mirror of
https://github.com/labmlai/annotated_deep_learning_paper_implementations.git
synced 2025-10-31 18:58:43 +08:00
86 lines
2.2 KiB
Python
86 lines
2.2 KiB
Python
#!/bin/python
|
|
|
|
import torch
|
|
import torchvision
|
|
import torchvision.transforms as transforms
|
|
|
|
import torch.nn as nn
|
|
import torch.nn.functional as F
|
|
|
|
import matplotlib.pyplot as plt
|
|
import numpy as np
|
|
|
|
from sklearn.model_selection import KFold
|
|
from torch.utils.data.sampler import SubsetRandomSampler
|
|
|
|
|
|
|
|
# Plot the loss of multiple runs together
|
|
def PlotLosses(losses, titles, save=None):
|
|
fig = plt.figure()
|
|
fig.set_size_inches(14, 22)
|
|
# Plot results on 3 subgraphs
|
|
# subplot integers:
|
|
# nrows
|
|
# ncols
|
|
# index
|
|
sublplot_str_start = "" + str(len(losses)) + "1"
|
|
|
|
for i in range(len(losses)):
|
|
subplot = sublplot_str_start + str(i+1)
|
|
loss = losses[i]
|
|
title = titles[i]
|
|
|
|
ax = plt.subplot(int(subplot))
|
|
ax.plot(range(len(loss)), loss)
|
|
ax.set_xlabel("Epoch")
|
|
ax.set_title(title)
|
|
ax.set_ylabel("Loss")
|
|
|
|
# Save Figure
|
|
if save:
|
|
plt.savefig(save)
|
|
else:
|
|
plt.show()
|
|
|
|
|
|
|
|
def ClassSpecificTestCifar10(net, testdata, device=None):
|
|
classes = ('plane', 'car', 'bird', 'cat','deer', 'dog', 'frog', 'horse', 'ship', 'truck')
|
|
class_correct = list(0. for i in range(10))
|
|
class_total = list(0. for i in range(10))
|
|
with torch.no_grad():
|
|
for data in testdata:
|
|
if device:
|
|
images, labels = data[0].to(device), data[1].to(device)
|
|
else:
|
|
images, labels = data
|
|
|
|
outputs = net(images)
|
|
_, predicted = torch.max(outputs, 1)
|
|
c = (predicted == labels).squeeze()
|
|
for i in range(4):
|
|
label = labels[i]
|
|
class_correct[label] += c[i].item()
|
|
class_total[label] += 1
|
|
|
|
# Print out
|
|
for i in range(10):
|
|
print('Accuracy of %5s : %2d %%' % (
|
|
classes[i], 100 * class_correct[i] / class_total[i]))
|
|
|
|
|
|
|
|
def GetActivation(name="relu"):
|
|
if name == "relu":
|
|
return nn.ReLU()
|
|
elif name == "leakyrelu":
|
|
return nn.LeakyReLU()
|
|
elif name == "Sigmoid":
|
|
return nn.Sigmoid()
|
|
elif name == "Tanh":
|
|
return nn.Tanh()
|
|
elif name == "Identity":
|
|
return nn.Identity()
|
|
else:
|
|
return nn.ReLU() |