fig clf inserted and not resize on kld
This commit is contained in:
Steffen Illium
@ -1,19 +1,22 @@
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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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from operator import mul
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from random import choices, choice
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import torch
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from torch import nn
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from torch.optim import Adam
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from torchvision.datasets import MNIST
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from datasets.mnist import MyMNIST
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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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import lib.variables as V
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from lib.visualization.generator_eval import GeneratorVisualizer
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class CNNRouteGeneratorModel(LightningBaseModule):
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@ -24,48 +27,71 @@ class CNNRouteGeneratorModel(LightningBaseModule):
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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, alternative = batch_xy
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batch_x, target = batch_xy
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generated_alternative, z, mu, logvar = self(batch_x)
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element_wise_loss = self.criterion(generated_alternative, alternative)
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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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target = batch_x if 'ae' in self.hparams.data_param.mode else target
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element_wise_loss = self.criterion(generated_alternative, target)
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kld_loss = -0.5 * torch.sum(1 + logvar - mu.pow(2) - logvar.exp())
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# Dimensional Resizing TODO: Does This make sense? Sanity Check it!
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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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if 'vae' in self.hparams.data_param.mode:
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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 TODO: Does This make sense? Sanity Check it!
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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
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loss = kld_loss + element_wise_loss
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loss = kld_loss + element_wise_loss
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else:
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loss = element_wise_loss
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kld_loss = 0
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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 = 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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if 'vae' in self.hparams.data_param.mode:
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z, mu, logvar = self.encode(batch_x)
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else:
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z = self.encode(batch_x)
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mu, logvar = z, z
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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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return_dict = dict(input=batch_x, batch_nb=batch_nb, output=generated_alternative, z=z, mu=mu, logvar=logvar)
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if 'hom' in self.hparams.data_param.mode:
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labels = torch.full((batch_x.shape[0], 1), V.HOMOTOPIC)
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elif 'alt' in self.hparams.data_param.mode:
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labels = torch.full((batch_x.shape[0], 1), V.ALTERNATIVE)
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elif 'vae' in self.hparams.data_param.mode:
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labels = torch.full((batch_x.shape[0], 1), V.ANY)
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elif 'ae' in self.hparams.data_param.mode:
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labels = torch.full((batch_x.shape[0], 1), V.ANY)
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else:
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labels = batch_x[:, 2].unsqueeze(1).max(dim=-1).values.max(-1).values
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return_dict.update(labels=self._move_to_model_device(labels))
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return return_dict
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def _test_val_epoch_end(self, outputs, test=False):
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val_restul_dict = self.generate_random()
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plt.close('all')
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from lib.visualization.generator_eval import GeneratorVisualizer
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g = GeneratorVisualizer(**val_restul_dict)
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fig = g.draw()
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g = GeneratorVisualizer(choice(outputs))
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fig = g.draw_io_bundle()
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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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fig = g.draw_latent()
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self.logger.log_image(f'{self.name}_Latent', 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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# 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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pass
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def validation_step(self, *args):
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return self._test_val_step(*args)
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@ -82,19 +108,23 @@ class CNNRouteGeneratorModel(LightningBaseModule):
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def __init__(self, *params, issubclassed=False):
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super(CNNRouteGeneratorModel, self).__init__(*params)
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if not issubclassed:
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if False:
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# Dataset
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self.dataset = TrajData(self.hparams.data_param.map_root, mode='generator_all_in_map',
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self.dataset = TrajData(self.hparams.data_param.map_root,
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mode=self.hparams.data_param.mode,
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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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self.criterion = nn.MSELoss()
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self.dataset = MyMNIST()
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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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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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self.feature_dim = self.lat_dim
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self.out_channels = 1 if 'generator' in self.hparams.data_param.mode else self.in_shape[0]
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########################################################
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# NN Nodes
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@ -119,7 +149,7 @@ class CNNRouteGeneratorModel(LightningBaseModule):
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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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self.enc_conv_1b = ConvModule(self.enc_conv_1a.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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@ -137,20 +167,8 @@ class CNNRouteGeneratorModel(LightningBaseModule):
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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_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.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.enc_flat = Flatten(self.enc_conv_3b.shape)
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last_conv_shape = self.enc_conv_2b.shape
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self.enc_flat = Flatten(last_conv_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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@ -160,46 +178,43 @@ class CNNRouteGeneratorModel(LightningBaseModule):
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#
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# Variational Bottleneck
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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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if 'vae' in self.hparams.data_param.mode:
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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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# Linear Bottleneck
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else:
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self.z = nn.Linear(self.feature_dim, self.lat_dim)
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#
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# Alternative Generator
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self.gen_lin_1 = nn.Linear(self.hparams.model_param.lat_dim, self.feature_dim)
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self.gen_lin_1 = nn.Linear(self.lat_dim, self.enc_flat.shape)
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self.gen_lin_2 = nn.Linear(self.feature_dim, self.enc_flat.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_last_conv = Flatten(self.enc_flat.shape, self.enc_conv_3b.shape)
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self.reshape_to_last_conv = Flatten(self.enc_flat.shape, last_conv_shape)
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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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self.gen_deconv_1a = DeConvModule(last_conv_shape, self.hparams.model_param.filters[2],
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conv_padding=1, conv_kernel=9, conv_stride=1,
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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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self.gen_deconv_2a = DeConvModule(self.gen_deconv_1a.shape, self.hparams.model_param.filters[1],
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conv_padding=1, 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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self.gen_deconv_out = DeConvModule(self.gen_deconv_2a.shape, self.out_channels, 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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# Encode
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z, mu, logvar = self.encode(batch_x)
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if 'vae' in self.hparams.data_param.mode:
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z, mu, logvar = self.encode(batch_x)
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else:
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z = self.encode(batch_x)
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mu, logvar = z, z
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#
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# Generate
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@ -220,148 +235,46 @@ class CNNRouteGeneratorModel(LightningBaseModule):
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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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# 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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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.enc_norm(combined_tensor)
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combined_tensor = self.activation(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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# Variational
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# Parameter and Sampling
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mu = self.mu(combined_tensor)
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logvar = self.logvar(combined_tensor)
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z = self.reparameterize(mu, logvar)
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return z, mu, logvar
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if 'vae' in self.hparams.data_param.mode:
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mu = self.mu(combined_tensor)
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logvar = self.logvar(combined_tensor)
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z = self.reparameterize(mu, logvar)
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return z, mu, logvar
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else:
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#
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# Linear Bottleneck
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z = self.z(combined_tensor)
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return z
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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.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_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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# alt_tensor = self.sigmoid(alt_tensor)
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return alt_tensor
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def generate_random(self, n=12):
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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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return self._test_val_step((samples, alternatives), -9999)
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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):
|
||||
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):
|
||||
raise NotImplementedError
|
||||
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', preprocessed=True,
|
||||
length=self.hparams.data_param.dataset_length, normalized=True)
|
||||
|
||||
116
lib/models/generators/cnn_discriminated.py
Normal file
116
lib/models/generators/cnn_discriminated.py
Normal file
@ -0,0 +1,116 @@
|
||||
from random import choices, seed
|
||||
import numpy as np
|
||||
|
||||
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.models.generators.cnn import CNNRouteGeneratorModel
|
||||
from lib.modules.blocks import ConvModule, ResidualModule, DeConvModule
|
||||
from lib.modules.utils import LightningBaseModule, Flatten
|
||||
|
||||
import matplotlib.pyplot as plt
|
||||
|
||||
|
||||
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):
|
||||
raise NotImplementedError
|
||||
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', preprocessed=True,
|
||||
length=self.hparams.data_param.dataset_length, normalized=True)
|
||||
@ -189,5 +189,5 @@ class MapStorage(UserDict):
|
||||
)
|
||||
|
||||
for map_file in map_files:
|
||||
current_map = Map().from_image(map_file, embedding_size=self.max_map_size)
|
||||
current_map = Map.from_image(map_file, embedding_size=self.max_map_size)
|
||||
self.__setitem__(map_file.name, current_map)
|
||||
|
||||
@ -5,7 +5,9 @@ from collections import defaultdict
|
||||
from configparser import ConfigParser
|
||||
from pathlib import Path
|
||||
|
||||
from lib.models.generators.cnn import CNNRouteGeneratorModel, CNNRouteGeneratorDiscriminated
|
||||
from lib.models.generators.cnn import CNNRouteGeneratorModel
|
||||
from lib.models.generators.cnn_discriminated import CNNRouteGeneratorDiscriminated
|
||||
|
||||
from lib.models.homotopy_classification.cnn_based import ConvHomDetector
|
||||
from lib.utils.model_io import ModelParameters
|
||||
from lib.utils.transforms import AsArray
|
||||
|
||||
@ -37,7 +37,7 @@ class Logger(LightningLoggerBase):
|
||||
@property
|
||||
def outpath(self):
|
||||
# ToDo: Add further path modification such as dataset config etc.
|
||||
return Path(self.config.train.outpath)
|
||||
return Path(self.config.train.outpath) / self.config.data.mode
|
||||
|
||||
def __init__(self, config: Config):
|
||||
"""
|
||||
|
||||
@ -9,6 +9,7 @@ def write_to_shelve(file_path, value):
|
||||
with shelve.open(str(file_path), protocol=pickle.HIGHEST_PROTOCOL) as f:
|
||||
new_key = str(len(f))
|
||||
f[new_key] = value
|
||||
f.close()
|
||||
|
||||
|
||||
def load_from_shelve(file_path, key):
|
||||
|
||||
@ -1,9 +1,15 @@
|
||||
from pathlib import Path
|
||||
_ROOT = Path('..')
|
||||
|
||||
# Labels for classes
|
||||
HOMOTOPIC = 1
|
||||
ALTERNATIVE = 0
|
||||
ANY = -1
|
||||
|
||||
# Colors for img files
|
||||
WHITE = 255
|
||||
BLACK = 0
|
||||
|
||||
DPI = 100
|
||||
# Variables for plotting
|
||||
PADDING = 0.25
|
||||
DPI = 50
|
||||
|
||||
@ -1,53 +1,106 @@
|
||||
from collections import defaultdict
|
||||
|
||||
import matplotlib.pyplot as plt
|
||||
import matplotlib.cm as cmaps
|
||||
from mpl_toolkits.axisartist.axes_grid import ImageGrid
|
||||
from sklearn.cluster import Birch, DBSCAN, KMeans
|
||||
from sklearn.decomposition import PCA
|
||||
from sklearn.manifold import TSNE
|
||||
|
||||
import lib.variables as V
|
||||
import numpy as np
|
||||
|
||||
|
||||
class GeneratorVisualizer(object):
|
||||
|
||||
def __init__(self, **kwargs):
|
||||
# val_results = dict(discriminated_bce_loss, batch_nb, pred_label, label, generated_alternative)
|
||||
self.alternatives = kwargs.get('generated_alternative')
|
||||
self.labels = kwargs.get('labels')
|
||||
self.trajectories = kwargs.get('trajectories')
|
||||
self.maps = kwargs.get('maps')
|
||||
def __init__(self, outputs, k=8):
|
||||
d = defaultdict(list)
|
||||
for key in outputs.keys():
|
||||
try:
|
||||
d[key] = outputs[key][:k].cpu().numpy()
|
||||
except AttributeError:
|
||||
d[key] = outputs[key][:k]
|
||||
except TypeError:
|
||||
self.batch_nb = outputs[key]
|
||||
for key in d.keys():
|
||||
self.__setattr__(key, d[key])
|
||||
|
||||
self._map_width, self._map_height = self.maps[0].squeeze().shape
|
||||
# val_results = dict(discriminated_bce_loss, batch_nb, pred_label, label, generated_alternative)
|
||||
self._map_width, self._map_height = self.input.shape[1], self.input.shape[2]
|
||||
self.column_dict_list = self._build_column_dict_list()
|
||||
self._cols = len(self.column_dict_list)
|
||||
self._rows = len(self.column_dict_list[0])
|
||||
|
||||
self.colormap = cmaps.tab20
|
||||
|
||||
def _build_column_dict_list(self):
|
||||
trajectories = []
|
||||
non_hom_alternatives = []
|
||||
hom_alternatives = []
|
||||
alternatives = []
|
||||
|
||||
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()
|
||||
for idx in range(self.output.shape[0]):
|
||||
image = (self.output[idx]).squeeze()
|
||||
label = 'Homotopic' if self.labels[idx].item() == V.HOMOTOPIC else 'Alternative'
|
||||
alternatives.append(dict(image=image, label=label))
|
||||
|
||||
for idx in range(len(alternatives)):
|
||||
image = (self.input[idx]).squeeze()
|
||||
label = 'original'
|
||||
trajectories.append(dict(image=image, label=label))
|
||||
|
||||
return trajectories, hom_alternatives, non_hom_alternatives
|
||||
return trajectories, alternatives
|
||||
|
||||
def draw(self):
|
||||
padding = 0.25
|
||||
additional_size = self._cols * padding + 3 * padding
|
||||
width = (self._map_width * self._cols) / V.DPI + additional_size
|
||||
height = (self._map_height * self._rows) / V.DPI + additional_size
|
||||
@staticmethod
|
||||
def cluster_data(data):
|
||||
|
||||
cluster = Birch()
|
||||
|
||||
labels = cluster.fit_predict(data)
|
||||
return labels
|
||||
|
||||
def draw_latent(self):
|
||||
plt.close('all')
|
||||
clusterer = KMeans(10)
|
||||
try:
|
||||
labels = clusterer.fit_predict(self.logvar)
|
||||
except ValueError:
|
||||
fig = plt.figure()
|
||||
return fig
|
||||
if self.z.shape[-1] > 2:
|
||||
fig, axs = plt.subplots(ncols=2, nrows=1)
|
||||
transformers = [TSNE(2), PCA(2)]
|
||||
for idx, transformer in enumerate(transformers):
|
||||
transformed = transformer.fit_transform(self.z)
|
||||
|
||||
colored = self.colormap(labels)
|
||||
ax = axs[idx]
|
||||
ax.scatter(x=transformed[:, 0], y=transformed[:, 1], c=colored)
|
||||
ax.set_title(transformer.__class__.__name__)
|
||||
ax.set_xlim(np.min(transformed[:, 0])*1.1, np.max(transformed[:, 0]*1.1))
|
||||
ax.set_ylim(np.min(transformed[:, 1]*1.1), np.max(transformed[:, 1]*1.1))
|
||||
elif self.z.shape[-1] == 2:
|
||||
fig, axs = plt.subplots()
|
||||
|
||||
# TODO: Build transformation for lat_dim_size >= 3
|
||||
print('All Predictions sucesfully Gathered and Shaped ')
|
||||
axs.set_xlim(np.min(self.z[:, 0]), np.max(self.z[:, 0]))
|
||||
axs.set_ylim(np.min(self.z[:, 1]), np.max(self.z[:, 1]))
|
||||
# ToDo: Insert Normalization
|
||||
colored = self.colormap(labels)
|
||||
plt.scatter(self.z[:, 0], self.z[:, 1], c=colored)
|
||||
else:
|
||||
raise NotImplementedError("Latent Dimensions can not be one-dimensional (yet).")
|
||||
|
||||
return fig
|
||||
|
||||
def draw_io_bundle(self):
|
||||
width, height = self._cols * 5, self._rows * 5
|
||||
additional_size = self._cols * V.PADDING + 3 * V.PADDING
|
||||
# width = (self._map_width * self._cols) / V.DPI + additional_size
|
||||
# height = (self._map_height * self._rows) / V.DPI + additional_size
|
||||
fig = plt.figure(figsize=(width, height), dpi=V.DPI)
|
||||
grid = ImageGrid(fig, 111, # similar to subplot(111)
|
||||
nrows_ncols=(self._rows, self._cols),
|
||||
axes_pad=padding, # pad between axes in inch.
|
||||
axes_pad=V.PADDING, # pad between axes in inch.
|
||||
)
|
||||
|
||||
for idx in range(len(grid.axes_all)):
|
||||
|
||||
Reference in New Issue
Block a user