106 lines
4.6 KiB
Python
106 lines
4.6 KiB
Python
from functools import reduce
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from operator import mul
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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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from torch.optim import Adam
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from torch.utils.data import DataLoader
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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.utils import LightningBaseModule, Flatten
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from lib.modules.blocks import ConvModule, ResidualModule
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import matplotlib.pyplot as plt
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class ConvHomDetector(LightningBaseModule):
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name = 'CNNHomotopyClassifier'
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def configure_optimizers(self):
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return Adam(self.parameters(), lr=self.hparams.lr)
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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 {'loss': loss, 'log': dict(loss=loss)}
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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, batch_nb=batch_nb)
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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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# 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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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.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 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.BCELoss()
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self.sigmoid = nn.Sigmoid()
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# NN Nodes
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# ============================
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# Convolutional Map Processing
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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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**dict(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=5, 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_2 = ResidualModule(self.map_conv_1.shape, ConvModule, 3,
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**dict(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_2 = ConvModule(self.map_res_2.shape, conv_kernel=5, 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_3 = ResidualModule(self.map_conv_2.shape, ConvModule, 3,
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**dict(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_3 = ConvModule(self.map_res_3.shape, conv_kernel=5, conv_stride=1,
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conv_padding=0, conv_filters=self.hparams.model_param.filters[0])
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self.flatten = Flatten(self.map_conv_3.shape)
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# ============================
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# Classifier
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#
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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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def forward(self, x):
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tensor = self.map_conv_0(x)
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tensor = self.map_res_1(tensor)
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tensor = self.map_conv_1(tensor)
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tensor = self.map_res_2(tensor)
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tensor = self.map_conv_2(tensor)
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tensor = self.map_conv_3(tensor)
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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.sigmoid(tensor)
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return tensor
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