
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combined_tensor = self.enc_conv_3b(combined_tensor)
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def encode(self, map_array, trajectory, label):
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label_array = torch.cat([torch.full((1, 1, self.in_shape[1], self.in_shape[2]), x.item())
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for x in label], dim=0)
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label_array = self._move_to_model_device(label_array)
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combined_tensor = torch.cat((map_array, trajectory, label_array), dim=1)
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combined_tensor = self.map_conv_0(combined_tensor)
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combined_tensor = self.map_res_1(combined_tensor)
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combined_tensor = self.map_conv_1(combined_tensor)
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combined_tensor = self.map_res_2(combined_tensor)
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combined_tensor = self.map_conv_2(combined_tensor)
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combined_tensor = self.map_res_3(combined_tensor)
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combined_tensor = self.map_conv_3(combined_tensor)
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combined_tensor = self.enc_flat(combined_tensor)
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combined_tensor = self.enc_lin_1(combined_tensor)
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combined_tensor = self.enc_lin_2(combined_tensor)
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combined_tensor = self.map_flat(combined_tensor)
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combined_tensor = self.map_lin(combined_tensor)
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combined_tensor = self.mixed_lin(combined_tensor)
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combined_tensor = self.mixed_norm(combined_tensor)
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combined_tensor = self.enc_norm(combined_tensor)
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combined_tensor = self.activation(combined_tensor)
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combined_tensor = self.mixed_lin(combined_tensor)
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combined_tensor = self.mixed_norm(combined_tensor)
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combined_tensor = self.enc_lin_2(combined_tensor)
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combined_tensor = self.enc_norm(combined_tensor)
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combined_tensor = self.activation(combined_tensor)
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#
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@ -226,19 +241,31 @@ class CNNRouteGeneratorModel(LightningBaseModule):
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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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def generate(self, z):
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alt_tensor = self.gen_lin_1(z)
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alt_tensor = self.activation(alt_tensor)
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alt_tensor = self.gen_lin_2(alt_tensor)
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alt_tensor = self.activation(alt_tensor)
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alt_tensor = self.reshape_to_last_conv(alt_tensor)
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alt_tensor = self.gen_deconv_1a(alt_tensor)
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alt_tensor = self.gen_deconv_1b(alt_tensor)
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alt_tensor = self.gen_deconv_2a(alt_tensor)
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alt_tensor = self.gen_deconv_2b(alt_tensor)
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alt_tensor = self.gen_deconv_3a(alt_tensor)
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alt_tensor = self.gen_deconv_3b(alt_tensor)
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alt_tensor = self.gen_deconv_out(alt_tensor)
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# alt_tensor = self.activation(alt_tensor)
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alt_tensor = self.sigmoid(alt_tensor)
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return alt_tensor
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trajectories = [x.get_random_trajectory() for x in maps]
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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] * 2
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trajectories = self._move_to_model_device(torch.stack(trajectories))
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def generate_random(self, n=12):
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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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samples, alternatives = zip(*[self.dataset.test_dataset[choice]
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for choice in choices(range(self.dataset.length), k=n)])
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samples = self._move_to_model_device(torch.stack([torch.as_tensor(x) for x in samples]))
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alternatives = self._move_to_model_device(torch.stack([torch.as_tensor(x) for x in alternatives]))
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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), None), -9999)
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return self._test_val_step((samples, alternatives), -9999)
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class CNNRouteGeneratorDiscriminated(CNNRouteGeneratorModel):
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@ -329,11 +356,12 @@ class CNNRouteGeneratorDiscriminated(CNNRouteGeneratorModel):
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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',
|
||||
self.dataset = TrajData(self.hparams.data_param.map_root, mode='just_route', preprocessed=True,
|
||||
length=self.hparams.data_param.dataset_length, normalized=True)
|
||||
|
||||
@ -0,0 +1,348 @@
|
||||
from random import choice
|
||||
|
||||
import torch
|
||||
from functools import reduce
|
||||
from operator import mul
|
||||
|
||||
from torch import nn
|
||||
from torch.optim import Adam
|
||||
|
||||
from datasets.trajectory_dataset import TrajData
|
||||
from lib.evaluation.classification import ROCEvaluation
|
||||
from lib.modules.blocks import ConvModule, ResidualModule, DeConvModule
|
||||
from lib.modules.utils import LightningBaseModule, Flatten
|
||||
|
||||
import matplotlib.pyplot as plt
|
||||
|
||||
|
||||
class CNNRouteGeneratorModel(LightningBaseModule):
|
||||
|
||||
name = 'CNNRouteGenerator'
|
||||
|
||||
def configure_optimizers(self):
|
||||
return Adam(self.parameters(), lr=self.hparams.train_param.lr)
|
||||
|
||||
def training_step(self, batch_xy, batch_nb, *args, **kwargs):
|
||||
batch_x, alternative = batch_xy
|
||||
generated_alternative, z, mu, logvar = self(batch_x)
|
||||
element_wise_loss = self.criterion(generated_alternative, alternative)
|
||||
# see Appendix B from VAE paper:
|
||||
# Kingma and Welling. Auto-Encoding Variational Bayes. ICLR, 2014
|
||||
# https://arxiv.org/abs/1312.6114
|
||||
# 0.5 * sum(1 + log(sigma^2) - mu^2 - sigma^2)
|
||||
|
||||
kld_loss = -0.5 * torch.sum(1 + logvar - mu.pow(2) - logvar.exp())
|
||||
# Dimensional Resizing TODO: Does This make sense? Sanity Check it!
|
||||
# kld_loss /= reduce(mul, self.in_shape)
|
||||
# kld_loss *= self.hparams.data_param.dataset_length / self.hparams.train_param.batch_size * 100
|
||||
|
||||
loss = (kld_loss + element_wise_loss) / 2
|
||||
return dict(loss=loss, log=dict(element_wise_loss=element_wise_loss, loss=loss, kld_loss=kld_loss))
|
||||
|
||||
def _test_val_step(self, batch_xy, batch_nb, *args):
|
||||
batch_x, alternative = batch_xy
|
||||
map_array = batch_x[0]
|
||||
trajectory = batch_x[1]
|
||||
label = batch_x[2].max()
|
||||
|
||||
z, _, _ = self.encode(batch_x)
|
||||
generated_alternative = self.generate(z)
|
||||
|
||||
return dict(map_array=map_array, trajectory=trajectory, batch_nb=batch_nb, label=label,
|
||||
generated_alternative=generated_alternative, pred_label=-1, alternative=alternative
|
||||
)
|
||||
|
||||
def _test_val_epoch_end(self, outputs, test=False):
|
||||
maps, trajectories, labels, val_restul_dict = self.generate_random()
|
||||
|
||||
from lib.visualization.generator_eval import GeneratorVisualizer
|
||||
g = GeneratorVisualizer(maps, trajectories, labels, val_restul_dict)
|
||||
fig = g.draw()
|
||||
self.logger.log_image(f'{self.name}_Output', fig, step=self.global_step)
|
||||
plt.clf()
|
||||
|
||||
return dict(epoch=self.current_epoch)
|
||||
|
||||
def validation_step(self, *args):
|
||||
return self._test_val_step(*args)
|
||||
|
||||
def validation_epoch_end(self, outputs: list):
|
||||
return self._test_val_epoch_end(outputs)
|
||||
|
||||
def test_step(self, *args):
|
||||
return self._test_val_step(*args)
|
||||
|
||||
def test_epoch_end(self, outputs):
|
||||
return self._test_val_epoch_end(outputs, test=True)
|
||||
|
||||
def __init__(self, *params, issubclassed=False):
|
||||
super(CNNRouteGeneratorModel, self).__init__(*params)
|
||||
|
||||
if not issubclassed:
|
||||
# Dataset
|
||||
self.dataset = TrajData(self.hparams.data_param.map_root, mode='generator_all_in_map',
|
||||
length=self.hparams.data_param.dataset_length, normalized=True)
|
||||
self.criterion = nn.MSELoss()
|
||||
|
||||
# Additional Attributes #
|
||||
#######################################################
|
||||
self.map_shape = self.dataset.map_shapes_max
|
||||
self.trajectory_features = 4
|
||||
self.res_net = self.hparams.model_param.use_res_net
|
||||
self.lat_dim = self.hparams.model_param.lat_dim
|
||||
self.feature_dim = self.lat_dim * 10
|
||||
########################################################
|
||||
|
||||
# NN Nodes
|
||||
###################################################
|
||||
#
|
||||
# Utils
|
||||
self.activation = nn.ReLU()
|
||||
self.sigmoid = nn.Sigmoid()
|
||||
|
||||
#
|
||||
# Map Encoder
|
||||
self.enc_conv_0 = ConvModule(self.map_shape, conv_kernel=3, conv_stride=1, conv_padding=1,
|
||||
conv_filters=self.hparams.model_param.filters[0],
|
||||
use_norm=self.hparams.model_param.use_norm,
|
||||
use_bias=self.hparams.model_param.use_bias)
|
||||
|
||||
self.enc_res_1 = ResidualModule(self.enc_conv_0.shape, ConvModule, 2, conv_kernel=5, conv_stride=1,
|
||||
conv_padding=2, conv_filters=self.hparams.model_param.filters[0],
|
||||
use_norm=self.hparams.model_param.use_norm,
|
||||
use_bias=self.hparams.model_param.use_bias)
|
||||
self.enc_conv_1a = ConvModule(self.enc_res_1.shape, conv_kernel=3, conv_stride=1, conv_padding=0,
|
||||
conv_filters=self.hparams.model_param.filters[1],
|
||||
use_norm=self.hparams.model_param.use_norm,
|
||||
use_bias=self.hparams.model_param.use_bias)
|
||||
self.enc_conv_1b = ConvModule(self.enc_conv_1a.shape, conv_kernel=3, conv_stride=2, conv_padding=0,
|
||||
conv_filters=self.hparams.model_param.filters[1],
|
||||
use_norm=self.hparams.model_param.use_norm,
|
||||
use_bias=self.hparams.model_param.use_bias)
|
||||
|
||||
self.enc_res_2 = ResidualModule(self.enc_conv_1b.shape, ConvModule, 2, conv_kernel=5, conv_stride=1,
|
||||
conv_padding=2, conv_filters=self.hparams.model_param.filters[1],
|
||||
use_norm=self.hparams.model_param.use_norm,
|
||||
use_bias=self.hparams.model_param.use_bias)
|
||||
self.enc_conv_2a = ConvModule(self.enc_res_2.shape, conv_kernel=5, conv_stride=1, conv_padding=0,
|
||||
conv_filters=self.hparams.model_param.filters[2],
|
||||
use_norm=self.hparams.model_param.use_norm,
|
||||
use_bias=self.hparams.model_param.use_bias)
|
||||
self.enc_conv_2b = ConvModule(self.enc_conv_2a.shape, conv_kernel=5, conv_stride=1, conv_padding=0,
|
||||
conv_filters=self.hparams.model_param.filters[2],
|
||||
use_norm=self.hparams.model_param.use_norm,
|
||||
use_bias=self.hparams.model_param.use_bias)
|
||||
|
||||
self.enc_res_3 = ResidualModule(self.enc_conv_2b.shape, ConvModule, 2, conv_kernel=7, conv_stride=1,
|
||||
conv_padding=3, conv_filters=self.hparams.model_param.filters[2],
|
||||
use_norm=self.hparams.model_param.use_norm,
|
||||
use_bias=self.hparams.model_param.use_bias)
|
||||
self.enc_conv_3a = ConvModule(self.enc_res_3.shape, conv_kernel=7, conv_stride=1, conv_padding=0,
|
||||
conv_filters=self.hparams.model_param.filters[2],
|
||||
use_norm=self.hparams.model_param.use_norm,
|
||||
use_bias=self.hparams.model_param.use_bias)
|
||||
self.enc_conv_3b = ConvModule(self.enc_conv_3a.shape, conv_kernel=7, conv_stride=1, conv_padding=0,
|
||||
conv_filters=self.hparams.model_param.filters[2],
|
||||
use_norm=self.hparams.model_param.use_norm,
|
||||
use_bias=self.hparams.model_param.use_bias)
|
||||
|
||||
# Trajectory Encoder
|
||||
self.env_gru_1 = nn.GRU(input_size=self.trajectory_features, hidden_size=self.feature_dim,
|
||||
num_layers=3, batch_first=True)
|
||||
|
||||
self.enc_flat = Flatten(self.enc_conv_3b.shape)
|
||||
self.enc_lin_1 = nn.Linear(self.enc_flat.shape, self.feature_dim)
|
||||
|
||||
#
|
||||
# Mixed Encoder
|
||||
self.enc_lin_2 = nn.Linear(self.feature_dim, self.feature_dim)
|
||||
self.enc_norm = nn.BatchNorm1d(self.feature_dim) if self.hparams.model_param.use_norm else lambda x: x
|
||||
|
||||
#
|
||||
# Variational Bottleneck
|
||||
self.mu = nn.Linear(self.feature_dim, self.lat_dim)
|
||||
self.logvar = nn.Linear(self.feature_dim, self.lat_dim)
|
||||
|
||||
#
|
||||
# Alternative Generator
|
||||
self.gen_lin_1 = nn.Linear(self.hparams.model_param.lat_dim, self.feature_dim)
|
||||
|
||||
self.gen_lin_2 = nn.Linear(self.feature_dim, self.enc_flat.shape)
|
||||
|
||||
self.gen_gru_x = nn.GRU(None, None, batch_first=True)
|
||||
|
||||
|
||||
|
||||
def forward(self, batch_x):
|
||||
#
|
||||
# Encode
|
||||
z, mu, logvar = self.encode(batch_x)
|
||||
|
||||
#
|
||||
# Generate
|
||||
alt_tensor = self.generate(z)
|
||||
return alt_tensor, z, mu, logvar
|
||||
|
||||
@staticmethod
|
||||
def reparameterize(mu, logvar):
|
||||
std = torch.exp(0.5 * logvar)
|
||||
eps = torch.randn_like(std)
|
||||
return mu + eps * std
|
||||
|
||||
def encode(self, batch_x):
|
||||
combined_tensor = self.enc_conv_0(batch_x)
|
||||
combined_tensor = self.enc_res_1(combined_tensor) if self.use_res_net else combined_tensor
|
||||
combined_tensor = self.enc_conv_1a(combined_tensor)
|
||||
combined_tensor = self.enc_conv_1b(combined_tensor)
|
||||
combined_tensor = self.enc_res_2(combined_tensor) if self.use_res_net else combined_tensor
|
||||
combined_tensor = self.enc_conv_2a(combined_tensor)
|
||||
combined_tensor = self.enc_conv_2b(combined_tensor)
|
||||
combined_tensor = self.enc_res_3(combined_tensor) if self.use_res_net else combined_tensor
|
||||
combined_tensor = self.enc_conv_3a(combined_tensor)
|
||||
combined_tensor = self.enc_conv_3b(combined_tensor)
|
||||
|
||||
combined_tensor = self.enc_flat(combined_tensor)
|
||||
combined_tensor = self.enc_lin_1(combined_tensor)
|
||||
combined_tensor = self.enc_lin_2(combined_tensor)
|
||||
|
||||
combined_tensor = self.enc_norm(combined_tensor)
|
||||
combined_tensor = self.activation(combined_tensor)
|
||||
combined_tensor = self.enc_lin_2(combined_tensor)
|
||||
combined_tensor = self.enc_norm(combined_tensor)
|
||||
combined_tensor = self.activation(combined_tensor)
|
||||
|
||||
#
|
||||
# Parameter and Sampling
|
||||
mu = self.mu(combined_tensor)
|
||||
logvar = self.logvar(combined_tensor)
|
||||
z = self.reparameterize(mu, logvar)
|
||||
return z, mu, logvar
|
||||
|
||||
def generate(self, z):
|
||||
alt_tensor = self.gen_lin_1(z)
|
||||
alt_tensor = self.activation(alt_tensor)
|
||||
alt_tensor = self.gen_lin_2(alt_tensor)
|
||||
alt_tensor = self.activation(alt_tensor)
|
||||
alt_tensor = self.reshape_to_last_conv(alt_tensor)
|
||||
alt_tensor = self.gen_deconv_1a(alt_tensor)
|
||||
alt_tensor = self.gen_deconv_1b(alt_tensor)
|
||||
alt_tensor = self.gen_deconv_2a(alt_tensor)
|
||||
alt_tensor = self.gen_deconv_2b(alt_tensor)
|
||||
alt_tensor = self.gen_deconv_3a(alt_tensor)
|
||||
alt_tensor = self.gen_deconv_3b(alt_tensor)
|
||||
alt_tensor = self.gen_deconv_out(alt_tensor)
|
||||
# alt_tensor = self.activation(alt_tensor)
|
||||
alt_tensor = self.sigmoid(alt_tensor)
|
||||
return alt_tensor
|
||||
|
||||
def generate_random(self, n=6):
|
||||
maps = [self.map_storage[choice(self.map_storage.keys_list)] for _ in range(n)]
|
||||
|
||||
trajectories = [x.get_random_trajectory() for x in maps]
|
||||
trajectories = [x.draw_in_array(self.map_storage.max_map_size) for x in trajectories]
|
||||
trajectories = [torch.as_tensor(x, dtype=torch.float32) for x in trajectories] * 2
|
||||
trajectories = self._move_to_model_device(torch.stack(trajectories))
|
||||
|
||||
maps = [torch.as_tensor(x.as_array, dtype=torch.float32) for x in maps] * 2
|
||||
maps = self._move_to_model_device(torch.stack(maps))
|
||||
|
||||
labels = self._move_to_model_device(torch.as_tensor([0] * n + [1] * n))
|
||||
return maps, trajectories, labels, self._test_val_step(((maps, trajectories, labels), None), -9999)
|
||||
|
||||
|
||||
class CNNRouteGeneratorDiscriminated(CNNRouteGeneratorModel):
|
||||
|
||||
name = 'CNNRouteGeneratorDiscriminated'
|
||||
|
||||
def training_step(self, batch_xy, batch_nb, *args, **kwargs):
|
||||
batch_x, label = batch_xy
|
||||
|
||||
generated_alternative, z, mu, logvar = self(batch_x)
|
||||
map_array, trajectory = batch_x
|
||||
|
||||
map_stack = torch.cat((map_array, trajectory, generated_alternative), dim=1)
|
||||
pred_label = self.discriminator(map_stack)
|
||||
discriminated_bce_loss = self.criterion(pred_label, label.float().unsqueeze(-1))
|
||||
|
||||
# see Appendix B from VAE paper:
|
||||
# Kingma and Welling. Auto-Encoding Variational Bayes. ICLR, 2014
|
||||
# https://arxiv.org/abs/1312.6114
|
||||
# 0.5 * sum(1 + log(sigma^2) - mu^2 - sigma^2)
|
||||
kld_loss = -0.5 * torch.sum(1 + logvar - mu.pow(2) - logvar.exp())
|
||||
# Dimensional Resizing
|
||||
kld_loss /= reduce(mul, self.in_shape)
|
||||
|
||||
loss = (kld_loss + discriminated_bce_loss) / 2
|
||||
return dict(loss=loss, log=dict(loss=loss,
|
||||
discriminated_bce_loss=discriminated_bce_loss,
|
||||
kld_loss=kld_loss)
|
||||
)
|
||||
|
||||
def _test_val_step(self, batch_xy, batch_nb, *args):
|
||||
batch_x, label = batch_xy
|
||||
|
||||
generated_alternative, z, mu, logvar = self(batch_x)
|
||||
map_array, trajectory = batch_x
|
||||
|
||||
map_stack = torch.cat((map_array, trajectory, generated_alternative), dim=1)
|
||||
pred_label = self.discriminator(map_stack)
|
||||
|
||||
discriminated_bce_loss = self.criterion(pred_label, label.float().unsqueeze(-1))
|
||||
return dict(discriminated_bce_loss=discriminated_bce_loss, batch_nb=batch_nb,
|
||||
pred_label=pred_label, label=label, generated_alternative=generated_alternative)
|
||||
|
||||
def validation_step(self, *args):
|
||||
return self._test_val_step(*args)
|
||||
|
||||
def validation_epoch_end(self, outputs: list):
|
||||
return self._test_val_epoch_end(outputs)
|
||||
|
||||
def _test_val_epoch_end(self, outputs, test=False):
|
||||
evaluation = ROCEvaluation(plot_roc=True)
|
||||
pred_label = torch.cat([x['pred_label'] for x in outputs])
|
||||
labels = torch.cat([x['label'] for x in outputs]).unsqueeze(1)
|
||||
mean_losses = torch.stack([x['discriminated_bce_loss'] for x in outputs]).mean()
|
||||
|
||||
# Sci-py call ROC eval call is eval(true_label, prediction)
|
||||
roc_auc, tpr, fpr = evaluation(labels.cpu().numpy(), pred_label.cpu().numpy(), )
|
||||
if test:
|
||||
# self.logger.log_metrics(score_dict)
|
||||
self.logger.log_image(f'{self.name}_ROC-Curve', plt.gcf(), step=self.global_step)
|
||||
plt.clf()
|
||||
|
||||
maps, trajectories, labels, val_restul_dict = self.generate_random()
|
||||
|
||||
from lib.visualization.generator_eval import GeneratorVisualizer
|
||||
g = GeneratorVisualizer(maps, trajectories, labels, val_restul_dict)
|
||||
fig = g.draw()
|
||||
self.logger.log_image(f'{self.name}_Output', fig, step=self.global_step)
|
||||
plt.clf()
|
||||
|
||||
return dict(mean_losses=mean_losses, roc_auc=roc_auc, epoch=self.current_epoch)
|
||||
|
||||
def test_step(self, *args):
|
||||
return self._test_val_step(*args)
|
||||
|
||||
def test_epoch_end(self, outputs):
|
||||
return self._test_val_epoch_end(outputs, test=True)
|
||||
|
||||
@property
|
||||
def discriminator(self):
|
||||
if self._disc is None:
|
||||
raise RuntimeError('Set the Discriminator first; "set_discriminator(disc_model)')
|
||||
return self._disc
|
||||
|
||||
def set_discriminator(self, disc_model):
|
||||
if self._disc is not None:
|
||||
raise RuntimeError('Discriminator has already been set... What are trying to do?')
|
||||
self._disc = disc_model
|
||||
|
||||
def __init__(self, *params):
|
||||
super(CNNRouteGeneratorDiscriminated, self).__init__(*params, issubclassed=True)
|
||||
|
||||
self._disc = None
|
||||
|
||||
self.criterion = nn.BCELoss()
|
||||
|
||||
self.dataset = TrajData(self.hparams.data_param.map_root, mode='just_route',
|
||||
length=self.hparams.data_param.dataset_length, normalized=True)
|
||||
|
||||
@ -60,7 +60,7 @@ class ConvHomDetector(LightningBaseModule):
|
||||
super(ConvHomDetector, self).__init__(hparams)
|
||||
|
||||
# Dataset
|
||||
self.dataset = TrajData(self.hparams.data_param.map_root, mode='all_in_map', )
|
||||
self.dataset = TrajData(self.hparams.data_param.map_root, mode='classifier_all_in_map', )
|
||||
|
||||
# Additional Attributes
|
||||
self.map_shape = self.dataset.map_shapes_max
|
||||
|
||||
@ -22,7 +22,7 @@ class Flatten(nn.Module):
|
||||
try:
|
||||
x = torch.randn(self.in_shape).unsqueeze(0)
|
||||
output = self(x)
|
||||
return output.shape[1:]
|
||||
return output.shape[1:] if len(output.shape[1:]) > 1 else output.shape[-1]
|
||||
except Exception as e:
|
||||
print(e)
|
||||
return -1
|
||||
|
||||
@ -1,10 +1,9 @@
|
||||
import shelve
|
||||
from collections import UserDict
|
||||
from pathlib import Path
|
||||
|
||||
import copy
|
||||
from math import sqrt
|
||||
from random import choice
|
||||
from random import Random
|
||||
|
||||
import numpy as np
|
||||
|
||||
@ -53,8 +52,12 @@ class Map(object):
|
||||
assert array_like_map_representation.ndim == 3
|
||||
self.map_array: np.ndarray = array_like_map_representation
|
||||
self.name = name
|
||||
self.prng = Random()
|
||||
pass
|
||||
|
||||
def seed(self, seed):
|
||||
self.prng.seed(seed)
|
||||
|
||||
def __setattr__(self, key, value):
|
||||
super(Map, self).__setattr__(key, value)
|
||||
if key == 'map_array' and self.map_array is not None:
|
||||
@ -102,7 +105,7 @@ class Map(object):
|
||||
return trajectory
|
||||
|
||||
def get_valid_position(self):
|
||||
valid_position = choice(list(self._G.nodes))
|
||||
valid_position = self.prng.choice(list(self._G.nodes))
|
||||
return valid_position
|
||||
|
||||
def get_trajectory_from_vertices(self, *args):
|
||||
|
||||
@ -20,6 +20,8 @@ class Generator:
|
||||
|
||||
self.data_root = Path(data_root)
|
||||
|
||||
|
||||
|
||||
def generate_n_trajectories_m_alternatives(self, n, m, datafile_name, processes=0, **kwargs):
|
||||
datafile_name = datafile_name if datafile_name.endswith('.pik') else f'{str(datafile_name)}.pik'
|
||||
kwargs.update(n=m)
|
||||
|
||||
22
lib/utils/tools.py
Normal file
22
lib/utils/tools.py
Normal file
@ -0,0 +1,22 @@
|
||||
import pickle
|
||||
import shelve
|
||||
from pathlib import Path
|
||||
|
||||
|
||||
def write_to_shelve(file_path, value):
|
||||
check_path(file_path)
|
||||
file_path.parent.mkdir(exist_ok=True, parents=True)
|
||||
with shelve.open(str(file_path), protocol=pickle.HIGHEST_PROTOCOL) as f:
|
||||
new_key = str(len(f))
|
||||
f[new_key] = value
|
||||
|
||||
|
||||
def load_from_shelve(file_path, key):
|
||||
check_path(file_path)
|
||||
with shelve.open(str(file_path)) as d:
|
||||
return d[key]
|
||||
|
||||
|
||||
def check_path(file_path):
|
||||
assert isinstance(file_path, Path)
|
||||
assert str(file_path).endswith('.pik')
|
||||
@ -5,12 +5,13 @@ import lib.variables as V
|
||||
|
||||
class GeneratorVisualizer(object):
|
||||
|
||||
def __init__(self, maps, trajectories, labels, val_result_dict):
|
||||
def __init__(self, **kwargs):
|
||||
# val_results = dict(discriminated_bce_loss, batch_nb, pred_label, label, generated_alternative)
|
||||
self.alternatives = val_result_dict['generated_alternative']
|
||||
self.labels = labels
|
||||
self.trajectories = trajectories
|
||||
self.maps = maps
|
||||
self.alternatives = kwargs.get('generated_alternative')
|
||||
self.labels = kwargs.get('labels')
|
||||
self.trajectories = kwargs.get('trajectories')
|
||||
self.maps = kwargs.get('maps')
|
||||
|
||||
self._map_width, self._map_height = self.maps[0].squeeze().shape
|
||||
self.column_dict_list = self._build_column_dict_list()
|
||||
self._cols = len(self.column_dict_list)
|
||||
@ -24,10 +25,13 @@ class GeneratorVisualizer(object):
|
||||
for idx in range(self.alternatives.shape[0]):
|
||||
image = (self.alternatives[idx]).cpu().numpy().squeeze()
|
||||
label = self.labels[idx].item()
|
||||
# Dirty and Quick hack incomming.
|
||||
if label == V.HOMOTOPIC:
|
||||
hom_alternatives.append(dict(image=image, label='Homotopic'))
|
||||
non_hom_alternatives.append(None)
|
||||
else:
|
||||
non_hom_alternatives.append(dict(image=image, label='NonHomotopic'))
|
||||
hom_alternatives.append(None)
|
||||
for idx in range(max(len(hom_alternatives), len(non_hom_alternatives))):
|
||||
image = (self.maps[idx] + self.trajectories[idx]).cpu().numpy().squeeze()
|
||||
label = 'original'
|
||||
@ -48,10 +52,13 @@ class GeneratorVisualizer(object):
|
||||
|
||||
for idx in range(len(grid.axes_all)):
|
||||
row, col = divmod(idx, len(self.column_dict_list))
|
||||
current_image = self.column_dict_list[col][row]['image']
|
||||
current_label = self.column_dict_list[col][row]['label']
|
||||
grid[idx].imshow(current_image)
|
||||
grid[idx].title.set_text(current_label)
|
||||
if self.column_dict_list[col][row] is not None:
|
||||
current_image = self.column_dict_list[col][row]['image']
|
||||
current_label = self.column_dict_list[col][row]['label']
|
||||
grid[idx].imshow(current_image)
|
||||
grid[idx].title.set_text(current_label)
|
||||
else:
|
||||
continue
|
||||
fig.cbar_mode = 'single'
|
||||
fig.tight_layout()
|
||||
return fig
|
||||
|
||||
Reference in New Issue
Block a user