project Refactor, CNN Classifier Basics
This commit is contained in:
Steffen Illium
@ -1,32 +1,34 @@
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import matplotlib.pyplot as plt
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from sklearn.metrics import roc_curve, auc
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class ROCEvaluation(object):
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linewidth = 2
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def __init__(self, prepare_figure=False):
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self.prepare_figure = prepare_figure
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self.epoch = 0
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def __call__(self, prediction, label, plotting=False):
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# Compute ROC curve and ROC area
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fpr, tpr, _ = roc_curve(prediction, label)
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roc_auc = auc(fpr, tpr)
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if plotting:
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fig = plt.gcf()
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fig.plot(fpr, tpr, color='darkorange', lw=self.linewidth, label=f'ROC curve (area = {roc_auc})')
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return roc_auc, fpr, tpr
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def _prepare_fig(self):
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fig = plt.gcf()
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fig.plot([0, 1], [0, 1], color='navy', lw=self.linewidth, linestyle='--')
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fig.xlim([0.0, 1.0])
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fig.ylim([0.0, 1.05])
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fig.xlabel('False Positive Rate')
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fig.ylabel('True Positive Rate')
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fig.legend(loc="lower right")
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return fig
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import matplotlib.pyplot as plt
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from sklearn.metrics import roc_curve, auc
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class ROCEvaluation(object):
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linewidth = 2
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def __init__(self, plot_roc=False):
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self.plot_roc = plot_roc
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self.epoch = 0
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def __call__(self, prediction, label):
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# Compute ROC curve and ROC area
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fpr, tpr, _ = roc_curve(prediction, label)
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roc_auc = auc(fpr, tpr)
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if self.plot_roc:
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_ = plt.gcf()
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plt.plot(fpr, tpr, color='darkorange', lw=self.linewidth, label=f'ROC curve (area = {roc_auc})')
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self._prepare_fig()
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return roc_auc, fpr, tpr
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def _prepare_fig(self):
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fig = plt.gcf()
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ax = plt.gca()
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plt.plot([0, 1], [0, 1], color='navy', lw=self.linewidth, linestyle='--')
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plt.xlim([0.0, 1.0])
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plt.ylim([0.0, 1.05])
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plt.xlabel('False Positive Rate')
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plt.ylabel('True Positive Rate')
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fig.legend(loc="lower right")
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return fig
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@ -1,6 +1,13 @@
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from datasets.paired_dataset import TrajPairData
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from lib.modules.blocks import ConvModule
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from lib.modules.utils import LightningBaseModule
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import torch
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from functools import reduce
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from operator import mul
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from torch import nn
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from torch.optim import Adam
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from datasets.trajectory_dataset import TrajData
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from lib.modules.blocks import ConvModule, ResidualModule, DeConvModule
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from lib.modules.utils import LightningBaseModule, Flatten
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class CNNRouteGeneratorModel(LightningBaseModule):
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@ -8,36 +15,169 @@ class CNNRouteGeneratorModel(LightningBaseModule):
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name = 'CNNRouteGenerator'
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def configure_optimizers(self):
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pass
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def validation_step(self, *args, **kwargs):
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pass
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def validation_end(self, outputs):
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pass
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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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pass
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batch_x, label = batch_xy
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generated_alternative, z, mu, logvar = self(batch_x + [label, ])
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map_array, trajectory = batch_x
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map_stack = torch.cat((map_array, trajectory, generated_alternative), dim=1)
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pred_label = self.discriminator(map_stack)
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discriminated_bce_loss = self.criterion(pred_label, label.float().unsqueeze(-1))
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# see Appendix B from VAE paper:
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# Kingma and Welling. Auto-Encoding Variational Bayes. ICLR, 2014
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# https://arxiv.org/abs/1312.6114
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# 0.5 * sum(1 + log(sigma^2) - mu^2 - sigma^2)
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kld_loss = -0.5 * torch.sum(1 + logvar - mu.pow(2) - logvar.exp())
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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_step(self, *args, **kwargs):
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pass
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@property
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def discriminator(self):
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if self._disc is None:
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raise RuntimeError('Set the Discriminator first; "set_discriminator(disc_model)')
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return self._disc
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def set_discriminator(self, disc_model):
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if self._disc is not None:
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raise RuntimeError('Discriminator has already been set... What are trying to do?')
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self._disc = disc_model
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def __init__(self, *params):
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super(CNNRouteGeneratorModel, self).__init__(*params)
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# Dataset
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self.dataset = TrajPairData(self.hparams.data_param.data_root)
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self.dataset = TrajData(self.hparams.data_param.map_root, mode='just_route')
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# Additional Attributes
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self.in_shape = self.dataset.map_shapes_max
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# Todo: Better naming and size in Parameters
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self.feature_dim = 10
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self._disc = None
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# NN Nodes
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###################################################
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#
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# Utils
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self.relu = nn.ReLU()
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self.criterion = nn.MSELoss()
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#
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# Map Encoder
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self.map_conv_0 = ConvModule(self.in_shape, conv_kernel=3, conv_stride=1, conv_padding=1,
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conv_filters=self.hparams.model_param.filters[0])
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self.conv2 = ConvModule(self.conv1.shape, conv_kernel=3, conv_stride=1, conv_padding=0,
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conv_filters=self.hparams.model_param.filters[0])
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self.conv3 = ConvModule(self.conv2.shape, conv_kernel=3, conv_stride=1, conv_padding=0,
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conv_filters=self.hparams.model_param.filters[0])
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self.map_res_1 = ResidualModule(self.map_conv_0.shape, ConvModule, 2, conv_kernel=3, conv_stride=1,
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conv_padding=1, conv_filters=self.hparams.model_param.filters[0])
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self.map_conv_1 = ConvModule(self.map_res_1.shape, conv_kernel=3, conv_stride=1, conv_padding=0,
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conv_filters=self.hparams.model_param.filters[1])
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def forward(self, x):
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pass
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self.map_res_2 = ResidualModule(self.map_conv_1.shape, ConvModule, 2, conv_kernel=3, conv_stride=1,
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conv_padding=1, conv_filters=self.hparams.model_param.filters[1])
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self.map_conv_2 = ConvModule(self.map_res_2.shape, conv_kernel=3, conv_stride=1, conv_padding=0,
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conv_filters=self.hparams.model_param.filters[2])
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self.map_res_3 = ResidualModule(self.map_conv_2.shape, ConvModule, 2, conv_kernel=3, conv_stride=1,
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conv_padding=1, conv_filters=self.hparams.model_param.filters[2])
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self.map_conv_3 = ConvModule(self.map_res_3.shape, conv_kernel=5, conv_stride=1, conv_padding=0,
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conv_filters=self.hparams.model_param.filters[2]*2)
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self.map_flat = Flatten(self.map_conv_3.shape)
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self.map_lin = nn.Linear(reduce(mul, self.map_conv_3.shape), self.feature_dim)
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#
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# Trajectory Encoder
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self.traj_conv_1 = ConvModule(self.in_shape, conv_kernel=3, conv_stride=1, conv_padding=0,
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conv_filters=self.hparams.model_param.filters[0])
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self.traj_conv_2 = ConvModule(self.traj_conv_1.shape, conv_kernel=3, conv_stride=1, conv_padding=0,
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conv_filters=self.hparams.model_param.filters[0])
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self.traj_conv_3 = ConvModule(self.traj_conv_2.shape, conv_kernel=3, conv_stride=1, conv_padding=0,
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conv_filters=self.hparams.model_param.filters[0])
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self.traj_flat = Flatten(self.traj_conv_3.shape)
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self.traj_lin = nn.Linear(reduce(mul, self.traj_conv_3.shape), self.feature_dim)
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#
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# Variational Bottleneck
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self.mu = nn.Linear(self.feature_dim + self.feature_dim + 1, self.hparams.model_param.lat_dim)
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self.logvar = nn.Linear(self.feature_dim + self.feature_dim + 1, self.hparams.model_param.lat_dim)
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#
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# Alternative Generator
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self.alt_lin_1 = nn.Linear(self.hparams.model_param.lat_dim, self.feature_dim)
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self.alt_lin_2 = nn.Linear(self.feature_dim, reduce(mul, self.traj_conv_3.shape))
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self.reshape_to_map = Flatten(reduce(mul, self.traj_conv_3.shape), self.traj_conv_3.shape)
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self.alt_deconv_1 = DeConvModule(self.traj_conv_3.shape, self.hparams.model_param.filters[2],
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conv_padding=0, conv_kernel=5, conv_stride=1)
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self.alt_deconv_2 = DeConvModule(self.alt_deconv_1.shape, self.hparams.model_param.filters[1],
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conv_padding=0, conv_kernel=3, conv_stride=1)
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self.alt_deconv_3 = DeConvModule(self.alt_deconv_2.shape, self.hparams.model_param.filters[0],
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conv_padding=1, conv_kernel=3, conv_stride=1)
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self.alt_deconv_out = DeConvModule(self.alt_deconv_3.shape, 1, activation=None,
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conv_padding=1, conv_kernel=3, conv_stride=1)
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def forward(self, batch_x):
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#
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# Sorting the Input
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map_array, trajectory, label = batch_x
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#
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# Encode
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map_tensor = self.map_conv_0(map_array)
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map_tensor = self.map_res_1(map_tensor)
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map_tensor = self.map_conv_1(map_tensor)
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map_tensor = self.map_res_2(map_tensor)
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map_tensor = self.map_conv_2(map_tensor)
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map_tensor = self.map_res_3(map_tensor)
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map_tensor = self.map_conv_3(map_tensor)
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map_tensor = self.map_flat(map_tensor)
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map_tensor = self.map_lin(map_tensor)
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traj_tensor = self.traj_conv_1(trajectory)
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traj_tensor = self.traj_conv_2(traj_tensor)
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traj_tensor = self.traj_conv_3(traj_tensor)
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traj_tensor = self.traj_flat(traj_tensor)
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traj_tensor = self.traj_lin(traj_tensor)
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mixed_tensor = torch.cat((map_tensor, traj_tensor, label.float().unsqueeze(-1)), dim=1)
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mixed_tensor = self.relu(mixed_tensor)
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#
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# Parameter and Sampling
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mu = self.mu(mixed_tensor)
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logvar = self.logvar(mixed_tensor)
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z = self.reparameterize(mu, logvar)
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#
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# Generate
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alt_tensor = self.alt_lin_1(z)
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alt_tensor = self.alt_lin_2(alt_tensor)
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alt_tensor = self.reshape_to_map(alt_tensor)
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alt_tensor = self.alt_deconv_1(alt_tensor)
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alt_tensor = self.alt_deconv_2(alt_tensor)
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alt_tensor = self.alt_deconv_3(alt_tensor)
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alt_tensor = self.alt_deconv_out(alt_tensor)
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return alt_tensor, z, mu, logvar
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@staticmethod
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def reparameterize(mu, logvar):
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std = torch.exp(0.5 * logvar)
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eps = torch.randn_like(std)
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return mu + eps * std
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@ -1,14 +1,16 @@
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from lib.modules.blocks import LightningBaseModule
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from lib.modules.losses import BinaryHomotopicLoss
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from lib.modules.utils import LightningBaseModule
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from lib.objects.map import Map
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from lib.objects.trajectory import Trajectory
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import torch.nn as nn
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nn.MSELoss
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class LinearRouteGeneratorModel(LightningBaseModule):
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def test_epoch_end(self, outputs):
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pass
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name = 'LinearRouteGenerator'
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def configure_optimizers(self):
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@ -33,6 +35,12 @@ class LinearRouteGeneratorModel(LightningBaseModule):
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pred_y = self(map_x, traj_x, label_x)
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loss = self.loss(traj_x, pred_y)
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def training_step(self, batch_xy, batch_nb, *args, **kwargs):
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batch_x, batch_y = batch_xy
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pred_y = self(batch_x)
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loss = self.criterion(pred_y, batch_y.unsqueeze(-1).float())
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return dict(loss=loss, log=dict(loss=loss))
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def test_step(self, *args, **kwargs):
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@ -41,7 +49,7 @@ class LinearRouteGeneratorModel(LightningBaseModule):
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def __init__(self, *params):
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super(LinearRouteGeneratorModel, self).__init__(*params)
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self.loss = BinaryHomotopicLoss(self.map_storage)
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self.criterion = BinaryHomotopicLoss(self.map_storage)
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def forward(self, map_x, traj_x, label_x):
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pass
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@ -24,41 +24,44 @@ class ConvHomDetector(LightningBaseModule):
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def training_step(self, batch_xy, batch_nb, *args, **kwargs):
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batch_x, batch_y = batch_xy
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pred_y = self(batch_x)
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loss = F.binary_cross_entropy(pred_y, batch_y.float())
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loss = self.criterion(pred_y, batch_y.unsqueeze(-1).float())
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return {'loss': loss, 'log': dict(loss=loss)}
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def test_step(self, batch_xy, **kwargs):
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def test_step(self, batch_xy, batch_nb, **kwargs):
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batch_x, batch_y = batch_xy
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pred_y = self(batch_x)
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return dict(prediction=pred_y, label=batch_y)
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return dict(prediction=pred_y, label=batch_y, batch_nb=batch_nb)
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def test_end(self, outputs):
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evaluation = ROCEvaluation()
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predictions = torch.stack([x['prediction'] for x in outputs])
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labels = torch.stack([x['label'] for x in outputs])
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def test_epoch_end(self, outputs):
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evaluation = ROCEvaluation(plot_roc=True)
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predictions = torch.cat([x['prediction'] for x in outputs])
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labels = torch.cat([x['label'] for x in outputs]).unsqueeze(1)
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scores = evaluation(predictions.numpy(), labels.numpy(), )
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self.logger.log_metrics({key:value for key, value in zip(['roc_auc', 'tpr', 'fpr'], scores)})
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# Sci-py call ROC eval call is eval(true_label, prediction)
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roc_auc, tpr, fpr = evaluation(labels.cpu().numpy(), predictions.cpu().numpy(), )
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score_dict = dict(roc_auc=roc_auc)
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# self.logger.log_metrics(score_dict)
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self.logger.log_image(f'{self.name}', plt.gcf())
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pass
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def __init__(self, *params):
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super(ConvHomDetector, self).__init__(*params)
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return dict(log=score_dict)
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def __init__(self, hparams):
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super(ConvHomDetector, self).__init__(hparams)
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# Dataset
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self.dataset = TrajData(self.hparams.data_param.root)
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self.dataset = TrajData(self.hparams.data_param.map_root, mode='all_in_map')
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# Additional Attributes
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self.map_shape = self.dataset.map_shapes_max
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# Model Paramters
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# Model Parameters
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self.in_shape = self.dataset.map_shapes_max
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assert len(self.in_shape) == 3, f'Image or map shape has to have 3 dims, but had: {len(self.in_shape)}'
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self.criterion = nn.BCEWithLogitsLoss()
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# NN Nodes
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# ============================
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# Convolutional Map Processing
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#
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self.map_conv_0 = ConvModule(self.in_shape, conv_kernel=3, conv_stride=1,
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conv_padding=0, conv_filters=self.hparams.model_param.filters[0])
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self.map_res_1 = ResidualModule(self.map_conv_0.shape, ConvModule, 3,
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@ -86,7 +89,6 @@ class ConvHomDetector(LightningBaseModule):
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self.linear = nn.Linear(reduce(mul, self.flatten.shape), self.hparams.model_param.classes * 10)
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# Comments on Multi Class labels
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self.classifier = nn.Linear(self.hparams.model_param.classes * 10, 1) # self.hparams.model_param.classes)
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self.out_activation = nn.Sigmoid() # nn.Softmax
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def forward(self, x):
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tensor = self.map_conv_0(x)
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@ -98,25 +100,4 @@ class ConvHomDetector(LightningBaseModule):
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tensor = self.flatten(tensor)
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tensor = self.linear(tensor)
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tensor = self.classifier(tensor)
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tensor = self.out_activation(tensor)
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return tensor
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# Dataloaders
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# ================================================================================
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# Train Dataloader
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def train_dataloader(self):
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return DataLoader(dataset=self.dataset.train_dataset, shuffle=True,
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batch_size=self.hparams.data_param.batchsize,
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num_workers=self.hparams.data_param.worker)
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# Test Dataloader
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def test_dataloader(self):
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return DataLoader(dataset=self.dataset.test_dataset, shuffle=True,
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batch_size=self.hparams.data_param.batchsize,
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num_workers=self.hparams.data_param.worker)
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# Validation Dataloader
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def val_dataloader(self):
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return DataLoader(dataset=self.dataset.val_dataset, shuffle=True,
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batch_size=self.hparams.data_param.batchsize,
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num_workers=self.hparams.data_param.worker)
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@ -1,11 +1,7 @@
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from abc import ABC
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from pathlib import Path
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from typing import Union
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import torch
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from torch import nn
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import torch.nn.functional as F
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import pytorch_lightning as pl
|
||||
from lib.modules.utils import AutoPad, Interpolate
|
||||
|
||||
#
|
||||
@ -26,12 +22,12 @@ class ConvModule(nn.Module):
|
||||
conv_filters=64, conv_kernel=5, conv_stride=1, conv_padding=0):
|
||||
super(ConvModule, self).__init__()
|
||||
|
||||
# Module Paramters
|
||||
# Module Parameters
|
||||
self.in_shape = in_shape
|
||||
in_channels, height, width = in_shape[0], in_shape[1], in_shape[2]
|
||||
self.activation = activation()
|
||||
|
||||
# Convolution Paramters
|
||||
# Convolution Parameters
|
||||
self.padding = conv_padding
|
||||
self.stride = conv_stride
|
||||
|
||||
@ -44,7 +40,7 @@ class ConvModule(nn.Module):
|
||||
)
|
||||
|
||||
def forward(self, x):
|
||||
x = self.norm(x) if self.norm else x
|
||||
x = self.norm(x)
|
||||
|
||||
tensor = self.conv(x)
|
||||
tensor = self.dropout(tensor)
|
||||
@ -72,10 +68,10 @@ class DeConvModule(nn.Module):
|
||||
self.in_shape = in_shape
|
||||
self.conv_filters = conv_filters
|
||||
|
||||
self.autopad = AutoPad() if autopad else lambda x: x
|
||||
self.interpolation = Interpolate(scale_factor=interpolation_scale) if interpolation_scale else lambda x: x
|
||||
self.norm = nn.BatchNorm2d(in_channels, eps=1e-04, affine=False) if normalize else lambda x: x
|
||||
self.dropout = nn.Dropout2d(dropout) if dropout else lambda x: x
|
||||
self.autopad = AutoPad() if autopad else lambda x: x
|
||||
self.interpolation = Interpolate(scale_factor=interpolation_scale) if interpolation_scale else lambda x: x
|
||||
self.norm = nn.BatchNorm2d(in_channels, eps=1e-04, affine=False) if normalize else lambda x: x
|
||||
self.dropout = nn.Dropout2d(dropout) if dropout else lambda x: x
|
||||
self.de_conv = nn.ConvTranspose2d(in_channels, self.conv_filters, conv_kernel, bias=use_bias,
|
||||
padding=self.padding, stride=self.stride)
|
||||
|
||||
@ -100,13 +96,13 @@ class ResidualModule(nn.Module):
|
||||
output = self(x)
|
||||
return output.shape[1:]
|
||||
|
||||
def __init__(self, in_shape, module_class, n, activation=None, **module_paramters):
|
||||
def __init__(self, in_shape, module_class, n, activation=None, **module_parameters):
|
||||
assert n >= 1
|
||||
super(ResidualModule, self).__init__()
|
||||
self.in_shape = in_shape
|
||||
module_paramters.update(in_shape=in_shape)
|
||||
module_parameters.update(in_shape=in_shape)
|
||||
self.activation = activation() if activation else lambda x: x
|
||||
self.residual_block = nn.ModuleList([module_class(**module_paramters) for _ in range(n)])
|
||||
self.residual_block = nn.ModuleList([module_class(**module_parameters) for _ in range(n)])
|
||||
assert self.in_shape == self.shape, f'The in_shape: {self.in_shape} - must match the out_shape: {self.shape}.'
|
||||
|
||||
def forward(self, x):
|
||||
@ -143,5 +139,3 @@ class RecurrentModule(nn.Module):
|
||||
def forward(self, x):
|
||||
tensor = self.rnn(x)
|
||||
return tensor
|
||||
|
||||
|
||||
|
||||
@ -1,8 +1,11 @@
|
||||
from typing import List
|
||||
|
||||
import torch
|
||||
from torch import nn
|
||||
|
||||
from lib.modules.utils import FlipTensor
|
||||
from lib.objects.map import MapStorage
|
||||
from lib.objects.map import MapStorage, Map
|
||||
from lib.objects.trajectory import Trajectory
|
||||
|
||||
|
||||
class BinaryHomotopicLoss(nn.Module):
|
||||
@ -11,7 +14,10 @@ class BinaryHomotopicLoss(nn.Module):
|
||||
self.map_storage = map_storage
|
||||
self.flipper = FlipTensor()
|
||||
|
||||
def forward(self, x:torch.Tensor, y: torch.Tensor, mapnames: str):
|
||||
y_flipepd = self.flipper(y)
|
||||
circle = torch.cat((x, y_flipepd), dim=-1)
|
||||
masp = self.map_storage[mapnames].are
|
||||
def forward(self, x: torch.Tensor, y: torch.Tensor, mapnames: str):
|
||||
maps: List[Map] = [self.map_storage[mapname] for mapname in mapnames]
|
||||
for basemap in maps:
|
||||
basemap = basemap.as_2d_array
|
||||
|
||||
|
||||
|
||||
|
||||
@ -83,9 +83,9 @@ class LightningBaseModule(pl.LightningModule, ABC):
|
||||
print(e)
|
||||
return -1
|
||||
|
||||
def __init__(self, params):
|
||||
def __init__(self, hparams):
|
||||
super(LightningBaseModule, self).__init__()
|
||||
self.hparams = params
|
||||
self.hparams = hparams
|
||||
|
||||
# Data loading
|
||||
# =============================================================================
|
||||
@ -109,6 +109,10 @@ class LightningBaseModule(pl.LightningModule, ABC):
|
||||
def data_len(self):
|
||||
return len(self.dataset.train_dataset)
|
||||
|
||||
@property
|
||||
def n_train_batches(self):
|
||||
return len(self.train_dataloader())
|
||||
|
||||
def configure_optimizers(self):
|
||||
raise NotImplementedError
|
||||
|
||||
@ -121,7 +125,7 @@ class LightningBaseModule(pl.LightningModule, ABC):
|
||||
def test_step(self, *args, **kwargs):
|
||||
raise NotImplementedError
|
||||
|
||||
def test_end(self, outputs):
|
||||
def test_epoch_end(self, outputs):
|
||||
raise NotImplementedError
|
||||
|
||||
def init_weights(self):
|
||||
@ -134,6 +138,26 @@ class LightningBaseModule(pl.LightningModule, ABC):
|
||||
m.bias.data.fill_(0.01)
|
||||
self.apply(_weight_init)
|
||||
|
||||
# Dataloaders
|
||||
# ================================================================================
|
||||
# Train Dataloader
|
||||
def train_dataloader(self):
|
||||
return DataLoader(dataset=self.dataset.train_dataset, shuffle=True,
|
||||
batch_size=self.hparams.data_param.batchsize,
|
||||
num_workers=self.hparams.data_param.worker)
|
||||
|
||||
# Test Dataloader
|
||||
def test_dataloader(self):
|
||||
return DataLoader(dataset=self.dataset.test_dataset, shuffle=True,
|
||||
batch_size=self.hparams.data_param.batchsize,
|
||||
num_workers=self.hparams.data_param.worker)
|
||||
|
||||
# Validation Dataloader
|
||||
def val_dataloader(self):
|
||||
return DataLoader(dataset=self.dataset.val_dataset, shuffle=False,
|
||||
batch_size=self.hparams.data_param.batchsize,
|
||||
num_workers=self.hparams.data_param.worker)
|
||||
|
||||
|
||||
class FilterLayer(nn.Module):
|
||||
|
||||
|
||||
@ -12,6 +12,7 @@ import networkx as nx
|
||||
from matplotlib import pyplot as plt
|
||||
|
||||
from lib.objects.trajectory import Trajectory
|
||||
import lib.variables as V
|
||||
|
||||
|
||||
class Map(object):
|
||||
@ -145,14 +146,14 @@ class Map(object):
|
||||
|
||||
img = Image.new('L', (self.height, self.width), 0)
|
||||
draw = ImageDraw.Draw(img)
|
||||
draw.polygon(polyline, outline=1, fill=1)
|
||||
draw.polygon(polyline, outline=self.white, fill=self.white)
|
||||
|
||||
a = (np.where(np.asarray(img) == self.white, 1, 0) * np.where(self.as_2d_array == self.white, 1, 0)).sum()
|
||||
a = (np.where(np.asarray(img) == self.white, 1, 0) * np.where(self.as_2d_array == self.black, 1, 0)).sum()
|
||||
|
||||
if a:
|
||||
return False # Non-Homotoph
|
||||
return V.ALTERNATIVE # Non-Homotoph
|
||||
else:
|
||||
return True # Homotoph
|
||||
return V.HOMOTOPIC # Homotoph
|
||||
|
||||
def draw(self):
|
||||
fig, ax = plt.gcf(), plt.gca()
|
||||
|
||||
@ -1,78 +1,80 @@
|
||||
from math import atan2
|
||||
from typing import List, Tuple, Union
|
||||
|
||||
from matplotlib import pyplot as plt
|
||||
from lib import variables as V
|
||||
|
||||
import numpy as np
|
||||
|
||||
class Trajectory(object):
|
||||
|
||||
@property
|
||||
def vertices(self):
|
||||
return self._vertices
|
||||
|
||||
@property
|
||||
def xy_vertices(self):
|
||||
return [(x, y) for _, y, x in self._vertices]
|
||||
|
||||
@property
|
||||
def endpoints(self):
|
||||
return self.start, self.dest
|
||||
|
||||
@property
|
||||
def start(self):
|
||||
return self._vertices[0]
|
||||
|
||||
@property
|
||||
def dest(self):
|
||||
return self._vertices[-1]
|
||||
|
||||
@property
|
||||
def xs(self):
|
||||
return [x[2] for x in self._vertices]
|
||||
|
||||
@property
|
||||
def ys(self):
|
||||
return [x[1] for x in self._vertices]
|
||||
|
||||
@property
|
||||
def as_paired_list(self):
|
||||
return list(zip(self._vertices[:-1], self._vertices[1:]))
|
||||
|
||||
@property
|
||||
def np_vertices(self):
|
||||
return [np.array(vertice) for vertice in self._vertices]
|
||||
|
||||
def __init__(self, vertices: Union[List[Tuple[int]], None] = None):
|
||||
assert any((isinstance(vertices, list), vertices is None))
|
||||
if vertices is not None:
|
||||
self._vertices = vertices
|
||||
pass
|
||||
|
||||
def is_equal_to(self, other_trajectory):
|
||||
# ToDo: do further equality Checks here
|
||||
return self._vertices == other_trajectory.vertices
|
||||
|
||||
def draw(self, highlights=True, label=None, **kwargs):
|
||||
if label is not None:
|
||||
kwargs.update(color='red' if label == V.HOMOTOPIC else 'green',
|
||||
label='Homotopic' if label == V.HOMOTOPIC else 'Alternative')
|
||||
if highlights:
|
||||
kwargs.update(marker='o')
|
||||
fig, ax = plt.gcf(), plt.gca()
|
||||
img = plt.plot(self.xs, self.ys, **kwargs)
|
||||
return dict(img=img, fig=fig, ax=ax)
|
||||
|
||||
def min_vertices(self, vertices):
|
||||
vertices, last_angle = [self.start], 0
|
||||
for (x1, y1), (x2, y2) in self.as_paired_list:
|
||||
current_angle = atan2(x1-x2, y1-y2)
|
||||
if current_angle != last_angle:
|
||||
vertices.append((x2, y2))
|
||||
last_angle = current_angle
|
||||
else:
|
||||
continue
|
||||
if vertices[-1] != self.dest:
|
||||
vertices.append(self.dest)
|
||||
return self.__class__(vertices=vertices)
|
||||
from math import atan2
|
||||
from typing import List, Tuple, Union
|
||||
|
||||
from matplotlib import pyplot as plt
|
||||
from lib import variables as V
|
||||
|
||||
import numpy as np
|
||||
|
||||
|
||||
class Trajectory(object):
|
||||
|
||||
@property
|
||||
def vertices(self):
|
||||
return self._vertices
|
||||
|
||||
@property
|
||||
def xy_vertices(self):
|
||||
return [(x, y) for _, y, x in self._vertices]
|
||||
|
||||
@property
|
||||
def endpoints(self):
|
||||
return self.start, self.dest
|
||||
|
||||
@property
|
||||
def start(self):
|
||||
return self._vertices[0]
|
||||
|
||||
@property
|
||||
def dest(self):
|
||||
return self._vertices[-1]
|
||||
|
||||
@property
|
||||
def xs(self):
|
||||
return [x[2] for x in self._vertices]
|
||||
|
||||
@property
|
||||
def ys(self):
|
||||
return [x[1] for x in self._vertices]
|
||||
|
||||
@property
|
||||
def as_paired_list(self):
|
||||
return list(zip(self._vertices[:-1], self._vertices[1:]))
|
||||
|
||||
@property
|
||||
def np_vertices(self):
|
||||
return [np.array(vertice) for vertice in self._vertices]
|
||||
|
||||
def __init__(self, vertices: Union[List[Tuple[int]], None] = None):
|
||||
assert any((isinstance(vertices, list), vertices is None))
|
||||
if vertices is not None:
|
||||
self._vertices = vertices
|
||||
pass
|
||||
|
||||
def is_equal_to(self, other_trajectory):
|
||||
# ToDo: do further equality Checks here
|
||||
return self._vertices == other_trajectory.vertices
|
||||
|
||||
def draw(self, highlights=True, label=None, **kwargs):
|
||||
if label is not None:
|
||||
kwargs.update(color='red' if label == V.HOMOTOPIC else 'green',
|
||||
label='Homotopic' if label == V.HOMOTOPIC else 'Alternative',
|
||||
lw=1)
|
||||
if highlights:
|
||||
kwargs.update(marker='o')
|
||||
fig, ax = plt.gcf(), plt.gca()
|
||||
img = plt.plot(self.xs, self.ys, **kwargs)
|
||||
return dict(img=img, fig=fig, ax=ax)
|
||||
|
||||
def min_vertices(self, vertices):
|
||||
vertices, last_angle = [self.start], 0
|
||||
for (x1, y1), (x2, y2) in self.as_paired_list:
|
||||
current_angle = atan2(x1-x2, y1-y2)
|
||||
if current_angle != last_angle:
|
||||
vertices.append((x2, y2))
|
||||
last_angle = current_angle
|
||||
else:
|
||||
continue
|
||||
if vertices[-1] != self.dest:
|
||||
vertices.append(self.dest)
|
||||
return self.__class__(vertices=vertices)
|
||||
|
||||
@ -5,6 +5,7 @@ from collections import defaultdict
|
||||
from configparser import ConfigParser
|
||||
from pathlib import Path
|
||||
|
||||
from lib.models.generators.cnn import CNNRouteGeneratorModel
|
||||
from lib.models.homotopy_classification.cnn_based import ConvHomDetector
|
||||
from lib.utils.model_io import ModelParameters
|
||||
|
||||
@ -27,7 +28,7 @@ class Config(ConfigParser):
|
||||
|
||||
@property
|
||||
def model_class(self):
|
||||
model_dict = dict(classifier_cnn=ConvHomDetector)
|
||||
model_dict = dict(classifier_cnn=ConvHomDetector, generator_cnn=CNNRouteGeneratorModel)
|
||||
try:
|
||||
return model_dict[self.get('model', 'type')]
|
||||
except KeyError as e:
|
||||
|
||||
@ -1,8 +1,8 @@
|
||||
from pathlib import Path
|
||||
|
||||
from pytorch_lightning.logging.base import LightningLoggerBase
|
||||
from pytorch_lightning.logging.neptune import NeptuneLogger
|
||||
from pytorch_lightning.logging.test_tube import TestTubeLogger
|
||||
from pytorch_lightning.loggers.base import LightningLoggerBase
|
||||
from pytorch_lightning.loggers.neptune import NeptuneLogger
|
||||
from pytorch_lightning.loggers.test_tube import TestTubeLogger
|
||||
|
||||
from lib.utils.config import Config
|
||||
|
||||
|
||||
@ -1,5 +1,7 @@
|
||||
from argparse import Namespace
|
||||
from pathlib import Path
|
||||
|
||||
import torch
|
||||
from natsort import natsorted
|
||||
from torch import nn
|
||||
|
||||
@ -35,30 +37,25 @@ class ModelParameters(Namespace):
|
||||
class SavedLightningModels(object):
|
||||
|
||||
@classmethod
|
||||
def load_checkpoint(cls, models_root_path, model, n=-1, tags_file_path=''):
|
||||
def load_checkpoint(cls, models_root_path, model=None, n=-1, tags_file_path=''):
|
||||
assert models_root_path.exists(), f'The path {models_root_path.absolute()} does not exist!'
|
||||
found_checkpoints = list(Path(models_root_path).rglob('*.ckpt'))
|
||||
|
||||
found_checkpoints = natsorted(found_checkpoints, key=lambda y: y.name)
|
||||
if model is None:
|
||||
model = torch.load(models_root_path / 'model_class.obj')
|
||||
assert model is not None
|
||||
|
||||
if not tags_file_path:
|
||||
tag_files = models_root_path.rglob('meta_tags.csv')
|
||||
tags_file_path = list(tag_files)[0]
|
||||
|
||||
return cls(weights=found_checkpoints[n], model=model, tags=tags_file_path)
|
||||
return cls(weights=found_checkpoints[n], model=model)
|
||||
|
||||
def __init__(self, **kwargs):
|
||||
self.weights: str = kwargs.get('weights', '')
|
||||
self.tags: str = kwargs.get('tags', '')
|
||||
|
||||
self.model = kwargs.get('model', None)
|
||||
assert self.model is not None
|
||||
|
||||
def restore(self):
|
||||
pretrained_model = self.model.load_from_metrics(
|
||||
weights_path=self.weights,
|
||||
tags_csv=self.tags
|
||||
)
|
||||
pretrained_model = self.model.load_from_checkpoint(self.weights)
|
||||
pretrained_model.eval()
|
||||
pretrained_model.freeze()
|
||||
return pretrained_model
|
||||
@ -1,5 +1,5 @@
|
||||
from pathlib import Path
|
||||
_ROOT = Path('..')
|
||||
|
||||
HOMOTOPIC = 0
|
||||
ALTERNATIVE = 1
|
||||
HOMOTOPIC = 1
|
||||
ALTERNATIVE = 0
|
||||
|
||||
Reference in New Issue
Block a user