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train running dataset fixed

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steffen
2020-03-05 20:50:07 +01:00
parent 1f25bf599b
commit 05033bed75
11 changed files with 41 additions and 49 deletions
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lib/models/homotopy_classification/cnn_based.py
+17 -17
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@@ -11,6 +11,7 @@ from datasets.trajectory_dataset import TrajData
from lib.evaluation.classification import ROCEvaluation
from lib.modules.utils import LightningBaseModule, Flatten
from lib.modules.blocks import ConvModule, ResidualModule
import matplotlib.pyplot as plt
class ConvHomDetector(LightningBaseModule):
@@ -36,10 +37,9 @@ class ConvHomDetector(LightningBaseModule):
predictions = torch.stack([x['prediction'] for x in outputs])
labels = torch.stack([x['label'] for x in outputs])
scores = evaluation(predictions.numpy(), labels.numpy())
self.logger.log_metrics()
scores = evaluation(predictions.numpy(), labels.numpy(), )
self.logger.log_metrics({key:value for key, value in zip(['roc_auc', 'tpr', 'fpr'], scores)})
self.logger.log_image(f'{self.name}', plt.gcf())
pass
def __init__(self, *params):
@@ -88,6 +88,19 @@ class ConvHomDetector(LightningBaseModule):
self.classifier = nn.Linear(self.hparams.model_param.classes * 10, 1) # self.hparams.model_param.classes)
self.out_activation = nn.Sigmoid() # nn.Softmax
def forward(self, x):
tensor = self.map_conv_0(x)
tensor = self.map_res_1(tensor)
tensor = self.map_conv_1(tensor)
tensor = self.map_res_2(tensor)
tensor = self.map_conv_2(tensor)
tensor = self.map_conv_3(tensor)
tensor = self.flatten(tensor)
tensor = self.linear(tensor)
tensor = self.classifier(tensor)
tensor = self.out_activation(tensor)
return tensor
# Dataloaders
# ================================================================================
# Train Dataloader
@@ -107,16 +120,3 @@ class ConvHomDetector(LightningBaseModule):
return DataLoader(dataset=self.dataset.val_dataset, shuffle=True,
batch_size=self.hparams.data_param.batchsize,
num_workers=self.hparams.data_param.worker)
def forward(self, x):
tensor = self.map_conv_0(x)
tensor = self.map_res_1(tensor)
tensor = self.map_conv_1(tensor)
tensor = self.map_res_2(tensor)
tensor = self.map_conv_2(tensor)
tensor = self.map_conv_3(tensor)
tensor = self.flatten(tensor)
tensor = self.linear(tensor)
tensor = self.classifier(tensor)
tensor = self.out_activation(tensor)
return tensor