steffen/hom_traj_gen
1
0
Fork 0
Code Issues Pull Requests Actions Packages Projects Releases Wiki Activity

Debugging

Browse Source
This commit is contained in:
Si11ium
2020-02-28 19:11:53 +01:00
parent 7b3f781d19
commit 44f6589259
18 changed files with 134 additions and 78 deletions
Download Patch File Download Diff File Expand all files Collapse all files
lib/models/homotopy_classification/cnn_based.py
+20 -9
View File
@@ -1,3 +1,6 @@
from functools import reduce
from operator import mul
import torch
from torch import nn
import torch.nn.functional as F
@@ -13,7 +16,7 @@ class ConvHomDetector(LightningBaseModule):
name = 'CNNHomotopyClassifier'
def configure_optimizers(self):
return Adam(self.parameters(), lr=self.lr)
return Adam(self.parameters(), lr=self.hparams.lr)
def validation_step(self, *args, **kwargs):
pass
@@ -32,29 +35,36 @@ class ConvHomDetector(LightningBaseModule):
def __init__(self, *params):
super(ConvHomDetector, self).__init__(*params)
# Dataset
self.dataset = TrajData(self.hparams.data_param.data_root)
self.dataset = TrajData(self.hparams.data_param.root)
# Additional Attributes
self.map_shape = self.dataset.map_shapes_max
# Model Paramters
self.in_shape = self.dataset.map_shapes_max
assert len(self.in_shape) == 3, f'Image or map shape has to have 3 dims, but had: {len(self.in_shape)}'
# NN Nodes
# ============================
# Convolutional Map Processing
#
self.map_res_1 = ResidualModule(self.in_shape, ConvModule, 3,
self.map_conv_0 = ConvModule(self.in_shape, conv_kernel=3, conv_stride=1,
conv_padding=0, conv_filters=self.hparams.model_param.filters[0])
self.map_res_1 = ResidualModule(self.map_conv_0.shape, ConvModule, 3,
**dict(conv_kernel=3, conv_stride=1,
conv_padding=0, conv_filters=self.hparams.model_param.filters[0]))
conv_padding=1, conv_filters=self.hparams.model_param.filters[0]))
self.map_conv_1 = ConvModule(self.map_res_1.shape, conv_kernel=5, conv_stride=1,
conv_padding=0, conv_filters=self.hparams.model_param.filters[0])
self.map_res_2 = ResidualModule(self.map_conv_1.shape, ConvModule, 3,
**dict(conv_kernel=3, conv_stride=1,
conv_padding=0, conv_filters=self.hparams.model_param.filters[0]))
conv_padding=1, conv_filters=self.hparams.model_param.filters[0]))
self.map_conv_2 = ConvModule(self.map_res_2.shape, conv_kernel=5, conv_stride=1,
conv_padding=0, conv_filters=self.hparams.model_param.filters[0])
self.map_res_3 = ResidualModule(self.map_conv_2.shape, ConvModule, 3,
**dict(conv_kernel=3, conv_stride=1,
conv_padding=0, conv_filters=self.hparams.model_param.filters[0]))
conv_padding=1, conv_filters=self.hparams.model_param.filters[0]))
self.map_conv_3 = ConvModule(self.map_res_3.shape, conv_kernel=5, conv_stride=1,
conv_padding=0, conv_filters=self.hparams.model_param.filters[0])
@@ -64,12 +74,13 @@ class ConvHomDetector(LightningBaseModule):
# Classifier
#
self.linear = nn.Linear(self.flatten.shape.item(), self.hparams.model_param.classes * 10)
self.classifier = nn.Linear(self.linear.shape, self.hparams.model_param.classes)
self.linear = nn.Linear(reduce(mul, self.flatten.shape), self.hparams.model_param.classes * 10)
self.classifier = nn.Linear(self.hparams.model_param.classes * 10, self.hparams.model_param.classes)
self.softmax = nn.Softmax()
def forward(self, x):
tensor = self.map_res_1(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)