plt.title('Dataset Images', fontsize=9)
epoch_list = [6, 9, 12, 18, 29]
for i in range(5):
fig.add_subplot(2, 3, 2 + i)
plt.imshow(model.list_thetas[epoch_list[i]])
plt.xticks([]), plt.yticks([])
plt.title('Transformed Images ' + str(epoch_list[i] + 1) + ' epochs',
fontsize=9)
fig.tight_layout()
plt.show(fig)
fig.savefig('stn_test.pdf', dpi=300)
visualize_stn(model)
temp = model.localization(tensor.cuda())
temp = temp.view(-1, model.n_units_stn)
# Regress the transformation matrices
theta = model.fc_loc(temp)
theta = theta.view(-1, 1, 2)
zoom = theta.narrow(2, 0, 1)
rotation = theta.narrow(2, 1, 1)
#print(theta)
# We only allow for zooming in the trafo
N_thetas = list(thetas.shape)[0]
identity_tensor = Variable(torch.tensor([[[1.0, 0.0, 0.0],
Images.append(temp)
#Load our pretrained CNN-Model
model, logger, _ = import_classifier()
# Construct dataset and dataloader with Images
evaluationset = Evalset(Images)
dataloader = DataLoader(evaluationset, batch_size=32, shuffle=True)
use_gpu = torch.cuda.is_available()
if not use_gpu:
print("Warning! No CUDA available. This is untested!")
if use_gpu:
model.cuda()
# Calculate the accuracy of the pretrained model on the data from the SVM
accuracy = evaluate(model, dataloader, use_gpu=use_gpu)
# Visualize the STN on sample images of SVM output
if use_gpu:
model.databatch = next(iter(dataloader))["tensor"].cuda()
else:
model.databatch = next(iter(dataloader))["tensor"].cuda()
visualize_stn(model, filename="./Classifier/Plots/stn_gtsdb_output.pdf")
print(
"For the GTSDB dataset (full) we obtain a total accuracy of the STN+CNN classifier of : "
+ str(accuracy))