Source code for draugr.torch_utilities.exporting.summary
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
__author__ = "Christian Heider Nielsen"
__doc__ = r"""
Created on 26-06-2021
"""
__all__ = ["to_latex_table"]
from torch import nn
[docs]def to_latex_table(m: nn.Module) -> str:
"""
:param m:
:return:
"""
return m.__repr__()
if __name__ == "__main__":
from torch.nn import functional
def aiasujd():
"""description
:return:
"""
class Model(nn.Module):
"""description"""
def __init__(self):
super().__init__()
self.conv0 = nn.Conv2d(1, 16, kernel_size=3, padding=5)
self.conv1 = nn.Conv2d(16, 32, kernel_size=3)
def forward(self, x):
"""
:param x:
:return:
"""
h = self.conv0(x)
h = self.conv1(h)
return h
model = Model()
print(to_latex_table(model))
def uiahsduhaw():
"""description
:return:
"""
class Model(nn.Module):
"""description"""
def __init__(self):
super().__init__()
self.conv1 = nn.Conv2d(1, 10, kernel_size=5)
self.conv2 = nn.Conv2d(10, 20, kernel_size=5)
self.conv2_drop = nn.Dropout2d()
self.fc1 = nn.Linear(320, 50)
self.fc2 = nn.Linear(50, 10)
def forward(self, x):
"""
:param x:
:return:
"""
x = functional.relu(functional.max_pool2d(self.conv1(x), 2))
x = functional.relu(
functional.max_pool2d(self.conv2_drop(self.conv2(x)), 2)
)
x = x.view(-1, 320)
x = functional.relu(self.fc1(x))
x = functional.dropout(x, training=self.training)
x = self.fc2(x)
return functional.log_softmax(x, dim=1)
model = Model()
print(to_latex_table(model))
uiahsduhaw()